For WizardKim-DPT2

sustainability

Article

Creating Sustainable Innovativeness through Big Data and Big Data Analytics Capability: From the Perspective of the Information Processing Theory

Michael Song, Haili Zhang * and Jinjin Heng School of Economics and Management, Xi’an Technological University, Xi’an 720021, China; [email protected] (M.S.); [email protected] (J.H.) * Correspondence: [email protected]

Received: 9 February 2020; Accepted: 2 March 2020; Published: 5 March 2020 ���������� �������

Abstract: Service innovativeness is a key sustainable competitive advantage that increases sustainability of enterprise development. Literature suggests that big data and big data analytics capability (BDAC) enhance sustainable performance. Yet, no studies have examined how big data and BDAC affect service innovativeness. To fill this research gap, based on the information processing theory (IPT), we examine how fits and misfits between big data and BDAC affect service innovativeness. To increase cross-national generalizability of the study results, we collected data from 1403 new service development (NSD) projects in the United States, China and Singapore. Dummy regression method was used to test the model. The results indicate that for all three countries, high big data and high BDAC has the greatest effect on sustainable innovativeness. In China, fits are always better than misfits for creating sustainable innovativeness. In the U.S., high big data is always better for increasing sustainable innovativeness than low big data is. In contrast, in Singapore, high BDAC is always better for enhancing sustainable innovativeness than low BDAC is. This study extends the IPT and enriches cross-national research of big data and BDAC. We conclude the article with suggestions of research limitations and future research directions.

Keywords: big data; big data analytics capability; innovations and sustainability; information processing theory; sustainable innovativeness

1. Introduction

The explosive growth of big data has brought opportunities and challenges for firms to rapidly develop and improve their competitiveness and sustainability of the enterprise development [1,2]. Sustainable innovation, particularly service innovation, is a key driver of sustainable competitive advantage [2]. Studies have demonstrated that big data is an invaluable resource in the development of service innovation [2–4], but also places great demands on the information processing capability of firms [5]. In the innovation literature, the information processing theory (IPT) [6] suggests that it is important to consider the fit between information processing demands and information processing capability [7,8]. IPT predicts that when there is a fit between a firm’s demands for information and its information processing capability, the firm will gain greater sustainable competitive advantage. In the era of big data, the big data processing and analysis requirements have increased significantly [4]. Firms need to use advanced technologies and tools, such as deep learning [5,9] and essential analytics capability [10,11], to identify market trends and evolution patterns contained in big data. A lack of big data analytics capability (BDAC) can leave firms with unharnessed big data, resulting in increased data storage costs and greater difficulty in converting data into useful, timely information [12,13].

Big data refers to the enormous volume of rapidly and incessantly compiled data from an immeasurable variety of market, consumer, social, and other activities. The increasingly digital modern

Sustainability 2020, 12, 1984; doi:10.3390/su12051984 www.mdpi.com/journal/sustainability

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era has seen the exponential growth of big data as an important information resource [14]. However, extracting value from big data requires analysis and utilization capabilities that can translate big data into usable information and create sustainable competitive advantages in innovation [12,15]. Thus, BDAC has become the focus of many recent researches [2,5]. With BDAC, managers can gain new perspectives and technologies to improve existing theoretical knowledge, enhance decision-making capability, and promote innovation [5,10,16]. Many scholars have begun realizing the importance of fit between big data and BDAC. Isik [4] pointed out that firms can align their big data processing demands with their BDAC to effectively use big data to advance their products or competition mode. Wang and Hajli [17] using the medical industry as their research setting, constructed a theoretical model of how BDAC implements the integration, processing, and visualization of big data to achieve sustainable growth in operational, organizational, management, and strategic areas. Hao et al. [2] examined the positive moderating effect of BDAC on the relationship between big data and sustainable innovation performance. Nevertheless, few researchers have focused on the measurement and empirical testing of the fit between big data and BDAC [4] and there has been little in-depth discussion on the impact of big data/BDAC fit on service innovation.

Innovativeness is a key indicator of service innovation success, which can help firms attract new customers and obtain sustainable competitive advantages [18]. As service innovation is a process of identifying and solving problems through the integration of resources and capabilities, the degree of sustainable innovativeness is largely affected by the type and level of resources and capabilities a firm has. The rapid development of big data has provided new development opportunities for firms [11] by helping them quickly understand changing market demand, identify and create new business opportunities, and achieve successful innovation [3,12,19,20]. BDAC encompasses a firm’s ability to obtain a new strategic and operational perspective through the combination, integration, and deployment of specific big data resources [10]. The effect of the fit between big data and BDAC on sustainable innovativeness is thus very important in discussing the process of service innovation. To facilitate our study of these issues, we developed three research questions:

RQ1: Do fits (the fit between high big data and high BDAC and the fit between low big data and low BDAC) increase sustainable innovativeness more than misfits (the misfit between high big data and low BDAC and the misfit between low big data and high BDAC) do?

RQ2: Does high-high fit (the fit between high big data and high BDAC) increase sustainable innovativeness more than low-low fit (the fit between low big data and low BDAC) does?

RQ3: Does low-high misfit (the misfit between low big data and high BDAC) increase sustainable innovativeness more than high-low misfit (the misfit between high big data and low BDAC) does? Or is the reverse true?

To answer these three questions, we draw on the IPT to develop a theoretical model of the effects of fits and misfits between big data and BDAC on sustainable innovativeness. We consider two types of alignments (fits): the fit between high big data and high BDAC (high-high fit) and the fit between low big data and low BDAC (low-low fit). We also evaluate two types of misfits: the misfit between high big data and low BDAC (high-low misfit) and the misfit between low big data and high BDAC (low-high misfit) (see Figure 1). Therefore, we examine four possible scenarios: high-low misfit, high-high fit, low-low fit, and low-high misfit.

We empirically test the theoretical model and conduct a three-country comparative study to assess its cross-national applicability by collecting data from 477 new service development (NSD) projects in the United States, 632 NSD projects in China, and 294 NSD projects in Singapore. We use dummy regression method to analyze the data.

Our study results suggest: (1) For the United States, China, and Singapore, high-high fit has the greatest impact on sustainable innovativeness. (2) For China, sustainable innovativeness is higher when big data and BDAC align (either high-high fit or low-low fit). Managers of NSD projects in China should increase big data and BDAC simultaneously to ensure that they are always in balance. (3) For the United States and Singapore, when either big data or BDAC is at a low level, fit is not always better

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than misfit. The U.S. NSD projects should strive to improve the level of big data, while Singapore NSD projects should focus on improving BDAC to achieve greater sustainable innovativeness.

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Figure 1. Four scenarios of the fits and misfits between big data and BDAC.

Our study results suggest: (1) For the United States, China, and Singapore, high-high fit has the

greatest impact on sustainable innovativeness. (2) For China, sustainable innovativeness is higher

when big data and BDAC align (either high-high fit or low-low fit). Managers of NSD projects in

China should increase big data and BDAC simultaneously to ensure that they are always in balance.

(3) For the United States and Singapore, when either big data or BDAC is at a low level, fit is not

always better than misfit. The U.S. NSD projects should strive to improve the level of big data, while

Singapore NSD projects should focus on improving BDAC to achieve greater sustainable

innovativeness.

We make three theoretical contributions to the literature on sustainability of big data application

and sustainable development theory: (1) We enrich research on the IPT by extending its application

to the context of big data and BDAC, defining information processing demands as big data and

information processing capability as BDAC. (2) We expand the empirical research on big data and

BDAC by exploring the impact of fits and misfits between big data and BDAC on sustainable

innovativeness. (3) We contribute to cross-national comparative research on sustainability of big data

and BDAC. Through empirical comparative analysis of data from the United States, China, and

Singapore, we find different impacts of fits and misfits between big data and BDAC on sustainable

innovativeness. The study results not only promote the application of the IPT to study of

sustainability of big data but also provide specific management suggestions for firms in different

countries to improve sustainable innovativeness through appropriate investment strategies for big

data and BDAC.

2. Theoretical Background and Framework

2.1. Information Processing Theory (IPT)

The IPT regards a firm as an open social system that constantly exchanges information with the

external environment and utilizes that information in business activities [7,8]. Galbraith [8] described

the IPT as having three core concepts: information processing demand, information processing

capability, and the fit between this demand and capability. On the one hand, firms can reduce

information processing demand by increasing slack resources, but this strategy increases costs for

firms. On the other hand, firms can increase the availability of usable information to support decision-

making by improving information processing capability [7]. When the information processing

capability (collection, transformation, storage, and exchange of information) fit with the firm’s

Figure 1. Four scenarios of the fits and misfits between big data and BDAC.

We make three theoretical contributions to the literature on sustainability of big data application and sustainable development theory: (1) We enrich research on the IPT by extending its application to the context of big data and BDAC, defining information processing demands as big data and information processing capability as BDAC. (2) We expand the empirical research on big data and BDAC by exploring the impact of fits and misfits between big data and BDAC on sustainable innovativeness. (3) We contribute to cross-national comparative research on sustainability of big data and BDAC. Through empirical comparative analysis of data from the United States, China, and Singapore, we find different impacts of fits and misfits between big data and BDAC on sustainable innovativeness. The study results not only promote the application of the IPT to study of sustainability of big data but also provide specific management suggestions for firms in different countries to improve sustainable innovativeness through appropriate investment strategies for big data and BDAC.

2. Theoretical Background and Framework

2.1. Information Processing Theory (IPT)

The IPT regards a firm as an open social system that constantly exchanges information with the external environment and utilizes that information in business activities [7,8]. Galbraith [8] described the IPT as having three core concepts: information processing demand, information processing capability, and the fit between this demand and capability. On the one hand, firms can reduce information processing demand by increasing slack resources, but this strategy increases costs for firms. On the other hand, firms can increase the availability of usable information to support decision- making by improving information processing capability [7]. When the information processing capability (collection, transformation, storage, and exchange of information) fit with the firm’s demand for information processing, the firm can obtain sustainable competitive advantage. Since the IPT was first proposed, many scholars have conducted research from the perspective of information processing to explore the impact of fit between the demand for information and information processing capability on firm performance. Most of the early research focused on strategy, structural design of the organization or team, and supply chain management [21,22]. More recently, scholars have applied the IPT to

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multiple research fields, including operations management, new product development, international management, and knowledge management, which has further expanded the applicability of the IPT [6,23,24]. However, most studies have applied the IPT to explore the fit between the traditional needs for information and information processing capabilities [21,24], with few studies considering the IPT in the context of big data and BDAC.

With the pervasiveness of big data in operations and organizational development, there is also very high demand for specialized information processing capabilities. In the face of the rapidly changing market environment, the value of big data is fleeting, and firms need timely and effective analysis to mine the information resources in the big data [19]. There is no inevitable relationship between the acquisition of information and the improvement of firm performance, only effective use of the information can lead to improved profitability. The IPT considers the effective allocation and coordination of a firm’s resources and capabilities such as how the adaptation and promotion of different elements within a firm can effectively advance innovation activities [25]. BDAC provides new information processing methods and technologies that enable firms to translate big data into new information that can be used in different ways and promote sustainable service innovation. Although some scholars have emphasized the importance of fit between big data processing demands and big data processing capability based on the IPT [4], there is a lack of in-depth empirical testing and consideration of the impact of fit in the field of service innovation. Therefore, in this study, we apply the IPT by treating big data as the information processing demand of firms and BDAC as the important information processing capability of firms, and discuss the impact of fit between big data and BDAC on sustainable innovativeness in the process of service innovation.

2.2. Big Data

There is still no consensus on a definition of big data because of the wide range and rich meaning it comprises [2]. Simply, big data refers to the large-scale data sets produced by new technology forms. A deeper characterization of big data considers the sources and composition of these data sets [1,3,10,14,19]. McAfee and Brynjolfsson [1] proposed that big data can be characterized according to the 3V’s of volume, variety, and velocity. Other scholars have added two additional V’s of veracity and value [14,26]. In this study, we define big data as large, complex, and real-time data streams that require complex management, analysis, and processing techniques to extract valuable information [10]. However, the real value of big data lies not only in its large quantity but also, more importantly, in its differences from traditional data. Big data has created a new and unique data generation and use environment, which is not possible with a small amount of data [3,27].

Since the rise of the Internet and the digital economy, big data has become the most important technological change in business and academia, bringing considerable benefits to business, scientific research, public management, and other industries [1,2]. Many scholars have proposed that big data is one of the most important resources for firms to achieve sustainable development [26,28]. For example, big data can use production processes and supplier information to increase productivity, reduce cost losses, and achieve sustainable corporate development [5]. Big data pervades modern life, transforming thinking and decision-making methods and becoming an important strategic resource for firms to achieve sustainable development [28]. Furthermore, as technology advances, the costs of big data storage and BDAC technologies gradually decline, allowing more firms to realize the importance of using and quantifying big data to enhance their competitive advantage [29].

Scholars have discussed the value of big data for firms from different perspectives. First, big data is helpful for firms to understand market and demand information. It also provides new perspectives for problem solving and enables firms to recombine existing resources and elements to efficiently enhance firm innovation [30]. Big data also provides a database of timely information to guide innovation activities, helping firms accurately predict market demand changes in a rapidly changing environment, enabling quick response to market demand, and suggesting new development directions and goals [3,19]. Second, the information provided by big data can enable managers to

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make scientifically supported, high-quality decisions based on big data analytics rather than intuition and experience [11,19]. The operational management perspective and new management knowledge provided by big data can help managers make more efficient decisions [11]. Third, big data can help managers better understand the information related to the market environment, customer demand, and product characteristics and thereby improve the efficiency of operation processes [20,31]. The basic information source provided by big data for managers can improve the efficiency of internal information sharing and the operational outcome of firms [20]. In supply chain management, big data can also help firms respond to the changing environment more quickly, reduce management costs, and improve the efficiency of firm operation planning [31]. Finally, big data can help firms identify opportunities and develop new business models to determine effective actions and strategies for successful innovation [20,32].

2.3. Big Data Analytics Capability (BDAC)

With the growth of big data, firms have access to huge and diverse databases. Scholars introduced the term data science to refer to the endeavor of effectively analyzing and visualizing the trends and models contained in big data [5]. BDAC describes the tools and means employed to generate information and knowledge from big data [14,26]. At present, most scholars define BDAC from two perspectives: the resource-based view perspective and big data utilization process perspective. From the perspective of the resource-based view, BDAC is an information technology capability that provides perspective to firms by using data management, infrastructure, and human resources to gain competitive advantage in the big data environment [14,33]. From the perspective of using big data to create business value and scientific decision-making in business processes, BDAC describes the ability of firms to analyze big data in planning, production, and transmission, thus enabling firms to acquire, store, process, and analyze a large amount of data in various forms and extract valuable, timely information [17,26]. In this study, we follow the research of [10] and define BDAC as the capability of firms to combine, integrate, and deploy specific big data resources.

With the increasing importance of big data to firms, many scholars and managers have been exploring how to make better use of BDAC to gain sustainable competitive advantage [26]. Research on BDAC can be divided into the following four aspects: First, BDAC can significantly improve firm performance [10,11,14,33]. In the context of big data, effective combination of organizational structure, infrastructure, human capital, and other resources can help firms to obtain high-level competitive advantage [14]. Second, BDAC can significantly affect the organizational agility of firms and improve their capability to cope with environmental changes. BDAC can help managers accurately grasp the rapidly changing market environment and propose corresponding business plans and solutions to gain sustainable competitive advantage [14,15,34]. Third, BDAC promotes the improvement of innovativeness of firms [16]. Rialti et al. [35] pointed out that BDAC can help firms to reintegrate existing resources and routines to discover and take advantage of new opportunities and develop innovative solutions to positively influence the innovation of firms. Fourth, BDAC can change business processes and management modes, promote effective allocation and control of resources, and realize business model innovation [17,30].

2.4. Sustainable Innovativeness

Innovativeness is an important measure of successful new product development, which is usually described from the perspective of firms or customers [36]. As new service products are the main achievements of NSD of firms, we draw from the results of previous research on product innovativeness to define sustainable innovativeness as the degree of novelty of new service products compared with existing service products and markets of firms [37,38].

NSD has become a key activity for firms to obtain sustainable development in a competitive market environment. Sustainable innovativeness is the key factor of service innovation and one of the important sources of sustainable competitive advantage. Therefore, the influencing factors of

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sustainable innovativeness are of great interest to scholars and managers [39]. From the resource-based view, relevant resources and information will significantly improve product innovativeness. The market information owned by firms can help them effectively evaluate customer demand and market trends and integrate them into the production of new service products, so as to develop new and distinctive products [40]. Cillo et al. [41] pointed out that different analysis methods of market information will have different effects on product innovativeness while Song et al. [38] found that the marketing resources and research and development (R&D) resources of new ventures have no significant impact on product innovativeness. Retrospective analysis of market information will negatively affect product innovativeness, and prospective analysis of market information will positively affect product innovativeness [41].

Previous research has considered the influencing factors of sustainable innovativeness from the perspective of the firm’s capability to process resources and information, proposing that the firm’s capability will affect sustainable innovativeness [18,39]. However, the relationship between a firm’s knowledge integration mechanism and product innovativeness may not be a simple linear one; instead some scholars have found that there is an inverted U-shaped relationship between them. Overemphasis on knowledge synthesis, configuration, and applicable formal processes and structures among team members can hinder the improvement of product innovativeness [42].

Many studies have found that information and resources are the key influencing factors of product innovativeness. Extending these findings to the context of big data, the key to extracting value from big data lies in the mining and analysis of big data by BDAC [10,19] and the key to the effective implementation of BDAC lies in having sufficient big data resources [13]. Nevertheless, there has been little in-depth examination of the fit between big data and BDAC, in particular with regard to the impact mechanism of such fit on sustainable innovativeness. As a result, firms lack research-based guidance on how to effectively maximize the value of their existing big data resources and BDAC in service innovation. Therefore, pursuing research on the impact of fit between big data and BDAC on sustainable innovativeness has important theoretical and practical significance.

3. Research Hypotheses

When there is fit between big data and BDAC, firms can fully mine their big data resources for valuable information to build their knowledge base, improve the scientific basis and quality of decision-making, and promote sustainable innovativeness. Based on the IPT, the fit between the demand for information and information processing capability will result in more effective output [7]. Therefore, attaining fit between big data and BDAC can help NSD projects achieve successful innovation activities more effectively and produce totally new service products that are novel and accepted by customers, thus building sustainable development.

In the case of high-high fit, NSD project teams have access to a large amount of big data and the high level of BDAC allows them to effectively analyze these data resources to obtain market and customer demand information, clarify the development trend of service innovation [1,14,33], and ultimately design novel service products [1].

In the case of low-low fit, the low level of big data leaves project teams unable to fully grasp the changes in market demand [3] but also reduces the cost of information storage and the pressure of information overload. At the same time, project teams can use the same level of BDAC to deeply mine the data they have to acquire information that helps them identify service innovation market segments, find the invention approaches to service innovation, and develop service products that can have an important impact on the existing industry [16].

When there are misfits between big data and BDAC, project teams cannot effectively balance big data resources and BDAC, which places project developers in the dilemma of a data storm that affects their cognitive ability and decision-making quality [13]. Big data/BDAC misfit also increases the cost of data storage, resulting in resource waste [7,12]. In the case of high-low misfit, although project teams have a large amount of data, they lack BDAC and thus can merely interpret the data. In this situation,

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the task of converting so much data into timely, usable information is difficult and overwhelming [14], which can affect the accuracy of analysis of market trends and easily lead to blind development and, ultimately, failure of service innovation [16].

In the case of low-high misfit, project managers have enough data mining technology to process, analyze, and visualize big data [34], but they have access to few data resources and thus lower requirements for BDAC. Such an imbalance will not only suppress sustainable innovativeness of service products but also cause redundancy and waste of resources [7], hindering the innovation activities of project teams. Thus, it is apparent that the roles of big data and BDAC are restricted by each other. We therefore hypothesize:

Hypothesis 1 (H1). Fits (the fit between high big data and high BDAC and the fit between low big data and low BDAC) improve sustainable innovativeness more than misfits (the misfit between high big data and low BDAC and the misfit between low big data and high BDAC) do.

Although fit between big data and BDAC may be more beneficial than misfit, there are differences in the impact on sustainable innovativeness between high-high fit and low-low fit. High levels of both big data and BDAC enable project managers to use advanced analysis technologies to accurately discover and classify important information from a massive variety of big data to identify new needs of users or determine new market opportunities [33]. With such high-quality, timely information [10], project managers can refine their goals for service innovation and achieve the leading position of service product innovation in their industries.

In the case of low-low fit, because the project managers have a low stock of big data, they lack timely and relevant information sources. Due to the low capability of data mining and analysis, project teams are unable to fully grasp insights into market developments and service innovation and thus suffer from a lack of service innovation inspiration and sustainable innovativeness [1,12]. We therefore hypothesize:

Hypothesis 2 (H2). High-high fit (the fit between high big data and high BDAC) improves sustainable innovativeness more than low-low fit (the fit between low big data and low BDAC) does.

When there are misfits between big data and BDAC, low-high misfit can improve sustainable innovativeness more than high-low misfit can. In the case of low-high misfit, although project managers do not have enough big data, the high level of BDAC can help them accurately find and sort out relevant information from existing data, design service innovation process and operation measures, recombine existing resources according to market demand, update product technology and functions [10,30], and otherwise maximize the value of their limited big data resources. Even with a lower level of big data, firms with advanced BDAC can carry out prospective analysis on existing market information, predict market environment and development directions, clarify the direction of service innovation, and effectively improve sustainable innovativeness [41].

In contrast, in the case of high-low misfit, although project managers have a large amount of big data, they lack the capability to extract information on market demand trends and predictions about consumption behavior, so they cannot effectively integrate and analyze the big data they have, resulting in the lack of innovation spirit and the inability to accurately assess the direction of service innovation [16]. Compared with low-high misfit, high-low misfit not only causes waste of resources and increases the cost burden of project managers [12] but creates the dilemma of dealing with too much information [16]. At the same time, big data itself will not be the source of differentiation advantage for project teams [10] because compared with the big data resources owned by project teams, BDAC is the key advantage to effectively utilizing market and customer information [14]. We therefore hypothesize:

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Hypothesis 3 (H3). Low-high misfit (the misfit between low big data and high BDAC) improves sustainable innovativeness more than high-low misfit (the misfit between high big data and low BDAC) does.

4. Methodology and Data Sources

The data for the U.S. and China come from the research project conducted by Hao et al. [2]. The details of the research methodology and data are described in Hao et al. [2]. For completeness, we rephrase their descriptions here. The research design includes three empirical studies. We empirically test the theoretical model of the impact of fit between big data and BDAC on sustainable innovativeness using data from 477 U.S. NSD projects. We then test the generalizability of the model and compare the similarities and differences between the United States and two other countries by conducting two empirical studies to collect data from 632 NSD projects in China and 294 NSD projects in Singapore, respectively [2]. We report these three empirical studies separately below.

As reported in Hao et al. [2], to develop and refine the study measures, the research team followed the cross-national research methodology recommended by [43] to conduct in-depth interviews with NSD teams in the United States, China, and Singapore. The final study measures and sources of the measures are reported in the Appendix A.

4.1. Empirical Study 1: The United States

4.1.1. Measurement

Different from the measures used by Hao et al. [2], the measurement scale for big data in this article includes five items that are adopted from Gupta and George [10]: (1) “We have access to very large, unstructured, or fast-moving data for analysis”; (2) “We integrate data from multiple internal sources into a data warehouse or mart for easy access”; (3) “We integrate external data with internal data to facilitate high-value analysis of our business environment”; (4) “Our big data analytics projects are adequately funded”; and (5) “Our big data analytics projects are given enough time to achieve their objectives”. Project team leaders rated their agreement or disagreement with these descriptions on a scale ranging from 0 (strongly disagree) to 10 (strongly agree). Based on factor analyses, item 5 was deleted.

The measurement items for BDAC are adopted from Hao et al. [2]. The specific measures are reproduced in the Appendix A. A sample measure is “We have advanced tools (analytics and algorithms) to extract values of the big data”. Project team leaders rated their team’s capabilities on a scale ranging from 0 (no capability) to 10 (very high level of capability).

We adapted the five measurement items for sustainable innovativeness from Song and Parry [37]. As presented in Appendix A, minor modifications were made to the measures based on the in-depth interviews and pretests. The final measures are: (1) “The products and services incorporate innovative technologies that have never been used in the industry before”; (2) “The products and services caused significant changes in the whole industry”; (3) “The products and services are among the first of their kind to be introduced into the market”; (4) “The products and services are highly innovative—totally new to the market”; (5) “The products and services are perceived as being the most innovative in the industry”. Project team leaders rated their team’s sustainable innovativeness in these areas on a scale ranging from 0 (strongly disagree) to 10 (strongly agree).

4.1.2. Data

As reported in Hao et al. [2], we chose 1000 U.S. firms from the Dun and Bradstreet database. We used the same data collection procedure as reported in Hao et al. [2]. We sent, via express mail and e-mail, a package/e-mail that included a personalized letter, the study survey, a pre-signed non-disclosure agreement (NDA), and (for the mail package) a prepaid return envelope. We asked each participating firm to select four different NSD projects for providing data: a “successful” NSD

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project, a “failure” NSD project, a typical NSD project, and a recent NSD project. We sent a follow-up letter/e-mail a week later. In addition, we sent second and third follow-up letters/e-mails and made phone calls to nonresponding firms to improve the response rate.

For this study, we selected all 477 NSD projects collected using the above procedure. The final data included 46 projects in hotel, traveling, and tourism services; 146 projects in banking, insurances, securities, financial investments, and related activities; 99 projects in information and semiconductor; 95 projects in Internet-related services; and 91 projects in health care services [2].

4.1.3. Analysis and Results

Table 1 shows the mean, standard deviation, correlations, and construct reliability for the U.S. sample. The values on the diagonal are Cronbach’s alpha coefficients for each variable, which are all above the threshold value of 0.7, indicating that the study measures we employed have high reliability.

Table 1. The U.S. sample: descriptive statistics and correlation coefficient matrix (N = 477).

Innovativeness Big Data BDAC

Innovativeness 0.855 Big Data 0.587 *** 0.918 BDAC 0.433 *** 0.419 *** 0.803 Mean 5.717 5.315 6.044 S.D. 2.138 2.749 2.056

Note: *** p < 0.01 (two-tailed test). BDAC = Big data analytics capability. The Cronbach’s alpha for each variable is on the diagonal; the intercorrelations among the variables are on the off diagonal.

We also conducted exploratory factor analysis of the scale items. Table 2 shows the factor loadings for the U.S. sample. For each measure to be included in the final analyses, it must load to the correct factor with loading greater than 0.5 and must have no cross-loadings with loading greater than 0.4 in all three empirical studies. Item 5 of big data and item 3 of BDAC did not meet the requirements and were deleted from the final analyses. The factor loadings of the remaining measures for the U.S. sample are presented in Table 2. All final measures loaded correctly into the corresponding factor.

Table 2. The U.S. sample: factor loadings from exploratory factor analysis (N = 477).

Measure Items Innovativeness Big Data BDAC

INNO 1 0.833 0.187 0.190 INNO 4 0.772 0.229 0.086 INNO 2 0.723 0.260 0.125 INNO 3 0.722 0.178 0.238 INNO 5 0.671 0.272 0.181

Big Data 2 0.225 0.870 0.146 Big Data 4 0.268 0.868 0.149 Big Data 1 0.329 0.813 0.121 Big Data 3 0.262 0.784 0.313 BDAC 2 0.114 0.115 0.821 BDAC 1 0.135 0.162 0.759 BDAC 4 0.172 0.149 0.752 BDAC 5 0.204 0.137 0.720

Note: BDAC = Big data analytics capability. Bold numbers indicate items that load highly for the corresponding factor.

Before regression analysis, we used the sample mean value of big data (5.315) and the sample mean value of BDAC (6.044) to divide the 477 NSD projects into four scenarios: two fits (high-high fit and low-low fit) and two misfits (high-low misfit and low-high misfit), as shown in Figure 2.

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Figure 2. The U.S. sample: fits and misfits between big data and BDAC (N = 477).

We used ordinary least squares (OLS) dummy regression to test the effect of two fits and two

misfits on sustainable innovativeness. Proc Reg of SAS 9.4 was used to provide estimates. As four

independent variables (two fits and two misfits) represent four dummy variables, option “noint” was

included in the model statement of the “Proc Reg” to exclude the intercept term in the “Proc Reg”

estimations. The estimated coefficients were the effects of fits and misfits on sustainable

innovativeness under four scenarios. To test the three hypotheses, we used the “TEST” statement of

the “Proc Reg Model” to examine whether or not the coefficients estimated in the model were

significantly different from each other as hypothesized. We tested for possible differences of all six

possible pairs and the results were all significant (p < 0.01).

Table 3 displays the final estimates. The results in Table 3 indicate that both fits and misfits have

significant positive impact on the sustainable innovativeness of NSD projects in the United States.

The results from six paired-wise tests indicate that these effects differ from each other (p < 0.01). To

examine whether or not each hypothesis is supported, we use the standardized estimates and the

results of the paired-wise tests.

As predicted by H1, the effect of high-high fit on sustainable innovativeness (b = 0.701; p < 0.01)

is the greatest. However, counter to H1, the positive effect of high-low misfit on sustainable

innovativeness (b = 0.400; p < 0.01) is greater than that of low-low fit (b = 0.384; p < 0.01). Thus, H1 is

only partially supported by the data.

The results suggest that the effect of high-high fit on sustainable innovativeness (b = 0.701; p <

0.01) is significantly higher than that of low-low fit (b = 0.384; p < 0.01). Thus, as predicted by H2,

high-high fit increases sustainable innovativeness more than low-low fit does (p < 0.01). The data

provide supports for H2.

H3 predicts that low-high misfit improves sustainable innovativeness more than high-low misfit

does. Counter to H3, the results in Table 3 indicate that the effect of low-high misfit on sustainable

innovativeness (b = 0.340; p < 0.01) is significantly lower, not higher (as hypothesized by H3), than

that of high-low misfit (b = 0.400; p < 0.01). Thus, H3 is not supported by the U.S. data.

Table 3. The U.S. sample: results of dummy regression analysis (N = 477).

Dependent Variable: Sustainable Innovativeness

Parameter Estimate (β) Standard Error (S.E.) Standardized Estimate (b)

Figure 2. The U.S. sample: fits and misfits between big data and BDAC (N = 477).

We used ordinary least squares (OLS) dummy regression to test the effect of two fits and two misfits on sustainable innovativeness. Proc Reg of SAS 9.4 was used to provide estimates. As four independent variables (two fits and two misfits) represent four dummy variables, option “noint” was included in the model statement of the “Proc Reg” to exclude the intercept term in the “Proc Reg” estimations. The estimated coefficients were the effects of fits and misfits on sustainable innovativeness under four scenarios. To test the three hypotheses, we used the “TEST” statement of the “Proc Reg Model” to examine whether or not the coefficients estimated in the model were significantly different from each other as hypothesized. We tested for possible differences of all six possible pairs and the results were all significant (p < 0.01).

Table 3 displays the final estimates. The results in Table 3 indicate that both fits and misfits have significant positive impact on the sustainable innovativeness of NSD projects in the United States. The results from six paired-wise tests indicate that these effects differ from each other (p < 0.01). To examine whether or not each hypothesis is supported, we use the standardized estimates and the results of the paired-wise tests.

Table 3. The U.S. sample: results of dummy regression analysis (N = 477).

Dependent Variable: Sustainable Innovativeness

Parameter Estimate (β) Standard Error (S.E.) Standardized Estimate (b)

High-Low Misfit 6.118 *** 0.206 0.400 High-High Fit 6.963 *** 0.134 0.701 Low-Low Fit 4.179 *** 0.146 0.384

Low-High Misfit 5.380 *** 0.213 0.340 Model F-value 1263.050 ***

R-square 0.914 Adjusted R-square 0.914

Note: *** p < 0.01 (two-tailed test). High-Low Misfit = the misfit between high big data and low BDAC; High-High Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC; Low-High Misfit = the misfit between low big data and high BDAC. The six paired-wise tests indicate that all pairs are significantly different from each other at p < 0.01 (one-tailed test).

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As predicted by H1, the effect of high-high fit on sustainable innovativeness (b = 0.701; p < 0.01) is the greatest. However, counter to H1, the positive effect of high-low misfit on sustainable innovativeness (b = 0.400; p < 0.01) is greater than that of low-low fit (b = 0.384; p < 0.01). Thus, H1 is only partially supported by the data.

The results suggest that the effect of high-high fit on sustainable innovativeness (b = 0.701; p < 0.01) is significantly higher than that of low-low fit (b = 0.384; p < 0.01). Thus, as predicted by H2, high-high fit increases sustainable innovativeness more than low-low fit does (p < 0.01). The data provide supports for H2.

H3 predicts that low-high misfit improves sustainable innovativeness more than high-low misfit does. Counter to H3, the results in Table 3 indicate that the effect of low-high misfit on sustainable innovativeness (b = 0.340; p < 0.01) is significantly lower, not higher (as hypothesized by H3), than that of high-low misfit (b = 0.400; p < 0.01). Thus, H3 is not supported by the U.S. data.

4.2. Empirical Study 2: China

4.2.1. Measurement Validation in Empirical Study 2

As reported in Hao et al. [2], all measures were translated into Chinese using the double-translation method [2] using four translators. Minor differences were discussed and resolved. Two pretests were performed to evaluate the appropriateness of formats and accuracies using the participants of the earlier interviewees. After pretests, minor modifications were made to formatting and wordings to create the final survey [2].

4.2.2. Data

As reported in Hao et al. [2], to ensure comparability with the sample of the United States, 524 companies listed in the Small and Medium Enterprise and Growth Enterprise Market Boards of the Shenzhen Stock Exchange in China were chosen as initial sampling frame. These companies were further reduced to 482 companies to match with the sample from the United States after deleting all companies with missing data. The details of the data collection were reported in [2]. This study used all 632 NSD projects from the dataset. The final data included 40 from hotel, traveling, and tourism services; 217 from banking, insurances, securities, financial investments, and related activities; 120 from information and semiconductor; 91 from Internet-related services; and 164 from health care services [2].

4.2.3. Analysis and Results

Table 4 shows the descriptive statistics and correlation coefficient matrix of each variable for the Chinese sample. The values on the diagonal are the Cronbach’s alpha coefficients of each variable, all of which are greater than 0.7, indicating high reliability of our study measures. To ensure the cross-national comparability of the data between China and the United States, we retained the same measurement items for factor analysis as in the U.S. analysis. Table 5 shows the factor loadings of each variable, which are all greater than 0.6, indicating high structural validity of the measurement items.

Table 4. The Chinese sample: descriptive statistics and correlation coefficient matrix (N = 632).

Innovativeness Big Data BDAC

Innovativeness 0.869 Big Data 0.588 *** 0.894 BDAC 0.389 *** 0.506 *** 0.767 Mean 5.297 4.571 6.254 S.D. 2.192 2.585 2.085

Note: *** p < 0.01 (two-tailed test). BDAC = Big data analytics capability. The Cronbach’s alpha for each scale is on the diagonal in italics; the intercorrelations among the variables are on the off diagonal.

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Table 5. The Chinese sample: factor loadings from exploratory factor analysis (N = 632).

Measure Items Innovativeness Big Data BDAC

INNO 1 0.819 0.242 0.070 INNO 3 0.811 0.238 0.049 INNO 5 0.743 0.224 0.194 INNO 4 0.735 0.180 0.191 INNO 2 0.728 0.211 0.149

Big Data 1 0.243 0.865 0.159 Big Data 2 0.241 0.797 0.275 Big Data 3 0.252 0.767 0.210 Big Data 4 0.377 0.752 0.200 BDAC 1 0.035 0.237 0.800 BDAC 2 0.175 0.023 0.762 BDAC 5 0.066 0.241 0.730 BDAC 4 0.285 0.237 0.622

Note: BDAC = Big data analytics capability. Bold numbers indicate items that load highly for the corresponding factor.

Following analysis of the U.S. sample, we used the mean values of big data and BDAC to divide the sample of Chinese NSD projects into four scenarios: two fits (high-high fit and low-low fit) and two misfits (high-low misfit and low-high misfit), as shown in Figure 3.

12

Note: *** p < 0.01 (two-tailed test). BDAC = Big data analytics capability. The Cronbach’s alpha for each

scale is on the diagonal in italics; the intercorrelations among the variables are on the off diagonal.

Table 5. The Chinese sample: factor loadings from exploratory factor analysis (N = 632).

Measure Items Innovativeness Big Data BDAC

INNO 1 0.819 0.242 0.070

INNO 3 0.811 0.238 0.049

INNO 5 0.743 0.224 0.194

INNO 4 0.735 0.180 0.191

INNO 2 0.728 0.211 0.149

Big Data 1 0.243 0.865 0.159

Big Data 2 0.241 0.797 0.275

Big Data 3 0.252 0.767 0.210

Big Data 4 0.377 0.752 0.200

BDAC 1 0.035 0.237 0.800

BDAC 2 0.175 0.023 0.762

BDAC 5 0.066 0.241 0.730

BDAC 4 0.285 0.237 0.622

Note: BDAC = Big data analytics capability. Bold numbers indicate items that load highly for the

corresponding factor.

Following analysis of the U.S. sample, we used the mean values of big data and BDAC to divide

the sample of Chinese NSD projects into four scenarios: two fits (high-high fit and low-low fit) and

two misfits (high-low misfit and low-high misfit), as shown in Figure 3.

Figure 3. The Chinese sample: fits and misfits between big data and BDAC (N = 632).

We used OLS dummy regression analysis to test the impacts of the two fits and the two misfits

on sustainable innovativeness. Table 6 shows the results of dummy regression analysis. To test the

three hypotheses, we used the “TEST” statement of the “Proc Reg Model” to examine whether or not

the coefficients estimated in the model were significantly different from each other as hypothesized.

We tested for possible differences of all six possible pairs and the results were all significant (p<0.01).

Figure 3. The Chinese sample: fits and misfits between big data and BDAC (N = 632).

We used OLS dummy regression analysis to test the impacts of the two fits and the two misfits on sustainable innovativeness. Table 6 shows the results of dummy regression analysis. To test the three hypotheses, we used the “TEST” statement of the “Proc Reg Model” to examine whether or not the coefficients estimated in the model were significantly different from each other as hypothesized. We tested for possible differences of all six possible pairs and the results were all significant (p<0.01).

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Table 6. The Chinese sample: results of dummy regression analysis (N = 632).

Dependent Variable: Sustainable Innovativeness

Parameter Estimate (β) Standard Error (S.E.) Standardized Estimate (b)

High-Low Misfit 5.660 *** 0.224 0.329 High-High Fit 6.748 *** 0.128 0.688 Low-Low Fit 4.130 *** 0.126 0.427

Low-High Misfit 4.653 *** 0.169 0.360 Model F-value 1315.420 ***

R-square 0.893 Adjusted R-square 0.893

Note: *** p < 0.01 (two-tailed test). High-Low Misfit = the misfit between high big data and low BDAC; High-High Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC; Low-High Misfit = the misfit between low big data and high BDAC. The six paired-wise tests indicate that all pairs are significantly different from each other at p < 0.01 (one-tailed test).

Our results show that both fits and misfits between big data and BDAC have significant positive impacts on sustainable innovativeness in China. The results from six paired-wise tests indicate that these effects differ from each other (p < 0.01). To examine whether or not each hypothesis is supported, we use the standardized estimates and the results of the paired-wise tests.

Results in Table 6 indicate that the positive effects of high-high fit (b = 0.688; p < 0.01) and low-low fit (b = 0.427; p < 0.01) on sustainable innovativeness are greater than for high-low misfit (b = 0.329; p < 0.01) and low-high misfit (b = 0.360; p < 0.01). Therefore, when there is a fit between big data and BDAC, NSD projects can achieve higher sustainable innovativeness. Thus, H1 is supported by the Chinese data.

Consistent with H2, the effect of high-high fit (b = 0.688; p < 0.01) on sustainable innovativeness is higher than that of low-low fit (b = 0.427; p < 0.01), indicating that NSD projects with high levels of both big data and BDAC can achieve higher sustainable innovativeness. Thus, H2 is also supported by the data.

As predicted by H3, the positive effect of low-high misfit (b = 0.360; p < 0.01) on sustainable innovativeness is greater than that of high-low misfit (b = 0.329; p < 0.01). Therefore, H3 is also supported by the Chinese data.

4.3. Empirical Study 3: Singapore

4.3.1. Measurement Validation

To collect data in Singapore, we used the same measurement items as for the U.S. sample. As in the Chinese sample, we distributed the study survey to 42 executives to conduct a pretest to ensure that the expression of each item would be accurately understood by the participants in Singapore. We made minor modifications on the formatting of the survey based on their feedback.

4.3.2. Data

To ensure comparability with the U.S. and China sample, companies were selected from the Singapore Stock Exchange and supplemented with a list of members of four business associations in Singapore. The data collection procedures described in the U.S. sample were adopted in Singapore. We ultimately collected complete data for 294 NSD projects: 14 NSD in hotel, traveling, and tourism services; 102 NSD in banking, insurances, securities, financial investments, and related activities; 62 NSD in information and semiconductor; 46 NSD in Internet-related services; and 70 NSD in health care services.

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4.3.3. Analysis and Results

The same data analyses are used to analyze the Singapore data. Table 7 shows the descriptive statistics and correlation coefficient matrix of each variable for the Singapore sample. The values on the diagonal are the Cronbach’s alpha coefficient for each variable, all of which are above 0.7, confirming the high validity of our study measures. We also conducted factor analysis of the scale items. As shown in Table 8, all factor loadings are between 0.641 and 0.884, indicating high structural validity of our measurement scale.

Table 7. The Singaporean sample: descriptive statistics and correlation coefficient matrix (N = 294).

Innovativeness Big Data BDAC

Innovativeness 0.881 Big Data 0.566 *** 0.915 BDAC 0.393 *** 0.521 *** 0.775 Mean 4.298 3.430 6.353 S.D. 2.184 2.507 2.167

Note: *** p < 0.01 (two-tailed test). BDAC = Big data analytics capability. The Cronbach’s alpha for each scale is on the diagonal in italics; the intercorrelations among the variables are on the off diagonal.

Table 8. The Singaporean sample: factor loading of variables (N = 294).

Measure Items Innovativeness Big Data BDAC

Innovativeness INNO 1 0.854 0.249 0.117 INNO 3 0.850 0.123 0.002 INNO 2 0.778 0.116 0.222 INNO 4 0.700 0.335 0.105 INNO 5 0.679 0.397 0.199

Big Data Big Data 1 0.214 0.884 0.168 Big Data 2 0.273 0.842 0.205 Big Data 4 0.280 0.831 0.197 Big Data 3 0.243 0.744 0.225

BDAC BDAC 1 0.058 0.240 0.817 BDAC 2 0.096 0.002 0.743 BDAC 4 0.302 0.242 0.703 BDAC 5 0.062 0.413 0.641

Note: BDAC = Big data analytics capability. Bold numbers indicate items that load highly for the corresponding factor.

Following Study 1 and 2, we used the mean values of big data and BDAC to divide the Singapore sample into fits (high-high fit and low-low fit) and misfits (high-low misfit and low-high misfit) categories as shown in Figure 4.

We then used OLS dummy regression analysis to test the impacts of the fits and misfits between big data and BDAC on sustainable innovativeness. To test the three hypotheses, we used the “TEST” statement of the “Proc Reg Model” to examine whether or not the coefficients estimated in the model were significantly different from each other as hypothesized. The results shown in Table 9 reveal that the fits and misfits between big data and BDAC have significant positive impacts on sustainable innovativeness. The results from six paired-wise tests indicate that these effects differ from each other (p < 0.10).

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15

Figure 4. The Singaporean sample: fits and misfits between big data and BDAC (N = 294).

Table 9. The Singaporean sample: results of dummy regression analysis (N = 294).

Dependent Variable: Sustainable Innovativeness

Parameter Estimate (β) Standard Error (S.E.) Standardized Estimate (b)

High-Low Misfit 5.144 *** 0.426 0.264

High-High Fit 6.091 *** 0.195 0.684

Low-Low Fit 3.215 *** 0.177 0.399

Low-High Misfit 3.642 *** 0.196 0.406

Model F-value 449.170 ***

R-square 0.861

Adjusted R-

square 0.859

Note: *** p < 0.01 (two-tailed test). High-Low Misfit = the misfit between high big data and low BDAC;

High-High Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big

data and low BDAC; Low-High Misfit = the misfit between low big data and high BDAC. The six

paired-wise tests indicate that all pairs are significantly different from each other at p < 0.10 (one-

tailed test).

To examine whether or not each hypothesis is supported, we used the standardized estimates

and the results of the paired-wise tests. The results in Table 9 indicate that high-high fit (b = 0.684; p

< 0.01) has the greatest impact on sustainable innovativeness. However, counter to H1, the positive

effect of low-low fit (b = 0.399; p < 0.01) on sustainable innovativeness is lower, not higher, than that

of low-high misfit (b = 0.406; p < 0.01). Thus, H1 is only partially supported by the Singapore data.

We further find that the effect of high-high fit (b = 0.684; p < 0.01) on sustainable innovativeness

is greater than that of low-low fit (b = 0.399; p < 0.01), indicating that H2 is supported by the Singapore

data.

The date also shows that as predicted by H3, the effect of low-high misfit (b = 0.406; p < 0.01) on

sustainable innovativeness is greater than that of high-low misfit (b = 0.264; p < 0.01). Thus, H3 is

supported by the Singaporean data.

4.4. Summary of Hypothesis Testing for All Three Empirical Studies

Figure 4. The Singaporean sample: fits and misfits between big data and BDAC (N = 294).

Table 9. The Singaporean sample: results of dummy regression analysis (N = 294).

Dependent Variable: Sustainable Innovativeness

Parameter Estimate (β) Standard Error (S.E.) Standardized Estimate (b)

High-Low Misfit 5.144 *** 0.426 0.264 High-High Fit 6.091 *** 0.195 0.684 Low-Low Fit 3.215 *** 0.177 0.399

Low-High Misfit 3.642 *** 0.196 0.406 Model F-value 449.170 ***

R-square 0.861 Adjusted R-square 0.859

Note: *** p < 0.01 (two-tailed test). High-Low Misfit = the misfit between high big data and low BDAC; High-High Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC; Low-High Misfit = the misfit between low big data and high BDAC. The six paired-wise tests indicate that all pairs are significantly different from each other at p < 0.10 (one-tailed test).

To examine whether or not each hypothesis is supported, we used the standardized estimates and the results of the paired-wise tests. The results in Table 9 indicate that high-high fit (b = 0.684; p < 0.01) has the greatest impact on sustainable innovativeness. However, counter to H1, the positive effect of low-low fit (b = 0.399; p < 0.01) on sustainable innovativeness is lower, not higher, than that of low-high misfit (b = 0.406; p < 0.01). Thus, H1 is only partially supported by the Singapore data.

We further find that the effect of high-high fit (b = 0.684; p < 0.01) on sustainable innovativeness is greater than that of low-low fit (b = 0.399; p < 0.01), indicating that H2 is supported by the Singapore data.

The date also shows that as predicted by H3, the effect of low-high misfit (b = 0.406; p < 0.01) on sustainable innovativeness is greater than that of high-low misfit (b = 0.264; p < 0.01). Thus, H3 is supported by the Singaporean data.

4.4. Summary of Hypothesis Testing for All Three Empirical Studies

Table 10 summarizes the results of the six paired-wise tests for three empirical studies. The results suggest the following results of the effects of fits and misfits on innovativeness:

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1. In the United States, high-high fit > high-low misfit > low-low fit > low-high misfit (p < 0.01). Therefore, H1 is partially supported because low-low fit < high-low misfit (not > as predicted by H1); and H2 is supported. However, counter to H3, the effect of low-high misfit fit on sustainable innovativeness is less, not higher (as predicted by H3), than High-Low Misfit is.

2. In China, high-high fit > low-low fit > low-high misfit > high-low misfit (p < 0.01). Therefore, all three hypotheses are supported as predicted.

3. In Singapore, high-high fit > low-high misfit > low-low fit > high-low misfit (p < 0.10). Therefore, H1 is partially supported because low-low fit < low-high misfit (not > as predicted by H1); and both H2 and H3 are supported.

Table 10. Summary results of three hypotheses in three countries.

Hypothesis Pair Comparison The United

States (N = 477)

China (N = 632)

Singapore (N = 294)

H1 (fits > misfits) High-High Fit > Low-High Misfit 39.680 *** 98.070 *** 78.300 *** High-High Fit > High-Low Misfit 11.860 *** 17.760 *** 4.070 ** Low-Low Fit > Low-High Misfit 21.640 *** 6.180 *** 2.620 * (<) Low-Low Fit > High-Low Misfit 59.020 *** (<) 35.350 *** 17.480 ***

H2 (HH > LL) High-High Fit > Low-Low Fit 197.290 *** 212.910 *** 119.450 *** H3 (LH > HL) Low-High Misfit > High-Low Misfit 6.220 *** (<) 12.860 *** 10.240 ***

Note: Numbers in Table 10 are F-statistics. (<) indicates that the effect is “less, not higher as predicted by the hypothesis”. * p < 0.10; ** p < 0.05; *** p < 0.01 (because all hypotheses are directional, one-tailed test is used). High-Low Misfit = the misfit between high big data and low BDAC; High-High Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC; Low-High Misfit = the misfit between low big data and high BDAC.

5. Cross-National Comparative Analyses

To explore the similarities and differences among our samples in the United States, China, and Singapore, we summarize the standardized estimates of fits and misfits on sustainable innovativeness in Table 11. The results suggest that a high level of big data matched with a high level of BDAC has the greatest positive effect on sustainable innovativeness. The importance of the other three scenarios differs across countries.

Table 11. Ranking of the standardized estimates of the effects of fits and misfits on sustainable innovativeness.

Dependent Variable: Sustainable Innovativeness

Rank The United States

(Standardized Estimate b) China

(Standardized Estimate b) Singapore

(Standardized Estimate b)

1 High-High Fit (0.701) High-High Fit (0.688) High-High Fit (0.684) 2 High-Low Misfit (0.400) Low-Low Fit (0.427) Low-High Misfit (0.406) 3 Low-Low Fit (0.384) Low-High Misfit (0.360) Low-Low Fit (0.399) 4 Low-High Misfit (0.340) High-Low Misfit (0.329) High-Low Misfit (0.264)

Note: High-Low Misfit = the misfit between high big data and low BDAC; High-High Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC; Low-High Misfit = the misfit between low big data and high BDAC.

In the United States, high-low misfit has a larger effect on sustainable innovativeness than low-low fit and low-high misfit do. Low-high misfit has the least effect on sustainable innovativeness. The significant differences are validated by the paired-wise tests (p < 0.01). Access to high big data resources provides project leaders with rich information about markets, customers, and competitors to inform innovation activities [19]. A low level of big data resources reduces project team’s ability to accurately evaluate the market development and demand directions, resulting in misdirected

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innovation activities and missed market opportunities. In addition, when big data is lacking, too much BDAC can cause capacity redundancy and blur the focus of existing big data analysis, leading to ineffective innovation activities.

In China, low-low fit has a larger impact on sustainable innovativeness than low-high misfit and high-low misfit. Fits are better than misfits. Results of paired-wise tests in Table 10 suggest that the differences are significant (p < 0.01). Thus, for NSD projects in China, it is important that the levels of big data and BDAC be in alignment to support the improvement of sustainable innovativeness. When there is high big data and low BDAC, projects are unable to meet the needs for data analysis, and experience data overload and blind innovation.

In Singapore, a high level of BDAC can improve sustainable innovativeness: after high-high fit, low-high misfit has the largest impact, followed by low-low fit and high-low misfit. Results of paired-wise tests in Table 10 suggest that the differences are significant (p < 0.10). The effect of low-high misfit on sustainable innovativeness is 1.538 times higher (0.406/0.264) than that of high-low misfit, indicating that big data on its own is unlikely to be a source of competitive advantage for NSD projects in Singapore [33], but a high level of BDAC can lead to effective mining and analysis of the available big data to create benefits for NSD projects.

To further evaluate cross-national differences on how fits and misfits affect sustainable innovativeness, we performed dummy regression analyses using pooled data of three countries. The United States is the base case. Two country dummy variables (China and Singapore) and eight interaction terms (country dummy variables multiply by four fits and misfits) were introduced into the equation. Table 12 presents the results of the analyses. The four coefficient estimates for the four interaction terms with China (or Singapore) as dummy variable show the differences between the United States and China (or Singapore). The differences between China and Singapore can be evaluated by using the sum of the coefficients (U.S. + China vs. U.S. + Singapore). We used “TEST” option in the model statement of the “Proc Reg” to compare the estimates. We present the results in Table 13.

Table 12. Results of regression analysis using pooled data (N = 1403).

Dependent Variable: Sustainable Innovativeness

Independent Variables Parameter Estimate

(β) Standard Error

(S.E.) Standardized Estimate

(b)

High-Low Misfit 6.118 *** 0.211 0.368 High-High Fit 6.963 *** 0.137 0.718 Low-Low Fit 4.179 *** 0.150 0.429

Low-High Misfit 5.380 *** 0.218 0.423 China × High-Low Misfit −0.458 0.304 −0.018

China × High-High Fit −0.215 0.185 −0.015 China × Low-Low Fit −0.049 0.194 −0.003

China × Low-High Misfit −0.727 *** 0.273 −0.038 Singapore × High-Low Misfit −0.974 ** 0.481 −0.019

Singapore × High-High Fit −0.873 *** 0.241 −0.038 Singapore × Low-Low Fit −0.963 *** 0.234 −0.046

Singapore × Low-High Misfit −1.738 *** 0.295 −0.075

Model F-value 1006.620 *** R-square 0.897

Adjusted R-square 0.896

Note: ** p < 0.05; *** p < 0.01 (two-tailed test). High-Low Misfit = the misfit between high big data and low BDAC; High-High Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC; Low-High Misfit = the misfit between low big data and high BDAC. China = 1 if the sample is Chinese; 0 otherwise. Singapore = 1 if the sample is Singaporean; 0 otherwise. The base case is the United States.

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Table 13. Testing results of the cross-national differences between China and Singapore.

China Singapore Does the Effect Differ?

(F-Statistics and Significant Level)

The Effect of High-Low Misfit The Effect of High-Low Misfit 1.130 The Effect of High-High Fit The Effect of High-High Fit 7.900 *** The Effect of Low-Low Fit The Effect of Low-Low Fit 17.700 ***

The Effect of Low-High Misfit The Effect of Low-High Misfit 15.300 ***

Note: *** p < 0.01 (two-tailed test). High-Low Misfit = the misfit between high big data and low BDAC; High-High Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC; Low-High Misfit = the misfit between low big data and high BDAC. Dummy variables: China = 1 if the sample is Chinese, 0 if not; Singapore = 1 if the sample is Singaporean, 0 if not.

The results in Tables 12 and 13 suggest that the coefficients for interaction terms (for both China and Singapore) are all negative and that the numbers are more negative in Singapore than in China. Therefore, the effects of fits and misfits on innovativeness is highest in the U.S. than in China and in Singapore. The results suggest following additional cross-national differences for each of the scenarios:

(1) For effect of high-low misfit on sustainable innovativeness, the effect is less (β = −0.974; p < 0.05), in Singapore than in the U.S. There are no significant differences in the effect between U.S. and China (p > 0.10) and between China and Singapore (p > 0.10).

(2) For effect of high-high fit on sustainable innovativeness, the effect is the largest in the U.S. (β = 6.963), the same in China (−0.215) but it is not significantly different from the U.S. with p > 0.10), and the smallest in Singapore (β = 6.963–0.873= 6.090; p < 0.01). The results in Table 12 suggest that the difference between U.S. and Singapore is significant (p < 0.01). The results in Table 13 indicate that the difference between China and Singapore is significant (p < 0.01).

(3) For effect of low-low fit on sustainable innovativeness, the effect is also the highest in the U.S. (β = 4.179), the same in China (−0.049 but it is not significantly different from the U.S. with p > 0.10), and the lowest in Singapore (β = 4.179–0.963= 3.216; p < 0.01). The results in Table 12 suggest that the difference between U.S. and Singapore is significant (p < 0.01). The results in Table 13 indicate that the difference between China and Singapore is significant (p < 0.01).

(4) For low-high misfit on sustainable innovativeness, the effect is the highest in the U.S. (β = 5.380), second in China (β = 5.380–0.727 = 4.653) and lowest in Singapore (β = 5.380–1.738 = 3.642). The differences are all significant (p < 0.01).

6. Conclusions, Implications, and Future Research

6.1. Conclusions

Based on the IPT, we developed a theoretical model for studying the differential effects of fits and misfits between big data and BDAC on sustainable innovativeness. We investigated four scenarios and their impacts on sustainable innovativeness in a three-country comparative study. We tested for significant differences between six pairs of the combinations and between the three pairs of the countries. The empirical results provided at least partial supports for all three hypotheses.

First, as predicted by Hypothesis 1, we found that in China the effect of fits between big data and BDAC on sustainable innovativeness is always stronger than that of misfits. However, in the United States and Singapore, we found that the effect of low-low fit on sustainable innovativeness is lower than that of misfits, indicating that the effect of fits between big data and BDAC on sustainable innovativeness is not always stronger than that of misfits in these countries. This finding challenges the assertions of previous research that fit between information, and information processing capability is necessary to obtain value for the firm [4,7].

Second, as hypothesized in H3, across all three countries, we found that the positive impact of high-high fit on sustainable innovativeness is greater than that of low-low fit. This finding supports the conclusions of previous research that a high level of big data is a high-quality resource that can be fully

Sustainability 2020, 12, 1984 19 of 23

interpreted with a high level of BDAC to provide NSD project managers with insights into markets and customers and thereby ensure the development of successful service products [10,19,30,33]. Our finding that high levels of big data and BDAC can maximize sustainable innovativeness thus adds to the results of Hao et al. [2], who suggested that when big data is high, improving BDAC will inhibit innovation performance.

Third, we found significant differences in the impact of low-high misfit and high-low misfit on sustainable innovativeness across the three countries. In the United States, the positive impact of high-low misfit on sustainable innovativeness is higher than that of low-high misfit. This result, consistent with Tan and Zhan [3], shows that rich big data resources can provide more sufficient, reliable, and relevant information to guarantee the success of NSD projects even if BDAC is insufficient to fully exploit these resources. Contrary to Song et al. [38], who found that the level of marketing and R&D resources has an insignificant relationship with product innovativeness, we found that if U.S. firms pursuing NSD projects lack big data resources, they cannot accurately obtain the valuable information needed to ensure the sustainable innovativeness of service products. In contrast, in China and Singapore, the impact of high-low misfit on sustainable innovativeness is less, not greater, than that of low-high misfit. This result suggests that firms in China and in Singapore should operate differently from firms in the U.S. They need to focus on increasing big data rather than BDAC to successfully develop innovative service products. As Rialti et al. [35], Gupta and George [10], and Ferraris et al. [11] have also found, even if there are limited big data resources, increasing BDAC can enable project leaders to integrate and internalize existing big data information to improve the sustainable innovativeness of projects.

Finally, the results from cross-national comparative analyses reveal four major conclusions. First, the fits have greater effect on sustainable innovativeness in the U.S. and in China than that in Singapore. Second, the impact of high-low misfit on sustainable innovativeness is higher in the U.S. than in Singapore. Third, the positive effect of low-high misfit on sustainable innovativeness is the largest in the U.S., followed by China, and then by Singapore. The possible reasons may be that there are differences in the development speed of big data and analytics capability among the three countries. Firms in the U.S. are better with applying big data and BDAC to develop innovative services and products than firms in China and in Singapore are.

6.2. Theoretical Implications

This research enriches the literature on big data and innovation in several ways. First, this study expands the application of the IPT with regard to big data. Previous studies on the IPT have focused on firms’ need for traditional information sources and information processing capability [21,24]. However, in the current marketplace, the need for information is largely affected by big data, which necessitates higher information processing capability [19]. This study specifically considers big data and BDAC, explores the application of the IPT in the context of big data and service innovation, and complements existing research on the IPT [23,24].

Although other scholars such as Isik [4] have discussed the need for big data and information processing capability and stressed the importance of their alignment to generate value from big data, they have neither specified measurement items for these constructs nor conducted in-depth empirical tests. Thus, this study fills these gaps in the empirical analysis of big data and BDAC by using fieldwork and case studies to refine the definitions and connotations of big data and BDAC, improving existing measurement scales, and proposing systematic measurement scales [14]. This study is also the first to consider both fits and misfits between big data and BDAC and assess their impacts on sustainable innovativeness. This not only enhances the previous research focusing only on the impact of big data or BDAC [3,14,16,19] but also contributes to research on sustainable innovativeness [18] by demonstrating the important impact of different configurations of fit between big data and BDAC in the context of service innovation.

Sustainability 2020, 12, 1984 20 of 23

Finally, this study enriches the theory of cross-national big data management. Previous research on big data and BDAC has mostly focused on the data of a single country [3,17,35]. In this study we conducted a comparative analysis across three countries. By analyzing the data from NSD projects in the United States, China, and Singapore, we explored the similarities and differences of fits and misfits between big data and BDAC in the process of service innovation in these countries, building the literature in this area.

6.3. Managerial Implications

The results of our analysis of the impact of fits and misfits between big data and BDAC on sustainable innovativeness offer targeted recommendations for project managers in the different countries to achieve successful service innovation.

First, when there are sufficient resources available, NSD project managers in the United States, China, and Singapore should all invest in both big data and BDAC to improve sustainable innovativeness. It is important that managers ensure the synchronous improvement of both big data and BDAC and not emphasize the development of one aspect over the other.

Second, if resources are limited, then the recommended development strategies for project managers differ among the three countries.

NSD project managers in the United States should invest in large amounts of high-quality big data to ensure that the project always has a high level of big data resources to serve as the foundation of the project. Project managers can improve their big data resources in four ways: (1) increase the quantity and stock of big data as much as possible and constantly update the existing data to ensure its timeliness so team members can understand changing market conditions and make timely adjustments to the project; (2) build a data warehouse or mart to integrate various internal and external sources of big data (e.g., customer demand, market development trends, business processing, competitor information, etc.) and create a comprehensive knowledge base; (3) invest sufficient funds in NSD projects so they can be fully developed; and (4) allocate time for effective analysis of big data to ensure retention of reliable and relevant information, avoid decision-making mistakes, and achieve successful project outcomes.

In China, managers can improve sustainable innovativeness by ensuring that big data and BDAC maintain a balanced level. For example, if an NSD project has less big data, it should not invest in further improving analysis tools and technologies but instead should focus on in-depth analysis of existing data.

In Singapore, NSD project managers should focus on improvement of BDAC by investing in pertinent analysis technologies and tools to enhance the ability of the project team to transform big data into useful information. Managers can improve BDAC in three ways: (1) introduce advanced analysis and algorithm tools, effectively analyze big data of different structure forms, extract all information related to development activities, and find the connection between different processes and activities; (2) focus on predicting potential market opportunities and development trends from existing data resources; and (3) recruit high-quality team members with strong analytical skills and provide regular training to assist team members in adapting to the development of technology and analysis tools. Overall, project managers need to build a data-driven culture in their firm that supports big data thinking and improves the sensitivity and cognitive ability of employees with regard to data.

6.4. Limitations and Future Research

There are several shortcomings of this study that can be improved upon in future work. We focused here only on sustainable innovativeness as an important indicator of service innovation output. Future studies should also consider how fits and misfits affect the quality of new service products, the adoption of new service products, and innovation speed. These are all important sustainable competitive advantages for sustainable service development. Furthermore, our study sample included only five industries. Future studies should collect more data in other industries to

Sustainability 2020, 12, 1984 21 of 23

assess the generalizability of the research conclusions. Although we gained valuable insight from our analysis of data from the United States, China, and Singapore, future endeavors can be enhanced with data from other countries, particularly those that represent a variety of economic and cultural systems, to further enrich cross-national comparative research and contribute to the understanding of the sustainability of new service development.

Author Contributions: M.S. and H.Z. share the first-authorship of this article. H.Z. is corresponding author. Conceptualization, M.S., J.H., and H.Z.; methodology, M.S. and H.Z.; data curation, H.Z. and M.S.; writing—original draft preparation, J.H., H.Z., and M.S., writing-review and editing, M.S., J.H., and H.Z.; funding acquisition, H.Z. All authors have read and agreed to the authorship and content of the article. All authors have read and agreed to the published version of the manuscript.

Funding: This research was funded by the Humanities and Social Science Project of the China Ministry of Education under the grant with project title: “Breakthrough service innovation: effects of big data analytics and AI capability”. The partial funding was also supported by the Natural Science Foundation of Shaanxi Province of China, grant number 2018JQ7003.

Acknowledgments: The authors thank assistant editor of Sustainability and two anonymous reviewers for their useful suggestions which improve the quality of this article. The literature review and hypothesis development were based on the graduation thesis of Jinjin Heng.

Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Appendix A. Study Measures and Sources

Big Data (adopted from Gupta and George [10]). (0 = strongly disagree; 5 = neutral; 10 = strongly agree)

(1) We have access to very large, unstructured, or fast-moving data for analysis. (2) We integrate data from multiple internal sources into a data warehouse or mart for easy access. (3) We integrate external data with internal to facilitate high-value analysis of our

business environment. (4) Our big data analytics projects are adequately funded. (5) * Our big data analytics projects are given enough time to achieve their objectives.

Big Data Analytics Capability (BDAC) (adopted from Hao et al. [2]).

(1) We have advanced tools (analytics and algorithms) to extract values of the big data. (0 = no capability; 5 = median level; 10 = very high level of capability; adopted from Hao et al. [2], which was derived from Dubey et al. [34]; Gupta and George [10]).

(2) Our capability to discover relationships and dependencies from the big data is: (0 = no capability; 5 = neutral; 10 = very high level of capability; adopted from Hao et al. [2], which was developed based on field research).

(3) * Our capability to perform predictions of outcomes and behaviors from the big data is: (0 = no capability; 5 = median level; 10 = very high level of capability; adopted from Hao et al. [2], which was derived from Gupta and George [10]).

(4) Our capability to discover new correlations from the big data to spot market demand trends and predict user behavior is: (0 = no capability; 5 = median level; 10 = very high level of capability; adopted from Hao et al. [2]; which was derived from Akter et al. [14]; Wamba et al. [33]).

(5) Our big data analytics staff has the right skills to accomplish their jobs successfully. (0 = none; 5 = median level; 10 = very high level of capability; adopted from Hao et al. [2], which was derived from Gupta and George [10]).

Sustainable Innovativeness (adapted from Song and Parry [37]). (Minor modifications were made based on the pretests as reported in the text. The changes are shown below using the notations: deletion is

Sustainability 2020, 12, 1984 22 of 23

marked using

22

Sustainable Innovativeness (adapted from Song and Parry [37]). (Minor modifications were made based on the pretests as reported in the text. The changes are shown below using the notations: deletion is marked using deletion. Added text is marked with underline.) (0=strongly disagree; 5=neutral; 10=strongly agree).

(1) Our The products and services often incorporate innovative technologies which have never been used in the industry before. (2) Our The products and services caused significant changes in the whole industry. (3) Our The products and services are one of the first of its kind introduced into the market. (4) Our The products and services are highly innovative—totally new to the market. (5) Our The products and services are perceived as most innovative in the industry.

Note: * indicates that the item was deleted based on factor analyses as described in the text.

References.

1. Mcafee, A.; Brynjolfsson, E. Big data: The management revolution. Harv. Bus. Rev. 2012, 90, 60–68. 2. Hao, S.; Zhang, H.; Song, M. Big data, big data analytics capability, and sustainable innovation

performance. Sustainability 2019, 11, 7145. 3. Tan, K.H.; Zhan, Y. Improving new product development using big data: A case study of an electronics

company. R&D Manag. 2016, 47, 570–582. 4. Isik, Ö. Big Data Capabilities: An Organizational Information Processing Perspective. In Analytics and Data

Science. Annals of Information Systems; Deokar, A., Gupta, A., Iyer, L., Jones, M., Eds.; Springer: Berlin, 2018; pp. 29–40.

5. George, G.; Osinga, E.C.; Lavie, D.; Scott, B. Big data and data science methods for management research. Acad. Manag. J. 2016, 59, 1493–1507.

6. Peng, D.X.; Heim, G.R.; Mallick, D.N. Collaborative product development: The effect of project complexity on the use of information technology tools and new product development practices. Prod. Oper. Manag. 2014, 23, 1421–1438.

7. Tushman, M.; Nadler, D. Information processing as an integrating concept in organization design. Acad. Manag. Rev. 1978, 3, 613–624.

8. Galbraith, J.R. Organization design: An information processing view. Interfaces 1974, 4, 28–36. 9. LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444. 10. Gupta, M.; George, J.F. Toward the development of a big data analytics capability. Inf. Manag. Amst. 2016,

53, 1049–1064. 11. Ferraris, A.; Mazzoleni, A.; Devalle, A.; Couturier, J. Big data analytics capabilities and knowledge

management: Impact on firm performance. Manag. Deci. 2019, 57, 1923–1936. 12. Bumblauskas, D.; Nold, H.; Bumblauskas, P.; Igou, A. Big data analytics: Transforming data to action. Bus.

Proc. Manag. J. 2017, 23, 703–720. 13. Ghasemaghaei, M.; Ebrahimi, S.; Hassanein, K. Data analytics competency for improving firm decision

making performance. J. Strateg. Inf. Syst. 2018, 27, 101–113. 14. Akter, S.; Wamba, S.F.; Gunasekaran, A.; Dubey, R.; Childe, S.J. How to improve firm performance using

big data analytics capability and business strategy alignment. Int. J. Prod. Econ. 2016, 182, 113–131. 15. Jin, D.H.; Kim, H.J. Integrated understanding of big data, big data analysis, and business intelligence: A

case study of logistics. Sustainability 2018, 10, 3778. 16. Tan, K.H. Managerial perspectives of big data analytics capability towards product innovation. Strateg.

Direc. 2018, 34, 33–35. 17. Wang, Y.; Hajli, N. Exploring the path to big data analytics success in healthcare. J. Bus. Res. 2017, 70, 287–

299. 18. Su, Z.; Ahlstrom, D.; Li, J.; Cheng, D. Knowledge creation capability, absorptive capacity, and product

innovativeness. R&D Manag. 2013, 43, 473–485. 19. Johnson, J.S.; Friend, S.B.; Lee, H.S. Big data facilitation, utilization, and monetization: Exploring the 3Vs

in a new product development process. J. Prod. Innov. Manag. 2017, 34, 640–658. 20. Urbinati, A.; Bogers, M.; Chiesa, V.; Frattini, F. Creating and capturing value from big data: A multiple-

case study analysis of provider companies. Technovation 2019, 84–85, 21–36.

. Added text is marked with underline.) (0 = strongly disagree; 5 = neutral; 10 = strongly agree).

(1) The products and services ofter incorporate innovative technologies which have never been used in the industry before.

(2) The products and services caused significant changes in the whole industry. (3) The products and services are one of the first of its kind introduced into the market. (4) The products and services are highly innovative—totally new to the market. (5) The products and services are perceived as most innovative in the industry.

Note: * indicates that the item was deleted based on factor analyses as described in the text.

References

1. Mcafee, A.; Brynjolfsson, E. Big data: The management revolution. Harv. Bus. Rev. 2012, 90, 60–68. [PubMed] 2. Hao, S.; Zhang, H.; Song, M. Big data, big data analytics capability, and sustainable innovation performance.

Sustainability 2019, 11, 7145. [CrossRef] 3. Tan, K.H.; Zhan, Y. Improving new product development using big data: A case study of an electronics

company. R&D Manag. 2016, 47, 570–582. 4. Isik, Ö. Big Data Capabilities: An Organizational Information Processing Perspective. In Analytics and

Data Science. Annals of Information Systems; Deokar, A., Gupta, A., Iyer, L., Jones, M., Eds.; Springer: Berlin, Germany, 2018; pp. 29–40.

5. George, G.; Osinga, E.C.; Lavie, D.; Scott, B. Big data and data science methods for management research. Acad. Manag. J. 2016, 59, 1493–1507. [CrossRef]

6. Peng, D.X.; Heim, G.R.; Mallick, D.N. Collaborative product development: The effect of project complexity on the use of information technology tools and new product development practices. Prod. Oper. Manag. 2014, 23, 1421–1438. [CrossRef]

7. Tushman, M.; Nadler, D. Information processing as an integrating concept in organization design. Acad. Manag. Rev. 1978, 3, 613–624.

8. Galbraith, J.R. Organization design: An information processing view. Interfaces 1974, 4, 28–36. [CrossRef] 9. LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444. [CrossRef] 10. Gupta, M.; George, J.F. Toward the development of a big data analytics capability. Inf. Manag. Amst. 2016,

53, 1049–1064. [CrossRef] 11. Ferraris, A.; Mazzoleni, A.; Devalle, A.; Couturier, J. Big data analytics capabilities and knowledge

management: Impact on firm performance. Manag. Deci. 2019, 57, 1923–1936. [CrossRef] 12. Bumblauskas, D.; Nold, H.; Bumblauskas, P.; Igou, A. Big data analytics: Transforming data to action.

Bus. Proc. Manag. J. 2017, 23, 703–720. [CrossRef] 13. Ghasemaghaei, M.; Ebrahimi, S.; Hassanein, K. Data analytics competency for improving firm decision

making performance. J. Strateg. Inf. Syst. 2018, 27, 101–113. [CrossRef] 14. Akter, S.; Wamba, S.F.; Gunasekaran, A.; Dubey, R.; Childe, S.J. How to improve firm performance using big

data analytics capability and business strategy alignment. Int. J. Prod. Econ. 2016, 182, 113–131. [CrossRef] 15. Jin, D.H.; Kim, H.J. Integrated understanding of big data, big data analysis, and business intelligence: A case

study of logistics. Sustainability 2018, 10, 3778. [CrossRef] 16. Tan, K.H. Managerial perspectives of big data analytics capability towards product innovation. Strateg. Direc.

2018, 34, 33–35. [CrossRef] 17. Wang, Y.; Hajli, N. Exploring the path to big data analytics success in healthcare. J. Bus. Res. 2017, 70,

287–299. [CrossRef] 18. Su, Z.; Ahlstrom, D.; Li, J.; Cheng, D. Knowledge creation capability, absorptive capacity, and product

innovativeness. R&D Manag. 2013, 43, 473–485. 19. Johnson, J.S.; Friend, S.B.; Lee, H.S. Big data facilitation, utilization, and monetization: Exploring the 3Vs in a

new product development process. J. Prod. Innov. Manag. 2017, 34, 640–658. [CrossRef] 20. Urbinati, A.; Bogers, M.; Chiesa, V.; Frattini, F. Creating and capturing value from big data: A multiple-case

study analysis of provider companies. Technovation 2019, 84–85, 21–36. [CrossRef]

Sustainability 2020, 12, 1984 23 of 23

21. Bensaou, M.; Venkatraman, N. Configurations of interorganizational relationships: A comparison between U.S. and Japanese automakers. Manag. Sci. 1995, 41, 1471–1492. [CrossRef]

22. Gómez, J.; Salazar, I.; Vargas, P. Firm boundaries, information processing capacity, and performance in manufacturing firms. J. Manag. Inf. Syst. 2016, 33, 809–842. [CrossRef]

23. Song, M.; Bij, H.V.D.; Weggeman, M. Determinants of the level of knowledge application: A knowledge-based and information processing perspective. J. Prod. Innov. Manag. 2005, 22, 430–444. [CrossRef]

24. Moser, R.; Kuklinski, J.W.; Srivastava, M. Information processing fit in the context of emerging markets: An analysis of foreign SBUs in China. J. Bus. Res. 2017, 70, 234–247. [CrossRef]

25. Venkatraman, N.; Camillus, J.C. Exploring the concept of “fit” in strategic management. Acad. Manag. Rev. 1984, 9, 513–525.

26. Oncioiu, I.; Bunget, O.C.; Türkes, , M.C.; Căpus, neanu, S.; Topor, D.I.; Tamas, , A.S.; Rakos, , I.S.; Hint, M.S, . The impact of big data analytics on company performance in supply chain management. Sustainability 2019, 11, 4864. [CrossRef]

27. George, G.; Haas, M.R.; Pentland, A. Big data and management. Acad. Manag. J. 2014, 57, 321–326. [CrossRef] 28. Hu, F.; Liu, W.; Tsai, S.B.; Gao, J.; Bin, N.; Chen, Q. An empirical study on visualizing the intellectual structure

and hotspots of big data research from a sustainable perspective. Sustainability 2018, 10, 667. [CrossRef] 29. Chen, H.; Chiang, R.H.L.; Storey, V.C. Business intelligence and analytics: From big data to big impact. MIS

Q. 2012, 36, 1165–1188. [CrossRef] 30. Günther, W.A.; Rezazade Mehrizi, M.H.; Huysman, M.; Feldberg, F. Debating big data: A literature review

on realizing value from big data. J. Strateg. Inf. Syst. 2017, 26, 191–209. [CrossRef] 31. Ghasemaghaei, M. The role of positive and negative valence factors on the impact of bigness of data on big

data analytics usage. Int. J. Inf. Manag. 2020, 50, 395–404. [CrossRef] 32. Ghasemaghaei, M.; Calic, G. Does big data enhance firm innovation competency? The mediating role of

data-driven insights. J. Bus. Res. 2019, 104, 69–84. [CrossRef] 33. Wamba, S.F.; Gunasekaran, A.; Akter, S.; Ren, S.J.; Dubey, R.; Childe, S.J. Big data analytics and firm

performance: Effects of dynamic capabilities. J. Bus. Res. 2017, 70, 356–365. [CrossRef] 34. Dubey, R.; Gunasekaran, A.; Childe, S.J. Big data analytics capability in supply chain agility: The moderating

effect of organizational flexibility. Manag. Deci. 2019, 57, 2092–2112. [CrossRef] 35. Rialti, R.; Zollo, L.; Ferraris, A.; Alon, I. Big data analytics capabilities and performance: Evidence from a

moderated multi-mediation model. Technol. Forecast. Soc. Chang. 2019, 149, 1–10. [CrossRef] 36. Danneels, E.; Kleinschmidtb, E.J. Product innovativeness from the firm’s perspective: Its dimensions and

their relation with project selection and performance. J. Prod. Innov. Manag. 2001, 18, 357–373. [CrossRef] 37. Song, X.M.; Parry, M.E. Challenges of managing the development of breakthrough products in Japan. J. Oper.

Manag. 1999, 17, 665–688. [CrossRef] 38. Song, L.Z.; Song, M.; Di Benedetto, C.A. Resources, supplier investment, product launch advantages, and

first product performance. J. Oper. Manag. 2011, 29, 86–104. [CrossRef] 39. Calantone, R.J.; Chan, K.; Cui, A.S. Decomposing product innovativeness and its effects on new product

success. J. Prod. Innov. Manag. 2006, 23, 408–421. [CrossRef] 40. McNally, R.C.; Cavusgil, E.; Calantone, R.J. Product innovativeness dimensions and their relationships with

product advantage, product financial performance, and project protocol. J. Prod. Innov. Manag. 2010, 27, 991–1006. [CrossRef]

41. Cillo, P.; De Luca, L.M.; Troilo, G. Market information approaches, product innovativeness, and firm performance: An empirical study in the fashion industry. Res. Policy 2010, 39, 1242–1252. [CrossRef]

42. Tsai, K.H.; Liao, Y.C.; Hsu, T.T. Does the use of knowledge integration mechanisms enhance product innovativeness. Ind. Mark. Manag. 2015, 46, 214–223. [CrossRef]

43. Song, X.M.; Parry, M.E. What separates Japanese new product winners from losers. J. Prod. Innov. Manag. 1996, 13, 422–439. [CrossRef]

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Motivation to use big data and big data analytics in external auditing

Lina Dagilien_e and Lina Klovien_e Kauno Technologijos Universitetas, Kaunas, Lithuania

Abstract Purpose – This paper aims to explore organisational intentions to use Big Data and Big Data Analytics (BDA) in external auditing. This study conceptualises different contingent motivating factors based on prior literature and the views of auditors, business clients and regulators regarding the external auditing practices and BDA. Design/methodology/approach – Using the contingency theory approach, a literature review and 21 in- depth interviews with three different types of respondents, the authors explore factors motivating the use of BDA in external auditing. Findings – The study presents a few key findings regarding the use of BD and BDA in external auditing. By disclosing a comprehensive view of current practices, the authors identify two groups of motivating factors (company-related and institutional) and the circumstances in which to use BDA, which will lead to the desired outcomes of audit companies. In addition, the authors emphasise the relationship of audit companies, business clients and regulators. The research indicates a trend whereby external auditors are likely to focus on the procedures not only to satisfy regulatory requirements but also to provide more value for business clients; hence, BDA may be one of the solutions. Research limitations/implications – The conclusions of this study are based on interview data collected from 21 participants. There is a limited number of large companies in Lithuania that are open to co- operation. Future studies may investigate the issues addressed in this study further by using different research sites and a broader range of data. Practical implications – Current practices and outcomes of using BD and BDA by different types of respondents differ significantly. The authors wish to emphasise the need for audit companies to implement a BD-driven approach and to customise their audit strategy to gain long-term efficiency. Furthermore, the most challenging factors for using BDA emerged, namely, long-term audit agreements and the business clients’ sizes, structures and information systems. Originality/value – The original contribution of this study lies in the empirical investigation of the comprehensive state-of-the-art of BDA usage and motivating factors in external auditing. Moreover, the study examines the phenomenon of BD as one of the most recent and praised developments in the external auditing context. Finally, a contingency-based theoretical framework has been proposed. In addition, the research also makes a methodological contribution by using the approach of constructivist grounded theory for the analysis of qualitative data.

Keywords Big data, Contingent factors, Big data analytics, External auditing

Paper type Research paper

1. Introduction In the past several years, the technology of Big Data (BD) has gained remarkably in popularity within a variety of sectors, ranging from business and government to scientific and research fields (Ajana, 2015). The area of accounting and auditing is not an exception, as companies are confronted by an unprecedented level of semi-structured and unstructured

The authors are pleased to acknowledge comments on earlier version of the paper from delegates at 38th EAA Congress, Glasgow, April 2015.

MAJ 34,7

750

Received 27 January 2018 Revised 5 July 2018 18 September 2018 21 November 2018 Accepted 13 December 2018

Managerial Auditing Journal Vol. 34 No. 7, 2019 pp. 750-782 © EmeraldPublishingLimited 0268-6902 DOI 10.1108/MAJ-01-2018-1773

The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0268-6902.htm

massive data, which companies have to use and manage to be innovative, effective and competitive. On one hand, we can see excitement about BD emerging because of the recognition of opportunities in various areas (Marshall et al., 2015; Verma and Bhattacharyya, 2017; Vera-Baquero et al., 2015; Enget et al., 2017). On the other hand, the concept of BD is still confused (for example, social media data or business data) (Connelly et al., 2016; Harford, 2014) and quite vague in terms of the circumstances of use.

According to Wang and Cuthbertson (2015), the potentially important role played by BD and Big Data Analytics (BDA) in innovative auditing practice is evident. Quite a few studies have discussed and analysed broad areas of BD and BDA in external auditing by explaining and providing a context for researchers, drawing their attention to it in terms of general issues (Alles and Gray, 2016; Alles, 2015; Earley, 2015; Wang and Cuthbertson, 2015; Arnaboldi et al., 2017; Connelly et al., 2016) and arguing that the use of BDA is appropriate and valuable to ensure the audit quality (Dubey and Gunasekaran, 2015; Brown-Liburd et al., 2015; Vasarhelyi et al., 2015). BDA may improve the efficiency and effectiveness of financial statement audits (KPMG, 2017; Cao et al., 2015; Yoon et al., 2015; Gepp et al., 2018), but additional competencies and technological capabilities are necessary to implement BDA (KPMG, 2017; Enget et al., 2017; Dubey and Gunasekaran, 2015; Brown-Liburd et al., 2015; Zhang et al., 2015; Appelbaum et al., 2017, 2018).

Nonetheless, auditing is lagging behind the other research streams in the use of valuable BDA (Gepp et al., 2018). However, research on understanding the motives for using BDA is limited, as current studies do not attempt to explain why audit companies should actually use BDA. Hence, an external audit is analysed from two process points of view – the audit process between the audit company and client, and the audit process between the audit company and regulatory bodies. In fact, BD only became accessible recently through powerful analytical tools, but there are no obvious institutional forces that use BD information or to implement BDA at the corporate level. The problematisation proposed in the paper is the result of a dialectical interrogation (Alvesson and Sandberg, 2011) of audit companies, business clients and regulatory bodies and the domain of literature targeted to challenge assumptions. The use of innovative analytical tools such as BDA may cause a tension among audit companies, business clients and regulators. This aspect arises because of interdependence in the auditing process.

The previous literature has stipulated several contingent factors (namely, company size, strategic orientation, modern technologies and regulatory environment) that can strengthen or pose challenges to the use of BDA in external auditing. We elaborate on different operating factors, as underlying theoretical assumptions, relevant to consider their different influences on different stages of financial auditing, including the actors in financial auditing. Based on these assumptions, we raise the following research question:

RQ1. What factors influence the motivation to use BDA in external auditing and how intensively are these factors expressed by audit companies, business clients and regulators?

The main contributions of this paper are the following. To the best of our knowledge, we are among the first to study the comprehensive state-of-the-art of BDA usage, the motivating factors and the potential outcomes for audit companies empirically. We explain how different institutional and company-related factors are expressed and influence the decision of whether to use BDA in external auditing. In particular, we focus on the phenomenon of BD in external auditing by observing the views of diverse participants (namely, audit companies, audit clients and audit regulators). Prior literature that examined audit analytics focussed mainly on single influencing factors without taking the entire contingency-based

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view into account. This study investigates the use of BDA in external auditing from the perspective of contingency theory. In addition, the study also makes a methodological contribution by introducing the use of the constructivist grounded theory approach within the context of a novel research question, for which the existing literature and data are generally lacking.

The paper is organised as follows. The literature review and the theoretical framework pertaining to BDA use in an external auditing are presented in Section 2 of this paper. Section 3 presents the methodology used, while Section 4 presents the results and the findings from the interviews. The discussion and conclusion are presented in Section 5 of this paper. Research limitations and further research directions are also provided.

2. Literature review and theoretical framework 2.1 Literature review of big data analytics in external auditing During the past few years, researchers have produced an impressive amount of general reviews, conceptual and research papers in an attempt to define the concept of BD and data analytic tools. The 3Vs (volume, variety and velocity) are the three best-known defining dimensions of BD. Laney introduced the 3Vs concept in a 2001 MetaGroup research publication, 3D data management: Controlling data volume, variety and velocity. In much of the business research, BD is seen as a new opportunity to enhance productivity, efficiency and innovativeness in companies (Sheng et al., 2017; Verma and Bhattacharyya, 2017; Connelly et al., 2016; Marshall et al., 2015; Vera-Baquero et al., 2015; Ajana, 2015).

Overall, the emergence of BD is both promising and challenging for social research, as well as for the accounting and auditing areas, which are regarded as intrinsically data- intensive. According to Warren et al. (2015), BD will have increasingly important implications for accounting ecosystems in all senses, even as new types of data become accessible, as will the inherent technological paradoxes of BD and corporate reporting (Al-Htaybat and Alberti-Alhtaybat, 2017; Bhimani and Wilcocks, 2014) and new performance indicators based on BD (Arnaboldi et al., 2017).

In general, auditors work with structured financial data; however, the volume and complexity of business companies require even more rapid and sophisticated information and analyses of unstructured or semi-structured non-financial BD from both internal and external sources. In external auditing, BD may be conceptualised as an additional information resource that has a direct effect on the understanding about the environment of the business client and the performance of an audit. Moreover, the inclusion of BD may contribute to the development and evolution of effective BDA tools and changes in the audit processes.

BDA is the process of inspecting, cleaning, transforming and modelling BD to discover and communicate useful information and patterns, suggest conclusions and support decision-making (Cao et al., 2015) by using “smart” algorithms (Davenport, 2014). According to Wang and Cuthbertson (2015), the potential of BDA to improve the practice of auditing is quite significant. A detailed literature review is commonly accepted as the beginning step in research and is important to indicate relevant research in a field. Accordingly, this research began with a literature review of the fields of BD, BDA and auditing. Research synthesis was selected as the method for the literature review with the aim of using the existing literature (Cooper et al., 2009; Dixon-Woods et al., 2005). The literature review outlines a few main directions and possible influences of BDA in the context of auditing. A major research stream in the field argues that use of BDA is useful and valuable for ensuring audit quality (Cao et al., 2015; Dubey and Gunasekaran, 2015; Brown-Liburd et al., 2015; Yoon et al., 2015;

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Vasarhelyi et al., 2015) by improving the efficiency and effectiveness of financial statement audits and by using BD as audit evidence.

The second stream of research focusses on additional competences that are necessary to ensure an effective process when using BDA (Dubey and Gunasekaran, 2015). The latest research by McKinney et al. (2017); Enget et al. (2017); Janvrin and Weidenmier Watson (2017) and Sledgianowski et al. (2017) emphasises the need to incorporate issues of BD and BDA into the accounting curriculum by acknowledging that these technologies are transforming the accounting profession (Enget et al., 2017; Fay and Negangard, 2017; Brown-Liburd et al., 2015; Zhang et al., 2015).

The third stream of research emphasises the role of changes in auditing standards. On one hand, Appelbaum et al. (2017) argued that the standards themselves do not forbid the use of BDA, but that the economics of external audits make analytics more difficult or nearly impossible to use. On the other hand, the key methodological problem is using BD as audit evidence (Brown-Liburd and Vasarhelyi, 2015). According to the standards, BD evidence should be considered as less reliable for audit evidence (Appelbaum, 2016). Hence, changes in the methodological audit approach, a change in standards to focus on data, the processes that generate them and the analysis thereof, changes in the nature of accounting records and auditing domains will add value and relevance to the accounting profession (KPMG, 2017; Krahel and Titera, 2015; Vasarhelyi et al., 2015; Gray and Debreceny, 2014). Moreover, updated standards may help to overcome the auditing profession’s apparent reluctance to engage with BDA (Gepp et al., 2018).

Finally, the fourth stream of research explains the technological challenges for companies of using BDA, with the focus on continuous auditing technology (Rikhardssona and Dull, 2016; Appelbaum et al., 2016; Sun et al., 2015; Chen et al., 2015; Alles, 2015; Chiu et al., 2014) and BD techniques (Gepp et al., 2018; Appelbaum et al., 2017). Moreover, according to the literature review, Appelbaum et al. (2018) classified the audit analytics used in the various audit stages. As external auditing is inseparable from the characteristics of business clients, Al-Htaybat and Alberti-Alhtaybat (2017) identified the inherent technological paradoxes of using BD in corporate reporting.

According to the literature review, it could be stated that the main streams of research focus on and disclose the outcomes and value of the use of BDA in external auditing, the aspects that have an influence on the efficient use of BDA and discuss the interaction between BD and traditional sources of data, as well as BD’s impact on audit judgement and behavioural research. It could also be stated that the external conditions and the environment have an influence on the use of BDA in external auditing. On the other hand, the research could be described as fragmented, disclosing different but limited aspects that motivate or challenge the use of BDA in external auditing and a complete list of motivation factors influencing the use of BDA in external auditing has not been researched.

2.2 The theoretical framework Contingency theory focusses on how elements must fit together to reach the desired configuration and the forms of fit, as proposed by Venkatraman (1989). In fact, the contingency-based approach that is used widely in management research (Chenhall, 2003; Chapman, 1997; Ittner and Larcker, 1997) could be also applied to explain audit companies’ intentions to adopt analytical tools at the corporate level.

Considering the complexity and dynamism of the audit process, the necessity of using BDA might be influenced by different, contingent, external and internal factors. Auditors require access to documents, systems, policies and procedures to manage an audit. They must remain compliant with accounting and auditing standards, government regulations

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and internal requests. Audit teams may begin the audit process with meetings during which they gain risk and control awareness. Auditors perform substantive procedures and test controls, and then draft reports that they submit to management and regulatory authorities (Davoren, 2016). Many contingency variables have been found to be relevant, including the environment – in particular, environmental uncertainty and market competition (Otley, 2016), technology (Otley, 1980, 2016; Chenhall, 2003), national culture (Ahmad and Schroeder, 2003; Flynn and Saladin, 2006; Otley, 2016), strategic context (Wickramasinghe and Alawattage, 2007; Sila, 2007) and company size and structure (Otley, 2016; Wickramasinghe and Alawattage, 2007). While it is possible that all these play an important role in the design of control systems (Brivot et al., 2017), this paper focusses particularly on the main contingent factors that have been subject to investigation, namely, the environment, technology, strategic context, size and structure. The contingency of natural culture has not been taken into consideration.

Environment, as a contingency factor, may constitute the market and its associated factors, such as prices, products, competition, government policies, etc., (Wickramasinghe and Alawattage, 2007). Environment (as a contingency) may constitute the audit market’s uncertainty and its associated factors, such as audit fees, competition and regulators’ policies, such as the attitudes of those setting the standards (Li et al., 2018). Looking at the BDA’s influence from the external auditing point of view, audit market regulators play a particularly important role in ensuring audit companies’ public quality aspects and enhancing the use of data analytic tools.

Technologies can be understood as the processes used by companies to convert inputs into outputs (Khandwalla, 1977). When a company fails to match its technology to its structure, it does not succeed as a sustained organisation (Wickramasinghe and Alawattage, 2007). In audit companies, technologies involve both knowledge and techniques. Moreover, technology, as a contingent factor, refers to the so-called hard IT-related aspects adopted by companies (Garengo and Bititci, 2007). Hence, BDA, as an IT tool, may have a direct impact on the audit process by influencing the audit phase of engagement. BDA may have an indirect impact on the audit planning phase, as audit strategies and audit plans are developed according to the data and information coming from the analysis of client’s environment. BDA, as an IT tool, may also have a direct influence on compliance and substantive testing and on evaluations and reports. Overall, the need to use BDA may depend on the requirements of the audit regulatory bodies and business clients and on internal technological capabilities, IT-related managerial activities, such as the internal investments in hardware and software, external consultants, etc., (Tarek et al., 2017).

Based on the notions of contingency theory, researchers have discussed how the fit between environment and strategy can influence organisational performance. Thompson (1967) argued that changes in technology and environmental factors resulted in differences in structures, strategies and decision processes. Henderson and Mitchell (1997), Spanos and Lioukas (2001) and Johnson and Scholes’ (2008) research results supported the argument that strategy was one of the effects that had influence as a significant determinant of performance. Pateli and Giaglis (2005) developed a structured approach to changing the business model of a company (including strategy perspective), which introduced a technological innovation by keeping the principles of the old (traditional) business logic and taking the effects incurred from the firm’s internal and external environment into account. With reference to contingency theory, it might be suggested that strategic orientation could have a significant influence in persuading audit companies to use BDA in auditing process in an attempt to find the fit among new trends in technology, the environment and organisational strategy. Referring to contingency theory, one might suggest that strategic

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orientation could influence audit companies to use BDA in auditing process significantly. A BD-based approach is inseparable from the corporate core strategy and aims. As suggested by Gepp et al. (2018), long-term orientation towards the use of BDA may outline future opportunities for auditing in the context of real-time information and on collaborative platforms and in peer-to-peer marketplaces.

Size has also been found to be an important contingent factor in understanding the nature of organisational structures and behaviour (Wickramasinghe and Alawattage, 2007; Otley, 2016). This implies that audit companies need to pay attention to the size of the audit company itself and to that of the business client when creating an audit strategy and plan. According to contingency theory, large companies have extensive specialisation, standardisation and formalisation, but these features are less important in small companies (Wickramasinghe and Alawattage, 2007); thus, it could be stated that small clients might not be able to provide all the necessary information as BD for further analysis and the application of BDA tools. Furthermore, small audit companies might not be able to use BDA for their business clients because of a lack of trained staff and limited technological capabilities.

Structure refers to the establishment of certain relationships among people with specified goals and tasks (Wickramasinghe and Alawattage, 2007). A poorly fitting structure is nothing else but a waste of resources and leads to the ultimate collapse of the business (Mintzberg, 1987; Otley, 2016). Accordingly, it could be stated that different methods, instruments, functions and processes cannot be designed without finding the best structure alignment. From a BDA point of view, it might be assumed that a suitable and organic structure would be able to support the implementation of innovative analytical tools in audit companies and vice versa.

The literature describes several factors that can strengthen or pose a challenge to the use of BDA in external auditing by integrating them in a theoretical framework (Figure 1).

The theoretical framework contains key participants involved in the auditing process (audit companies, business clients and regulators), the auditing process (where BDA might appear in different phases of an audit) and the contingent factors discussed above.

3. Research methodology Based on the literature review, we explored different contingent factors that may motivate the use of BD and BDA in external auditing theoretically. Qualitative research (Birkinshaw

Figure 1. Theoretical

framework for influencing factors to use BDA in external

auditing Process Influence

REGULATORY BODIES

AUDIT COMPANY

AUDIT PROCESS

BUSINESS CLIENT COMPANY

Contingent factors:

Environment

Technology

Company size

Strategic orientation

Structure

BD/A

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et al., 2011) adopted the constructivist grounded theory approach as described by Charmaz (2006, 2014) for two main reasons:

(1) BD and BDA are rarely researched phenomena within the field of auditing, and we were motivated to understand “the actual production of meanings and concepts used by social actors in real settings” (Gephart, 2004, p. 457).

(2) We aimed to develop theoretical insights into a process about which there is little extant theorising or empirical knowledge (Suddaby, 2006).

This research uses the analysis approach suggested by Corbin and Strauss (1990) to present rich and detailed descriptions, which allows the reader to make sufficient contextual judgements to transfer the interview findings to alternative settings.

We followed the main stages in grounded theory research for qualitative data analysis (McNabb, 2008; Corley, 2015), namely, collecting data, open coding, axial coding and developing theoretical insights.

3.1 Data collection The research on the motivation to use BDA in external audits was conducted using semi- structured interviews to allow for follow-up questions. Interview questions derived from theory are the tools used to obtain information that will help to answer the research question (Glesne, 2006).

The respondents were selected on the basis of two considerations, namely, the company and the respondent’s position. With regard to the first consideration, the companies that were selected as the three case studies were selected an audit network company dealing with DA, a business client company dealing with BD and a regulator. This selection was intended to obtain different perspectives on the same phenomenon. Table I shows the description of the sample.

For the second consideration, the respondents were selected according to their positions in the company. Hence, the respondents were auditors and BD analysists working and

Table I. Sample description

Cases/companies Duration of recorded interviews in minutes

Transcript pages

No. of interviews

Big 4 (1) 41.48 7 1 Big 4 (2) 43.04 8 1 Big 4 (3) 36.45 7 1 Big 4 (4) 42.32 7 1 International audit network 130.05 27 3 National audit network 47.37 11 1

Audit companies 340.71 67 8 Global financial services and IT company 105.53 24 5 Financial institution operating worldwide 90.41 20 2 National energy company 25.59 5 1

Business companies (clients) 221.53 49 8 Tax analytics 141.58 32 4 Audit controller 39.14 8 1

Regulators 180.72 40 5 Total 742.96 156 21

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dealing with the company’s data. The selection of the participants, as different stakeholders, was also intended to improve the validity and reliability of the study (Yin, 2003) (Table II).

During the face-to-face interviews, which lasted for 35 min on average, the participants were given a copy of the interview guide (questionnaire, see Appendix) to ensure sufficient coverage of the research aim and the optimal use of time.

Part 1 was related to the background information and general understanding of BD in the company and the motivating factors for using BDA. Part 2 was related to the practical aspects of using BDA in the audit process. The proposed questions included “why” and “how” information and the respondents were asked to discuss the reasons, motivations, creation, implementation and use processes of BDA, including values, its challenges and the possible changes for the auditing process.

The interviews were tape-recorded with prior permission from the participants after they signed an official agreement. Towards the end of each interview, time was allowed for open and informal discussions to extract information that participants might otherwise have been reluctant to provide during the formal interview sessions. Overall, the interviews lasted for 12 h and 38 min, resulting in 156 pages of transcripts. The interviews were conducted in Lithuanian or English. Data were collected and analysed in 2015-2017, except for the interview with the BDA analyst from the audit company, which was conducted and analysed in 2018.

3.2 The setting of the Lithuanian audit market We focus next on the description of the setting of the Lithuanian audit market as a critical factor for the analysis and interpretation of the data.

The Lithuanian audit market is relatively young and concentrated. In 2009, the National Audit Standards were abandoned, and only the International Standards on Auditing (ISA) have been applied since. According to the data from the Lithuanian Chamber of Auditors of 1 February 2017, 357 auditors and 170 audit companies have been certified, of which 141 out of 170 audit companies were listed as very small companies, 25 audit companies as small companies, 4 audit companies as medium companies and 1 audit company as large.

In 2015, Lithuanian audit companies conducted 4,217 audits in total, including 3,898 financial statement audits in Lithuania, 273 audits on consolidated financial statements in Lithuania, 44 audits on interim financial statements in Lithuania and 2 audits abroad (Lithuanian Chamber of Auditors Report, 2015). Among the clients of audit companies, the current companies include public interest entities and companies that are legally required to carry out audits but, in general, there are not many large clients.

The audit market in Lithuania is concentrated – the ten largest audit companies, according to the received revenue from audit activities in 2015, accounted for almost 70 per cent of the audit market. The average fee per audit performed in 2015 amounted to e414,304. The highest average fee for one audit was for the companies in the Big 4 – e869,850, which is four times higher than it was for audit companies with one or two auditors and three times

Table II. Position of

respondents

Cases/companies Auditors BD analytics

Senior Partner Field expert Head

Audit companies 4 3 � 1 Business client companies 1 � 3 4 Regulators 1 � � 4 Total 9 12

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higher than it was for audit companies with three or more auditors (Lithuanian Chamber of Auditors Report, 2015). However, given the fact that the audit companies for the Big 4 spend most of their time on audits, the difference in the average fee for the audit service is lower. Significant fluctuations in the fees for services between international and smaller national audit companies are typical of the Lithuanian audit market. This situation can also be explained by the fact that international networking audit companies are auditing the largest and, at the same time, the most complex business companies.

3.3 Coding and analyses Preliminary coding on the basis of the 21 interviews was developed first. After the transcription of all the interviews was completed, all the transcripts were analysed by both researchers separately via a systematic process of coding and categorisation intended to group the information from the transcripts into similar concepts or themes that emerged from the analysis. We then discussed the open coding of sentences or paragraphs within the transcripts to identify key concepts emerging from the data and to link them to what allowed agreeing on certain open codes. Table III illustrates the open coding of the interview transcripts.

During the process of our further discussions and analyses, open codes were assigned to broader categories, called second-order codes, which highlighted the relationships among the open codes (Lee, 1999). These second-order codes were then used to create broader categories – axial codes – to facilitate theoretical insights (Lee, 1999), such as current practices, company factors, institutional factors and outcomes. Table IV shows the axial codes and the descriptions thereof.

Coding process and codes, as a method of qualitative data analysis, (McNabb, 2008; Corley, 2015) allowed for the identification of key concepts emerging from the qualitative data – the transcripts. Meaningful results and findings are presented on the basis of axial codes, which indicated the main groups of motivating factors for and the circumstances in which to use BD and BDA in external auditing.

4. Results and findings After careful consideration of the second-order and axial codes, “Current Practices” was organised to include the open codes of experience, benefits, financial resources and increasing trend, which were identified as having similarities based on their currently existing features. During the data analysis process, the second-order and axial code “institutional factors” was organised using open codes such as regulation system, market structure and education. Three open codes, namely, strategic decisions, governance structure and size were identified as a second-order code strategy-related factors and three open codes, namely, information system (IS), competent teams and internal capabilities were identified as a second-order code, “resource-related factors”. These two second-order codes were then used to create a broader category, namely, the axial code “company factors”. There were three open codes, which were planning, management and reporting, which were integrated based on their properties in a second-order code, “internal control”. Five open codes were understanding the client’s company, audit planning, audit performance and conclusion and audit team and audit fee were identified as having similarities; thus, they were combined in a second-order code, “audit process”. In addition, the open codes audit quality and control of audit quality were combined in a second-order code, “quality”. These three second-order codes were identified as having similarities, in the main areas that are influenced by the use of BD/BDA in business and audit companies and were combined in an axial code, “outcomes”.

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O pe n co de s

D ef in it io n

A ud

it co m pa ni es

B us in es s

co m pa ni es

T ax

an d au di t re gu

la to rs

E xp

er ie nc e

In fo rm

at io n re la ti ng

to th e co nc ep t,

un de rs ta nd

in g an d du

ra ti on

of us in g B D /

B D A in a co m pa ny

D is cl os ed

a D is cl os ed

D is cl os ed

w it h an

or ie nt at io n to w ar ds

th e

fu tu re

St ra te gi c

de ci si on

In fo rm

at io n re la te d to

th e co rp or at e

st ra te gy

an d to p m an ag em

en t’s

at ti tu de /

co m m it m en t to us in g B D an d m od er n da ta

an al yt ic to ol s

H ig h im

po rt an ce

c H ig h im

po rt an ce

N ot

di sc lo se db

G ov er na nc e

st ru ct ur e

In fo rm

at io n re la te d to

to p m an ag em

en t -

go ve rn m en t, fo re ig n m an ag em

en t, na ti on al

sh ar eh ol de rs ,g lo ba ln

et w or ki ng

co m pa ny

H ig h im

po rt an ce

H ig h im

po rt an ce

N ot

di sc lo se d

IS In fo rm

at io n re la te d to

th e ov er al lc or po ra te

in fo rm

at io n sy st em

,i nc lu di ng

th e in te rn al

co nt ro ls ys te m ,fi

na nc ia la cc ou nt in g

pr og ra m m es

an d no n- fi na nc ia ld at a

pr og ra m m es ,d at ab as es

an d so ft w ar e us ed ,

le ve lo fc om

pu te ri sa ti on

of bu

si ne ss

pr oc es se s

D is cl os ed

D is cl os ed

D is cl os ed

B en efi ts

In fo rm

at io n re la te d to

th e be ne fi ts of B D A ,

in cl ud

in g ad va nt ag es

re ce iv ed ,t im

e ef fi ci en cy ,m

on ey

sa vi ng

s an d va lu e fo r

so ci et y by

pr ov id in g da ta

th at

ar e m or e

re lia bl e

D is cl os ed

D is cl os ed

D is cl os ed

F in an ci al

re so ur ce s

In fo rm

at io n re la te d to

co st s of cr ea ti ng

an d

im pl em

en ti ng

B D A ,i nc lu di ng

th e fi na nc ia l

re so ur ce s ne ed ed

D is cl os ed

D is cl os ed

D is cl os ed

w it h an

or ie nt at io n to w ar ds

th e

fu tu re

Si ze

In fo rm

at io n re la te d to

th e co nd

it io ns

ne ed ed

to co lle ct an d im

pl em

en t B D su ch

as th e

au di t co m pa ny

’s si ze

an d th e cl ie nt ’s si ze

H ig h im

po rt an ce ,a ud

it co m pa ny

’s si ze

H ig h im

po rt an ce ,

cl ie nt ’s si ze

N ot

di sc lo se d

(c on ti nu

ed )

Table III. Open codes derived

from different interview transcripts

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759

O pe n co de s

D ef in it io n

A ud

it co m pa ni es

B us in es s

co m pa ni es

T ax

an d au di t re gu

la to rs

P la nn

in g

In fo rm

at io n re la te d to th e de ve lo pm

en t of

pl an ni ng

an d fo re ca st in g pe rf or m an ce ,

pr oc es se s an d ac ti vi ti es

by us in g B D A

N ot

di sc lo se d

D is cl os ed

N ot

di sc lo se d

U nd

er st an di ng

th e cl ie nt ’s

co m pa ny

In fo rm

at io n re la te d to un

de rs ta nd

in g th e

cl ie nt ’s co m pa ny

an d it s en vi ro nm

en t,

be tt er

ev al ua ti on

of in he re nt

ri sk s an d th e

co nt ro lt he re of

D is cl os ed

N ot

di sc lo se d

N ot

di sc lo se d

A ud

it pl an ni ng

In fo rm

at io n re la te d to th e pl an ni ng

ac ti vi ti es ,p re pa ra ti on

of th e au di t pl an

an d

au di t pr og ra m m es

by us in g B D A

D is cl os ed

N ot

di sc lo se d

N ot

di sc lo se d

A ud

it pe rf or m an ce

an d

co nc lu si on

In fo rm

at io n re la te d to pe rf or m in g th e au di t,

th e ap pl ic at io n of

an al yt ic al pr oc ed ur es

an d

co nt ro lt es ts ,p ro vi di ng

th e au di to r’ s

op in io n, co nc lu si on ,c on ti nu

ou s au di ti ng

in st ea d of

on a sa m pl e ba si s

D is cl os ed

N ot

di sc lo se d

N ot

di sc lo se d

R ep or ti ng

In fo rm

at io n re la te d to pr ov id in g re su lt s

ab ou tt he

co m pa ny

in th e re po rt to

m an ag em

en t, ex te rn al st ak eh ol de rs ,a nd

th e

lik e

D is cl os ed ,a ud

it co nc lu si on

D is cl os ed ,r ep or t

to m an ag em

en t

an d so

on .

N ot

di sc lo se d

A ud

it qu

al it y

In fo rm

at io n re la te d to hi gh

er au di t qu

al it y

by em

pl oy in g B D A an d an al ys in g/ ch ec ki ng

10 0 pe r ce nt

of co rp or at e da ta

D is cl os ed

N ot

di sc lo se d

D is cl os ed

w it h an

or ie nt at io n to w ar ds

th e

fu tu re

C on tr ol of au di t

qu al it y

In fo rm

at io n re la te d to th e co nt ro lo fa ud

it qu

al it y in si de

th e au di t co m pa ny

,a s w el la s

ex te rn al pu

bl ic co nt ro l

D is cl os ed

N ot

di sc lo se d

D is cl os ed

w it h an

or ie nt at io n to w ar ds

th e

fu tu re

M an ag em

en t

In fo rm

at io n re la te d to im

pr ov em

en ts in

co nt ro la nd

de ci si on -m

ak in g fu nc ti on s by

us in g B D an d B D A

N ot

di sc lo se d

D is cl os ed

N ot

di sc lo se d

(c on ti nu

ed )

Table III.

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O pe n co de s

D ef in it io n

A ud

it co m pa ni es

B us in es s

co m pa ni es

T ax

an d au di t re gu

la to rs

A ud

it te am

In fo rm

at io n re la te d to

th e ef fe ct iv e

m an ag em

en t of

th e au di t te am

by us in g B D

an d B D A

D is cl os ed

N ot

di sc lo se d

N ot di sc lo se d

A ud

it fe e

In fo rm

at io n re la ti ng

to au di t pr ic es ,w

hi ch

co ul d be

m or e co m pe ti ti ve

an d ea si ly

m an ag ed

by us in g B D A in au di t co m pa ni es

D is cl os ed

N ot

di sc lo se d

N ot di sc lo se d

R eg ul at io n

sy st em

In fo rm

at io n re la te d to

th e na ti on al

re gu

la ti ve

bo di es

an d le ga la ct s in fl ue nc e on

th e us e of B D

D is cl os ed

as ho w m uc h

th e au di t re gu

la to r is

st ri ct an d re qu

ir es

ad di ti on al re lia bi lit y te st s,

an al yt ic al pr oc ed ur es ,e tc .

D is cl os ed ,

be ca us e di ff er en t

se ct or s ha ve

di ff er en t

re gu

la ti on s.

D is cl os ed ,b y di sc lo si ng

ho w m uc h na ti on al ta x

re gu

la to r re qu

ir es

on lin

e da ta ,l ev el of ac co un

ti ng

co m pu

te ri za ti on s

M ar ke t st ru ct ur e

In fo rm

at io n re la te d to

th e m ar ke t st ru ct ur e

(c om

pe ti ti on ,o lig

op ol y or

m on op ol y)

in th e

in du

st ry

(b ot h th e au di t co m pa ny

an d th e

cl ie nt ), th e in fl ue nc e of

co m pe ti to r’ s on

th e

de ci si on

to us e B D

D is cl os ed

D is cl os ed

N ot di sc lo se d

C om

pe te nt

te am

In fo rm

at io n re la te d to

th e co m pe te nt

au di t

te am

,e m pl oy ee s an d co m pe te nc e ne ed ed

to w or k an d us e/ an al ys e B D in a cl ie nt ’s

co m pa ny

,b ei ng

ab le to ap pl y B D A

H ig h im

po rt an ce

H ig h im

po rt an ce

N ot di sc lo se d

(c on ti nu

ed )

Table III.

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761

O pe n co de s

D ef in it io n

A ud

it co m pa ni es

B us in es s

co m pa ni es

T ax

an d au di t re gu

la to rs

In te rn al

ca pa bi lit ie s

In fo rm

at io n re la te d to

th e ac ti vi ti es ,

ca pa bi lit ie s an d in te rn al pr oc es se s ne ed ed

to pr ep ar e an d us e/ an al ys e B D in

a co m pa ny

su ch

as IT

w it h re ga rd

to in fr as tr uc tu re

D is cl os ed

D is cl os ed

N ot di sc lo se d

In cr ea si ng

tr en d

In fo rm

at io n re la te d to

th e in cr ea si ng

ro le

an d in fl ue nc e of

B D A fo r di ff er en t pu

rp os es

in co m pa ni es

gl ob al ly ,a s w el la s po lit ic al

de ci si on s

D is cl os ed

D is cl os ed

D is cl os ed

E du

ca ti on

In fo rm

at io n re la ti ng

to th e in cr ea si ng

ne ed

fo r co m pe te nt

em pl oy ee s w it h bu

si ne ss ,I T

an d m at he m at ic al co m pe te nc e gl ob al ly

D is cl os ed

H ig h im

po rt an ce

D is cl os ed

N o te s:

a D is cl os ed

m ea ns

th at

th e op en

co de

w as

m en ti on ed

an d di sc us se d du

ri ng

th e in te rv ie w ;b no t di sc lo se d m ea ns

th at

th e op en

co de

w as

no t m en ti on ed

or di sc us se d du

ri ng

th e in te rv ie w ;c hi gh

im po rt an ce

m ea ns

th at

th e op en

co de

w as

m en ti on ed

an d di sc us se d ve ry

st ro ng

ly du

ri ng

th e in te rv ie w

Table III.

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The results are presented from the different respondent groups’ points of view.

4.1 Audit companies Current practices. Experience. Large audit companies (international networks) develop and apply analytic tools that are similar to the BDA content-wise and complexity-wise. On average, audit companies have applied modern analytic tools for two to four years in the Baltic region. The auditors emphasise that the application of such innovative data analytics in the Baltic region is actually not the first choice (as compared to the USA, the UK, Germany or some Asian countries’ audit markets, for example). Big 4 auditors shared similar practices:

We are a smaller country; therefore, we usually do not even get on the first wave of implementation and application of innovative data analytics [Big 4 (2)].

However, some experts emphasised that companies had only taken the first steps in analysing BD context, referring to the demand for BD-based tools:

We are making first steps but the practical implementation is not for today yet. [. . .] We are developing applications, methodology. Some regions are more advanced, like North America, UK or Asia. We [Lithuania] are more like recipients of innovations [Big 4 (1)].

Other experts confirmed that audit companies had already made a progress in developing and applying analytical tools and had started to use the more advanced versions in Lithuania:

[. . .] as we implement audit analytical tools very purposefully, now we develop and implement a new and advanced analytical tool which was created and developed in UK office of our company (International audit network).

Increasing trend. Conducting a BDA-based audit was a challenge for the auditors themselves:

A possibility to audit all data is even now hardly perceivable for some auditors, as big companies’ audits are based on sampling methods. [. . .] With technologies, a huge amount of information in an external audit does not play such an important role [Big 4 (1)].

Implementing BD technology-based tools establishes the conditions for changing the thinking and attitudes of both auditors and business clients. In the case of a client being a

Table IV. Axial codes derived from second-order

codes

Second-order codes Description Axial codes

Current practices Arguments and descriptions related to the current situation, experience and motivation to use BD/BDA in companies

Current practices

Strategy-related company factors

Different levels of the intensity of factors influencing and motivating the level of BD/BDA use from the internal environment of companies

Company factors

Resource-related company factors External factors Factors regulating, influencing and motivating the

level of BD/BDA use from the external environment of companies

Institutional factors

Internal control The main areas that are influenced by the use of BD/ BDA in business and audit companies

Outcomes Audit process Quality

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small business company, audit companies even have to show the value of using BDA in the audit process:

We indicate the main advantages of using BDA for our small or new clients [such as] using BDA we will be able to indicate the systemic problems and variances in your [business] company data, increase the quality of audit report and to find the fraud events (International audit network).

Benefits. The largest audit companies (international networks) assessed the BD and BDA unambiguously positively and treated them as a competitive advantage in the audit market in the long term. Enabling auditing technologies will probably foster the competitiveness of all audit companies in the oligopoly audit market:

[. . .] currently, analytics tools are used considerably more, as also our company itself has invested a lot into these new analytics tools. We think that Big 4 (2) Eagle [analytical tool] is a competitive advantage. [. . .] Unambiguously positive, as it helps to focus on riskier fields. It helps to identify the fields that might look suspicious [Big 4 (2)].

Financial resources. Small audit companies usually only apply very simple analytical tools, mainly because of lack of knowledge, poor financial resources and the cost of investment. The current practices of small- and medium-sized national audit companies and audit companies that belong to international networks strongly diverge with regard to applying modern technologies:

[. . .] by investing in analytical tools we always measure costs [. . .] as it’s really very expensive [Big 4 (3)].

[. . .] notwithstanding huge financial recourses needed, all investments are very useful. We operate in a very competitive business environment where we have to make our processes more efficient in order to compete with a lower price. [. . .] Technologies help to work efficiently and save costs (International audit network).

The largest companies were usually more experienced in the use of data analytics and were already gaining advantages because of the economy of scale.

Institutional factors. Regulation system. Institutional factors affect audit companies themselves through the requirements for the performance of more efficient audits (application of control tests and detailed procedures) and quality control. Hence, the importance of ISA is evident. Audit companies also have an impact via the client, such as additional legislative requirements for the quality of accounting and clients’ accounting IS.

If audited clients are small, their accounting IS will naturally be distinguished by a smaller quantity of structured and non-structured data. The size of the client is also associated with the fee for the audit. In fact, no companies in the Baltic region are big globally; therefore, strong competition in terms of price is prevalent.

[. . .] clients are too small, because if we talk about analytical tools, we encounter limitations, one of which is the size of the client, and then this is closely associated also with price limitations [Big 4 (2)].

Although Krahel and Titera (2015) and Vasarhelyi et al. (2015) argued that the application of BDA would also bring about changes in ISA, audit experts did not think that auditing standards and methods should necessarily change for the successful employment of these analytic tools. Current legal acts are sufficient to conduct a BD-based audit:

Audit standards that have these requirements already require all companies to conduct an audit in the most effective way using the analytics tools. This is simply another tool to achieve these goals in a faster and better way. But this does not change the way that an audit team should

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work, what the work principles are, how we plan, organise, review and what the quality control is [Big 4 (2)].

Standards are nevertheless a set of principles, not rules. As regards an understanding of the company, control environment and all processes, it is already laid down in the standards that you have to understand all processes, irrespective of whether you will subsequently validate the control or not, and whether you are going to trust them [Big 4 (4)].

Thus, auditing standards are focussed on the audit’s purpose and general principles, not on the techniques/analytics that are used to perform it.

Market structure. It is important to note that the market orientation of client’s company may also determine the use of BD technologies and the market’s size:

Lithuania is not a big market size. If companies are just orientated to the Lithuanian market, it is not large enough. They do not require substantial systems that would work with crazy amounts of data. [. . .] On the other hand, more and more service centres are being established in Lithuania [banks, sharing centres (explanation added)]. . . . The driver would be management established in a foreign country [Big 4 (1)].

Education. One of the most important aspects when attempting to apply BDA successfully is having competent employees. Education plays a critical role in providing audit specialists with interdisciplinary competence:

[. . .] even the universities themselves should focus more on IT by preparing specialists. It is a big challenge for us. We can see IT specialists who do not care anything about accounting, and graduated accountants who have poor skills in IT. Unfortunately, we do not see the merger. [. . .] So we are already looking for people with integrated skills [Big 4 (1)].

By developing and implementing BDA we saw the transformation in the audit profession and it’s not enough to be only an accountant or auditor but we also need to have IT competences. . . (International audit network).

As requirements for external auditor’s professional competence are set by public authorities, there may be inevitable changes in the long run.

Company factors. Strategy-related factors. The use of modern analytics in large network audit companies, including international audit networks, is based on the global strategy of IT innovations:

No large companies stand still, and, talking about our company, this is a really global network investing in these technologies. [. . .] there exists a common global strategy and a vision of the company, when we all [units in different regions] will start using a particular analytics tool [Big 4 (2)].

To be a part of a global business and to belong to international networks, plays an important role in using BD in external auditing and the client’s performance:

Most of the businesses, especially IT businesses, are foreign owned. They are driven by a parent company. [. . .] So, the ownership structure is an important factor [Big 4 (1)].

The motivation of audit companies to invest in analytics tools relies primarily on the size of the company and its strategic orientation. International audit networks and large audit companies have greater possibilities of creating or acquiring such powerful analytics tools:

We do not develop such analytics tools in the Lithuanian unit. We use what has been globally created in the company [Big 4 (2)].

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Notably, large audit companies (such as the Big 4) see BD as an increasingly essential part of their assurance practice (Alles and Gray, 2016). It is important to note that the size of the company determines the use of BD technologies not only due to the size of the audit company itself but also based on the size of the audit client. The business client’s size was one the most prevalent factors mentioned by the experts who were surveyed. If business companies are small, their data are naturally not defined by the characteristics of 3Vs. This theoretical presumption is consistent with the answers from regulators and auditors:

Multinational companies are big drivers. Facebook and Google are driving the auditors’ profession as well. We have to find ways to audit them and Big Data Analytics may help [Big 4 (1)].

The size of a company can have an influence on the use of BD from the point of view of the amount of data and probably in the future, even medium-sized companies will be able to apply and use it (Global financial services and IT company).

Resource-related factors. Audit companies have to be prepared in terms of their internal processes and capabilities to use BDA. They mainly need resources related to the preparation of IS and integrated teams of employees for the successful application of BD and BDA. As IT competencies are becoming extremely important, audit companies currently resolve this issue by having an IT person in the company or outsourcing IT competence:

[. . .] We know what we want but we do not have IT competencies, so it’s better to take from software companies. We are talking about major software companies like Microsoft, Oracle, SAP. Obviously, the cooperation with these companies will help to develop the tools [Big 4 (1)].

We have an IT person who works with different groups and consult about IT questions [Big 4 (4)].

Outcomes. Audit process. For audit companies, BD may help to provide a better understanding of the business client’s environment. All the experts interviewed claimed that the application of these analytic tools made the audit process more effective, particularly during the phase of understanding the client’s business environment and internal control and during the phase of performing substantive procedures:

The reasons to perform an audit are more focused on risks, conduct it in a better, quality manner, adapt to progress [Big 4 (2)].

Effectiveness is at the first place as competition by prices is essential. We are working totally in electronic space [Big 4 (3)].

[. . .] our analytics show a certain tendency and variances in, for example, your [business client] company and you [business client] are able to analyse detailed data where and why it [variances] were found (International audit network).

An audit company, as a profit-seeking organisation, seeks to conduct an audit in the most efficient way from the client’s and the quality point of view. Thus, analytic tools are one of the instruments that reduce the screening risk, and thus, minimise the likelihood of incorrect conclusions. Essential attention in the BD-based audit is paid to the verification of data reliability. This is irrespective of whether the client’s information would be received in the traditional way or via BDA; the issue of data reliability is always a priority:

The first work upon receipt of any information for auditing purposes is a test of its reliability. [. . .] The main question during the verification of quality control is whether a data reliability test has been made [Big 4 (4)].

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A set of BDA tools may also be beneficial for the drawing up of audit reports. During the auditing process, co-operation is maintained with the company’s management, and different reports may be drawn up (such as the auditor’s conclusion, the auditor’s report and letters to the management). The final auditor’s conclusion is standardised, with clear criteria for the information provided. Therefore, the BDA may have an indirect effect through the type of auditor’s opinion. In other words, when applying more effective analytic tools, the assumption is that the auditor had a better perception of the client’s environment, focussed accordingly on the riskiest fields and decreased the likelihood of having provided an incorrect opinion.

However, the possibility of using analytic technologies in other audit reports is much greater and may create more added value for the client, only without the compulsory compliance function:

A letter to the management where we share observations on internal control systems, their shortcomings, provide recommendations that do not necessarily impede an audit, but we simply share our insights. Thus, here we see very great possibilities that namely in this place [assessment of the internal control system] the use of BDA would be of great help because [. . .] it would be an analytics in different cross-sections [Big 4 (4)].

Quality. An audit market regulator and quality control may also be very important factors fostering BDA in external audits. State regulation of the audit market is gradually growing stronger across the world (SOX, Audit directives in the European Union, etc.). Thus, there is noticeable pressure from individual audit quality regulators to apply more advanced analytic tools in the audit process, which would translate into a better quality of risk-based audits:

The need to apply advanced analytics tools arose not only from the audit teams themselves but also from the quality control system. [. . .] An American regulator treats quality control systems of audit companies extremely strictly and its audits are substantial. This is also the second strict- wise and attitude-wise regulator in the Netherlands [Big 4 (4)].

Institutional quality control factors of external audit companies via the audit market regulators in different markets produced a different effect:

Maybe, if we were only a national company and with this regulator, then we would probably have less boost, but in fact, our global methodology team is in America and they work in the strongest professional regulation environment. Thus, all approaches, all innovations, novelties and pressure on the maintenance of audit quality come from over there [Big 4 (4)].

This is an approach of the global body that regulates all this audit policy [Big 4 (1)].

Internal control. When public interest companies are audited, the use of these tools becomes an essential element for assessing the control system and managing the audit risk:

[. . .] one of our tools makes a very good report from the accountancy data, which makes it clear whether a person has made any entries he cannot make and whether the duties are separated, whether one and the same person does not do both, debit and credit, as this entails an additional risk [Big 4 (2)].

Thus, there is a need for tools that would enable conducting an audit in an effective way, that would enable to conduct it in a faster and better way, as quality may not be compromised either, and the audit standards themselves, as I have mentioned, become not looser, but more stringent [Big 4 (2)].

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Estimation of a client’s internal control system is one of the compulsory analytical procedures for an auditor. The more complex and global the client company is, the more multidimensional and complex is the internal control system of the client.

Issues related to the audit company. According to the research results, all second-order codes were disclosed in the case of an audit company, and this could be explained as all contingent factors influenced the use of BD and BDA, but the influence occurred at different levels and degrees of importance. Our research results suggest that the use of BD and BDA depends strongly on the audit corporate strategy and governance structure and it confirms the research results of Verma and Bhattacharyya (2017). Moreover, it is likely that BDA enables auditors to act on structured and unstructured information. In line with Bhimani and Wilcocks (2014), we claim that the traditionally presumed sequential and linear links among corporate strategy, governance structure and IS design are no longer in play. This is the reason that we also suggest that, when applying the BDA, additional attention should be paid to the company’s IS as one of the elements of the internal control system. To a great extent, the IS depends on whether the auditor will be inclined to trust the data or to apply more detailed audit procedures. The issue of the reliability of the IS is crucial. Our study also suggests that the development of new analytical competence and even a new structure of audit teams with regard to BDA is necessary. In line with Al-Htaybat and Alhtaybat’s (2017) views on BD in corporate reporting, building such teams (that include analytics) will require audit companies to determine whether they want to outsource their analytics or whether they want to create their own platforms and systems.

4.2 Business clients Current practices. Experience and increasing trend. The use of BD and DA tools in business companies (including international companies) is already the practice, with more than five years of history and a trend towards expanded use in the future:

Banking sector was especially in a very good situation concerning BD because of regulation to collect and save historical data. Analytics was just the next natural step forward (Financial institution operating worldwide).

The implementation of BD technology-based tools establishes the conditions for changing the thinking and attitudes of business companies:

BD is a global trend, everybody [business companies] can see and understand the value of using BD and this understanding has become comprehensible to owners of businesses (Financial institution operating worldwide).

Benefits. Business companies see BD and DA as an essential process in today’s business environment and use them for a different purposes and benefits in areas such as cost saving, planning processes, forecasting of the client’s behaviour and sales:

[. . .] there are a lot of areas where labour work could be changed with analytic [. . .] to predict the client behaviour is one the possible usage of BD and another could be after-sale service (Financial institution operating worldwide).

[. . .] each business unit has its own data analytics in different levels, such as risks, fraud, pricing, transaction analytics, accounting analytics, marketing analytics (Global financial services and IT company)

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Financial resources. Business companies see the implementation and use of BDA as a process that is expensive and which requires a financial investment. The influence of this concept is that it is mainly large companies that are able to integrate and use BDA widely:

[. . .] from practical point of view, there are a small number of companies in Lithuania, which could be able to use it [BDA]. It is understandable that you [Business Company] cannot expect results from BD in six months, it is quite a long period and company has to understand this, you have to invest and work (Financial institution operating worldwide).

Institutional factors. Regulation system. The sector regulator (such as the financial sector) and the audit regulator play an important roles in the use of BDA:

[. . .] financial institutions historically must accumulate and save a different kind of data to manage risk issues (Financial institution operating worldwide).

The audit regulator should encourage audit companies to be more advanced technologically, to provide fresh news about novel audit analytics. Such topics are not even included in annual training for auditors (National audit network company).

Market structure. The main motivating factors for using BD in business companies are strong competition and long-term relationships with customers. Many interviewees emphasised:

The main motivating factor is to create a sustainable relationship with customers (Financial institution operating worldwide).

Competition is very strong in the market and a company needs to be better than its competitors, so BD helps to ensure this aspect (Global financial services and IT company).

Education. These global trends influence the need for employees with broader interdisciplinary competence, including knowledge about business, information technology and mathematics. Business companies confirmed the importance and lack of competent employees globally:

[. . .] companies are lacking competent employees and looking for them, . . . it is very difficult to find employees who would be ready to work in BDA area and even with experience (Financial institution operating worldwide).

[.] there is an increasing level of interest from universities and study programmes but we still are not able to find a fully prepared specialist able to work with BD. Mostly cases we invest in competences improvement of those employees who have IT, mathematical or analytical skills (Global financial services and IT company).

Company factors. Strategy-related factors. From the client’s perspective, the use of BDA and DA rely heavily on the corporate strategy and top management’s support:

The main objective of all financial institutions operating worldwide group is BD integration into business processes with purposes to minimise costs and to discover new possibilities for business development (Financial institution operating worldwide).

[. . .] as changes are very fast in the market, decisions made have to be grounded by BD and according to strategic choice of all company groups in all Europe and this is not limited to the Lithuanian market (Global financial services and IT company).

Resource-related factors. Large companies will be more financially able to invest in new technologies and capabilities (infrastructure and competent employees) and to invest in the

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future value that could be created by BD. In addition, it could be stated that companies in developing countries might be able to integrate BDA more quickly:

[. . .] because banking companies already started to develop business with more recent information technologies and systems that allow to integrate BDA and to be more flexible (Financial institution operating worldwide).

The main challenges for the application of BD in external auditing are the quality and comparability of data and qualified BD analysts because companies need to have employees who can find patterns in data and translate them into useful business information:

BD quality is very important . . . [. . .] We have two groups of BD, first is more raw data and using it is allowed but risks need to be evaluated, second is fully prepared BD (Financial institution operating worldwide).

The main internal challenge of using BD is HR and analytical skills integrating IT and business skills. [. . .] Also, one more challenge is IT system and necessary investments into these systems, consultancies (Financial institution operating worldwide).

Outcomes. Internal control. Business companies understand BD as the possible or the main source of data to manage the business and use BDA tools for internal management, decision- making, planning and reporting purposes:

We use BD in weekly control process by evaluating changes, influences and making decisions. [. . .] Our expectations are that BD application will grow in the area of business process development in the future. (Global financial services and IT company).

Issues related to business clients. The research results showed that not all second-order codes were indicated in the case of business companies. In particular, the difference from audit companies was in the area of outcomes. This could be explained by the fact that business companies mainly use BD information for internal purposes to manage business processes and make decisions. The research results confirmed that the possibility of applying BD and BDA depended on the size of the business company and its strategic orientation. Public interest companies, companies with international headquarters in different countries, may encounter actual BD in their activities. The motivation to use BDA and other DA is also important regardless of whether the client is a state-owned company or a private company. The main motivation to use BD and BDA tools is related to strong competition.

4.3 Regulator Current practices. Increasing trend. Regulatory bodies understand the importance of BD/ BDA tools and see them as an increasing trend for all sectors, business companies, audit companies and as a future direction in the case of regulatory bodies as these still do not have experience in this area:

[. . .] our performance is very closely related with BD technologies. [. . .] because of looking at the future all large business companies will need to provide all information to regulating governmental institutions in electronic form starting from 2017 (Tax analytics).

Benefits. Regulatory bodies confirmed the usefulness of BD and BDA for large business companies, governmental organisations and at the state level from the perspectives of time and quality:

It [analytics tool] shows directions where mistakes, irregularities might be (Tax analytics).

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[. . .] this was the initiative from business companies. As The State Tax Inspectorate disrupted companies with questions about different kind of data for two weeks, so it [BDA] is a benefit for both parts (State Tax Inspectorate).

Financial resources. According to the experts interviewed, there is a need for e-audits and for a funding project to support the implementation of e-audits, which will help to develop and use BD-based analytic tools for different purposes:

There should be some actions taken and start a project implementation in a three-year period (State Tax Inspectorate).

Cost benefit aspect is very important and we calculate the employees’ time saved for different processes from regulator and business company sides, this helps to evaluate money saved in five years, ten years or fifteen years (Tax analytics).

Institutional factors. Regulation system. Regulatory bodies play an important role at various levels, such as in the tax environment, and in terms of sector regulation and audit regulation. In the global regulation practice, it is still possible to notice different variants, ranging from the compulsory universal certification of accounting systems to plans to certify accounting information provided by companies:

Accounting systems are certified at the state level. [. . .] the same way an accountant must have a certificate, an IS must be certified. [. . .] The future will unambiguously have to be this way, as the number of errors due to low-quality information will make the process very painful (State Tax Inspectorate).

According to the experts interviewed, one of the factors motivating the use of BDA will definitely be the fostering of e-audits at the state level:

It is very important to make a breakthrough in the analytics, an audit breakthrough, a quality leap so that we could audit banks not in the way we audited Snoras or U°kio bank. Positive audit reports were issued and in a half-year, these banks became insolvent (explanation added) (State Tax Inspectorate).

Education. Regulatory bodies indicated the future need to integrate educational institutions in this increasing trend towards BD and BDA:

We plan to integrate researchers in the development of analytical tools. [. . .] there is still a lack of knowledge and wisdom about the same understanding. Education would be able to play a key role in this process (State Tax Inspectorate).

Outcomes. Audit process. Obviously, audit regulatory bodies do not participate directly in the audit process, but their key function is the public oversight of quality control. Responsible regulatory bodies evaluate how audit evidence is documented and the compliance with ISA and the completeness of substantial audit procedures and control tests, including audit evidence gathered via BD:

If transactions and accounting records are maintained in a ecentralizat way, a large company may simply face the fact that data are wrong. Overall, the system seems to be correct, but decentralization may show that, with time, these data have changed. This may be a big surprise for such large companies [Regulator (2)].

Quality. As Lithuania abandoned national auditing standards in 2009, the Lithuanian audit regulator does not have sufficient authority to change the implementation of the standards. It is not the standard setter and has more of an advisory role:

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So, the biggest driver comes from international accounting settlers. [. . .] For the more advanced regulators in Europe and other territories it is the tendency. As auditors, we move to a more sophisticated IT environment of auditing the clients. The regulators have to understand how the auditors audit. It might be even the beginning of the process [Big 4 (1)].

Internal control. Essentially, ISA lays down the provisions for assessing the client’s internal control system, the IS and controls regarding the IS:

There are many different types of accounting software and auditors are familiar with some and not familiar with others (Tax analytics).

The possibility of checking data in real time results in the likelihood that an audit may create a higher value for the client. This would not only be an auditing process based on historical data:

The reaction to on-going processes and the speed are very important. Now auditors make a sampling and audit the data that is half-a-year, one-year old. [. . .] Thus, this reaction in current time and controlling such data is very important to be able to react in a fast and expeditious manner (Tax analytics).

Overall, auditors and regulators presented a conservative attitude towards incorporating BD in decision-making for auditing aims. They admitted that BD played an important role, but that the change will still be taking place in the future.

Regulator-related issues. The research results showed that second-order codes were disclosed differently in the case of regulators. Company-related factors were not disclosed because regulatory bodies are not treated in the same way as are companies. Regulatory bodies still do not have current practice in the use of BD and BDA tools and the implementation, thereof, is planned for the future. Institutional factors were disclosed because regulatory bodies play an important role at various levels, such as in the tax environment, in sector regulation and audit public oversight. Outcomes were mainly disclosed with regard to quality, and this could be explained by the fact that regulatory bodies are responsible for the public oversight of quality control, continuous learning and education about innovative audit techniques, including BD and BDA. According to the research results, regulatory bodies could be seen as followers of business and audit companies in the use of BD and BDA tools.

5. Discussion and conclusion 5.1 Comparison and discussion of the results Based on the qualitative research, we identified four key results. By disclosing a comprehensive view of current practices (one), we identified two groups of motivating factors [company-related (two) and institutional (three)] for the use of BDA from an external auditing point of view, which may lead to the desired outcomes (four) for the audit companies.

Our findings showed that the current practice differed for business companies, audit companies and regulators. Business companies had used BDA tools for more than five years and saw this as an increasing trend in the future because of strong competition, and these tools were used to understand the customers’ behaviour, to manage risk and for internal management purposes. Hence, the use of BD and BDA was focussed mainly on the internal management needs and market/sales expectations. Audit companies had approximately three years of experience in the use of BDA tools. The use of modern analytics in large network audit companies was usually based on the global strategy of IT innovations and with the main purpose of ensuring the quality of the audit process and to issue a relevant

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auditor’s report. Regulatory bodies still did not have experience in the use of BD and BDA tools and assume this would be an increasing trend in the future.

Our study, therefore, emphasises the importance of interdependence among audit companies, business clients and regulators to enable the use of BD and BDA. Given this, business companies might be the drivers of the use of BD and BDA tools and audit companies might adopt these innovations because of high competition in the audit market. Moreover, the current practices of business companies provided and even created suitable conditions for external audit companies to use all the data (financial and non-financial, structured and unstructured) for audit purposes. This motivates external audit companies to use BDA as, firstly, business companies are able to provide BD and, secondly, the use of BDA for audit purposes allows the achievement of the desired outcomes, such as the efficiency and effectiveness of the audit, higher audit quality and minimising audit risk and having a better understanding of the client’s business environment and internal control.

Specifically, the study has provided evidence of the importance of motivating factors and circumstances that influence the use of BDA in external auditing process (Table V).

The results from the interviews showed that contingent factors may act both on the company level (such as size, strategic orientation, structure and technology) and on the institutional/external level (the audit market environment). What is more important is that the influence of different contingent factors was not the same. Company-related factors had a direct influence on the use of BDA in different phases of the audit, depending primarily on the audit company’s data-driven strategy and the business client’s size. Moreover, the audit market environment (the national regulator’s policy or the competition level) could be assumed to be an indirect contingency factor because audit companies have to evaluate environmental uncertainty and adapt to it.

Our findings showed that a company factor such as size influenced the use of BDA for both audit companies and clients. These results are in contrast to the study by Li et al. (2018), who found that corporate size did not influence the adoption of audit analytics in internal auditing significantly. One reason could be that, if the audit client is extremely large, the client will be confronted with plenty of semi-structured and unstructured massive data that cannot be analysed using traditional audit software and analytics. On the other hand, only a large audit company may have sufficient resources and substantial tools to be able to audit such a company. This is also consistent with previous research stating that large companies have extensive specialisation, standardisation and formalisation (Wickramasinghe and Alawattage, 2007), while small companies will not be able to provide all the necessary information as BD. In addition, a small audit company would encounter challenges when attempting to use BDA because of the lack of trained staff and technological capability (Alles, 2015).

With regard to the strategic orientation, our results are consistent with those of Li et al. (2018) and Verma and Bhattacharyya’s (2017) findings that the major reason for the non- adoption of BDA was that companies did not realise the strategic value of BDA, and they were not ready to make changes due to technological, organisational and environmental difficulties. Therefore, we conclude that a company’s strategic orientation and structure may also be important influential factors concerning the use of BDA. On the other hand, competent employees, internal capabilities and IS are resource-related audit company factors because they are derived from the size of the company and from the strategic orientation/attitude towards the adoption of technology. Moreover, audit companies attempt to find a trade-off between the extent of information demanded by the environment and the company’s available resources.

Audit market regulations and education may have a particular impact on an audit company’s decision regarding the design of an audit strategy, such as how to apply modern

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auditing tools, how to ensure audit quality and what the topics for auditors’ training should be. Our results are in line with Tarek et al. (2017) and Li et al. (2018), confirming that the attitudes of audit regulatory bodies and legislative regulation followed by sector regulation and market structure are critical for fostering the use of BDA.

Specifically, we provide the following theoretical and practical implications: � Our paper expands on Li et al.’s (2018) study on understanding the use of audit

analytics for internal auditors due to several reasons. We aimed to investigate practices pertaining to the use of BDA, in particular, (not all audit analytics in general) in external auditing. Although external and internal auditors have similarities in terms of carrying out audit procedures, the role of external auditors of decreasing information asymmetry for capital markets is distinct and unique when compared to internal auditors. Furthermore, external auditors must be independent and do not participate in an

Table V. The highlights of motivating factors and circumstances

Motivating factors Motivating circumstances

Company-related Size Audit company’s size Audit companies with large international audit networks have more

capacity Business client’s size Large business clients may have more BD Strategic orientation Data-driven strategy Data-driven strategy of the audit company Client’s selected business model

Usually business to consumer (B2C) experience more BD

Relationship between the audit company and business clients

In the case of a long-term contract, additional costs for initial harmonisation and the correlation of different data sources

Structure Audit company’s structure

Global audit networks

Business client’s ownership structure

In the case of a business company, public procurement has to be organised for a state-owned company and, in most cases, only for one year

Sector Specific sectors in which BD is inherent, such as financial intermediation or telecommunications

Technology Digitalisation of the business process

High degree of IT usage by audit companies and business clients

Accounting software used by business clients

Technological level of accounting software. Usually BDA are not well adapted for working with national accounting software, as there are particular difficulties such as the extraction of data in the necessary format, and initial processing to receive such data

Professionals with BDA experience

Member of audit team/ outsourced professional/internal training

Institutional Audit market environment Audit market competition High audit market competition. Strong price competition is prevalent in the

Baltic region National audit regulator’s policy

Help/support to acquire BDA or AA, provide training about analytics in auditing

Education Higher education institutions to provide professionals with interdisciplinary data analytic skills

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audited company’s activity constantly, as internal auditors do. This means that external auditors have to gain understanding of the client’s environment and performance in a very short time; hence, BDA might be a useful tool. While Li et al. (2018) emphasised that only internal auditors should have more demand for the use of audit analytics to be efficient and effective, the high prices and competition in the external audit market are very important factors motivating the need to be more effective and implementing more analytics. From the interviews, we may summarise that audit clients seek: to negotiate for better pricing because of high competition in the audit market; and to get more value and insights about corporate risks and performance. This leads to a trend whereby external auditors are likely to focus on the procedures not just to satisfy regulatory requirements, but to provide more value for the audit client; hence, BDA may be one of the solutions.

� The results of our research also indicated diverse motivation in the use of BDA depending on the business client’s size. Large business companies usually acted as innovators in applying BD and audit companies were followers. In the case of the client being a small business company, audit companies played a proactive role and even had to demonstrate the value of using BDA in the audit process.

� The result that the national audit regulator was lagging behind in implementing audit analytics was particularly problematic from a BD and BDA perspective. In most cases, the national audit regulator played more of an advisory role, and was currently lagging behind with regard to BD and BDA. From this perspective, the study also outlined the dilemma of quality. Audit regulators need to ensure public oversight of quality control and provide training for auditors. However, regulators lacked knowledge about innovative BD-based techniques.

5.2 Conclusion and further research directions The results of our research revealed audit companies’ intentions to use BDA and to expand their understanding of the use of BD and BDA tools in external audits by emphasising the close relationship of audit companies and different; yet, related groups such as business clients and regulatory bodies.

We wish to emphasise the need to implement BD and BDA-based audit practices for audit companies as a way to improve audit quality and to foster the efficiency of audits, which may result in a competitive audit fee. This research also offers insights into helping to customise their audit strategies.

In addition, our research results indicated that large business clients were the main drivers of the use of BD and BDA in external auditing, as the current practices of large business companies allow and create suitable conditions for audit companies to use BD (financial and non-financial, structured and unstructured) for audit purposes. Large business clients usually act as innovators in applying BD and BDA, while audit companies are followers. However, a different direction in this relationship could be indicated in the case of small business clients, as audit companies play a proactive role in this scenario and even have to show the additional value of using BDA. Moreover, based on the interviews, we suggest that large networking audit companies may gain long-term effectivity, which is important regardless of whether the client is new or established. The other outcome is to ensure a higher audit quality resulting in better value for the shareholders, the management and society.

For business clients and regulators, this study might help them to understand the advantages and challenges of institutional and company factors concerning BDA use.

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5.3 Contribution Our study aims to contribute to the literature on auditing in the following ways. Firstly, it adds to the small body of research by offering an empirical investigation the state-of-the-art of BDA usage and motivating factors in external auditing. While prior studies (Li et al., 2018) have focussed on internal auditing, this paper addresses BDA and external auditors in particular. In addition, Verma and Bhattacharyya (2017) found that complexity and perceived costs were the inhibitors that prevented the adoption of BDA in business companies, while our research results indicated that the factors mentioned above were not critical. Secondly, our study examines the phenomenon of BD and BDA in the context of auditing. It is important to note that BD has specific characteristics compared to other types of data and opportunities to use BD within BDA is of increasing importance for audit companies, which to the authors’ knowledge, is absolutely new. Structured (around 10 per cent) and unstructured (around 90 per cent) of data that are large in size cannot be analysed using traditional software and database systems (Al-Htaybat and Alberti-Alhtaybat, 2017). Thirdly, the paper presents a contingency-based theoretical framework as a model explaining how different motivating factors may influence the use of BDA. The research also makes a methodological contribution by using the approach of constructivist grounded theory for the analysis of qualitative data.

5.4 Limitations The conclusions of this study are based on interview data collected from 21 participants. Future studies may investigate the issues addressed in this study further by using different research sites and a broader range of data. Although the theoretical method is highly transparent, it requires further testing to verify the mechanism on which it is based.

Furthermore, by keeping BDA as a tool, the use of which depends on the size of the company, our sample yielded all interviews in particularly large companies. There is a limited number of large companies in Lithuania that are open to co-operation. To test our research question more broadly, we suggest including additional audit and business companies in future research.

5.5 Future research There are a number of future research opportunities, as this is still a novel research area in the field of auditing and accounting. Having chosen a qualitative approach prevents a broader data collection method, which may provide different views. It would be worthwhile to carry out further empirical analyses of BDA either currently or potentially in use through a detailed case study or a quantitative survey to gather a broader range of insights. Our interview results provided mixed results with regard to the need to change auditing standards and auditing procedures when using BD. Thus, a deeper discussion of possible changes to audit procedures could be another relevant area for future research. As we identified that the national audit regulator is currently lagging behind in the area of audit analytics, it would be relevant to investigate the quality dilemma from the perspective of public oversight of quality control and the impact of international and national audit regulators on BDA and audit analytics in general. Furthermore, it is worth conducting research on changes in external auditors’ profession through education in analytical interdisciplinary skills. At the same time, future research could expand the scope of BD and BDA research for the internal purposes of companies, such as internal auditing, control processes and performance measurement. The interviewed experts confirmed the importance of BD usage for the management of pricing, fraud detection, complaints and risk assessment. Performance measurement integrated with BD would be able to support planning, control and decision-making processes by providing meaningful and appropriate information.

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References Ahmad, S. and Schroeder, R. (2003), “The impact of human resource management practices on

operational performance: recognizing country and industry differences”, Journal of Operations Management, Vol. 21 No. 1, pp. 19-43.

Ajana, B. (2015), “Augmented borders: big data and the ethics of immigration control”, Journal of Information, Communication and Ethics in Society, Vol. 13 No. 1, pp. 58-78.

Al-Htaybat, K. and Alberti-Alhtaybat, L. (2017), “Big data and corporate reporting: impacts and paradoxes”, Accounting, Auditing and Accountability Journal, Vol. 30 No. 4, pp. 850-873.

Alles, M.G. (2015), “Drivers of the use and facilitators and obstacles of the evolution of big data by the audit profession”, Accounting Horizons, Vol. 29 No. 2, pp. 439-449.

Alles, M.G. and Gray, G.L. (2016), “Incorporating big data in audits: identifying inhibitors and a research agenda to address those inhibitors”, International Journal of Accounting Information Systems, Vol. 22, pp. 44-59.

Alvesson, M. and Sandberg, J. (2011), “Generating research questions through problematization”, Academy of Management Review, Vol. 36 No. 2, pp. 247-271.

Appelbaum, D. (2016), “Securing big data provenance for auditors: the big data provenance black box as reliable evidence”, Journal of Emerging Technologies in Accounting, Vol. 13 No. 1, pp. 17-36.

Appelbaum, D., Kogan, A. and Vasarhelyi, A.M. (2017), “Big data and analytics in the modern audit engagement: research needs”, Auditing: A Journal of Practice and Theory, Vol. 36 No. 4, pp. 1-27.

Appelbaum, D., Kogan, A. and Vasarhelyi, A.M. (2018), “Analytical procedures in external auditing: a comprehensive literature survey and framework for external audit analytics”, Journal of Accounting Literature, Vol. 40, pp. 83-101.

Appelbaum, D., Kozlowski, S., Vasarhelyi, M.A. and White, J. (2016), “Designing CA/CM to fit not-for- profit organizations”, Managerial Auditing Journal, Vol. 31 No. 1, pp. 87-110.

Arnaboldi, M., Busco, C. and Cuganesan, S. (2017), “Accounting, accountability, social media and big data: revolution or hype?”, Accounting, Auditing and Accountability Journal, Vol. 30 No. 4, pp. 762-776.

Bhimani, A. and Wilcocks, L. (2014), “Digitisation, ‘big data’ and the transformation of accounting information”, Accounting and Business Research, Vol. 44 No. 4, pp. 469-490.

Birkinshaw, J., Brannen, M.Y. and Tung, R.L. (2011), “Reclaiming a place for qualitative methods in international business research”, Journal of International Business Studies, Vol. 42 No. 5, pp. 573-581.

Brivot, M., Gendron, Y. and Guénin, H. (2017), “Reinventing organizational control: meaning contest surrounding reputational risk controllability in the social media arena”, Accounting, Auditing and Accountability Journal, Vol. 30 No. 4, pp. 795-820.

Brown-Liburd, H. and Vasarhelyi, M.A. (2015), “Big data and audit evidence”, Journal of Emerging Technologies in Accounting, Vol. 12 No. 1, pp. 1-16.

Brown-Liburd, H., Issa, H. and Lombardi, D. (2015), “Behavioral implications of big data’s impact on audit judgment and decision making and future research directions”, Accounting Horizons, Vol. 29 No. 2, pp. 451-468.

Cao, M., Chychyla, R. and Stewart, T. (2015), “Big data analytics in financial statement audits”, Accounting Horizons, Vol. 29 No. 2, pp. 423-429.

Chapman, C.S. (1997), “Reflections on a contingent view of accounting”, Accounting, Organizations and Society, Vol. 22 No. 2, pp. 189-205.

Charmaz, K. (2006), Constructing Grounded Theory, 1st ed., London, Sage. Charmaz, K. (2014), Constructing Grounded Theory, 2nd ed., London, Sage. Chen, K., Li, X. and Wang, H. (2015), “On the model design of integrated intelligent big data analytics

systems”, Industrial Management and Data Systems, Vol. 115 No. 9, pp. 1666-1682.

Big data and big data analytics

777

Chenhall, R.H. (2003), “Management control systems design within its organizational context: findings from contingency based research and directions for the future”, Accounting, Organizations and Society, Vol. 28 Nos 2/3, pp. 127-168.

Chiu, V., Liu, Q. and Vasarhelyi, M.A. (2014), “The development and intellectual structure of continuous auditing research”, Journal of Accounting Literature, Vol. 33 Nos 1/2, pp. 37-57.

Cooper, H., Hedges, L.V. and Valentine, J.C. (2009), The Handbook of Research Synthesis and Meta- Analysis, Russell Sage Foundation, New York, NY.

Corbin, J.M. and Strauss, A. (1990), “Grounded theory research: procedures, canons and evaluative criteria”, Qualitative Sociology, Vol. 13 No. 1, pp. 3-21.

Corley, K.G. (2015), “A commentary on ‘what grounded theory is. . .’: engaging a phenomenon from the perspective of those living it”, Organizational Research Methods, Vol. 18 No. 4, pp. 600-605.

Davenport, T.H. (2014), “How strategists use ‘big data’ to support internal business decisions”, Discovery and Production”, Strategy and Leadership, Vol. 42 No. 4.

Davoren, J. (2016), “Contingency theory in auditing”, Chron, available at: http://smallbusiness.chron. com/contingency-theory-auditing-46110.html

Dixon-Woods, M., Agarwal, S., Jones, D., Young, B. and Sutton, A. (2005), “Synthesising qualitative and quantitative evidence: a review of possible methods”, Journal of Health Services Research and Policy, Vol. 10 No. 1, pp. 45-53.

Dubey, R. and Gunasekaran, A. (2015), “Education and training for successful career in big data and business analytics”, Industrial and Commercial Training, Vol. 47 No. 4, pp. 174-181.

Earley, C.E. (2015), “Data analytics in auditing: opportunities and challenges”, Business Horizons, Vol. 58 No. 5, pp. 493-500.

Enget, K., Saucedo, G.D. and Wright, N.S. (2017), “Mystery, Inc.: a big data case”, Journal of Accounting Education, Vol. 38, pp. 9-22.

Fay, R. and Negangard, E.M. (2017), “Manual journal entry testing: data analytics and the risk of fraud”, Journal of Accounting Education, Vol. 38, pp. 37-49.

Flynn, B. and Saladin, B. (2006), “Relevance of Baldrige constructs in an international context: a study of national culture”, Journal of Operations Management, Vol. 24 No. 5, pp. 583-603.

Garengo, P. and Bititci, U. (2007), “Towards a contingency approach to performance measurement: an empirical study in Scottish SMEs”, International Journal of Operations and Production Management, Vol. 27 No. 8, pp. 802-825.

Gephart, R.P. (2004), “Qualitative research and the academy of management journal”, Academy of Management Journal, Vol. 47 No. 4, pp. 454-462.

Gepp, A., Linnenluecke, M.K., O’Neill, T.J. and Smith, T. (2018), “Big data techniques in auditing research and practice: current trends and future opportunities”, Journal of Accounting Literature, Vol. 40, pp. 102-115.

Glesne, C. (2006), Becoming Qualitative Researchers: An Introduction, 3rd ed., Pearson, New York, NY. Gray, G.L. and Debreceny, R.S. (2014), “A taxonomy to guide research on the application of data mining

to fraud detection in financial statement audits”, International Journal of Accounting Information Systems, Vol. 15 No. 4, pp. 357-380.

Henderson, R. and Mitchell, W. (1997), “The interactions of organizational and competitive influences on strategy and performance”, Strategic Management Journal, Vol. 18, pp. 5-14.

Ittner, C.D. and Larcker, D.F. (1997), “Quality strategy, strategic control systems, and organizational performance”, Accounting, Organizations and Society, Vol. 22 Nos 3/4, pp. 293-314.

Janvrin, D.J. and Weidenmier Watson, M. (2017), “Big data’: a new twist to accounting”, Journal of Accounting Education, Vol. 38, pp. 3-8.

Johnson, G. and Scholes, K. (2008), Exploring Corporate Strategy, 8th ed., Prentice Hall, Upper Saddle River, NJ.

MAJ 34,7

778

Khandwalla, P.N. (1977), The Design of Organizations, Harcourt, Brace, Jovanovich, New York, NY. KPMG (2017), “Audit 2025, the future is now”, Forbes insights, March, available at: https://assets.kpmg.

com/content/dam/kpmg/us/pdf/2017/03/us-audit-2025-final-report.pdf Krahel, J.P. and Titera, W.R. (2015), “Consequences of big data and formalization on accounting and

auditing standards”, Accounting Horizons, Vol. 29 No. 2, pp. 409-422. Lee, T.W. (1999), Using Qualitative Research in Research, Sage, Thousand Oaks, CA. Li, H., Dai, J., Gershberg, T. and Vasarhelyi, M.A. (2018), “Understanding usage and value of audit

analytics for internal auditors: an organizational approach”, International Journal of Accounting Information Systems, Vol. 28, pp. 59-76.

Lithuanian Chamber of Auditors Report (2015), Lithuanian Chamber of Auditors Report, Lithuanian Chamber of Auditors, Vilnius, 26 July 2016.

McKinney, E., Jr, Yoos, C.J. and Snead, K. (2017), “The need for ‘skeptical’ accountants in the era of big data”, Journal of Accounting Education, Vol. 38, pp. 63-80.

McNabb, D.E. (2008), Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches, Sharpe, Armonk, New York, NY.

Marshall, A., Mueck, S. and Shockley, R. (2015), “How leading organizations use big data and analytics to innovate”, Strategy and Leadership, Vol. 43 No. 5.

Mintzberg, H. (1987), “The strategy concept I: five Ps for strategy”, California Management Review, Vol. 30 No. 1, pp. 11-25.

Otley, D.T. (1980), “The contingency theory of management accounting: achievement and prognosis”, Accounting, Organizations and Society, Vol. 5 No. 4, pp. 413-428.

Otley, D.T. (2016), “The contingency theory of management accounting and control: 1980-2014”, Management Accounting Research, Vol. 31, pp. 45-62.

Pateli, A.G. and Giaglis, G.M. (2005), “Technology innovation-induced business model change: a contingency approach”, Journal of Organizational Change Management, Vol. 18 No. 2, pp. 167-183.

Rikhardssona, P. and Dull, R. (2016), “An exploratory study of the adoption, application and impacts of continuous auditing technologies in small businesses”, International Journal of Accounting Information Systems, Vol. 20, pp. 26-37.

Sheng, J., Amankwah-Amoah, J. and Wang, X. (2017), “A multidisciplinary perspective of big data in management research”, International Journal of Production Economics, Vol. 197, pp. 97-112.

Sila, I. (2007), “Examining the effects of contextual factors on TQM and performance through the lens of organizational theories: an empirical study”, Journal of Operations Management, Vol. 25 No. 1, pp. 83-109.

Sledgianowski, D., Gomaa, M. and Tan, C. (2017), “Toward integration of big data, technology and information systems competencies into the accounting curriculum”, Journal of Accounting Education, Vol. 38, pp. 81-93.

Spanos, Y.E. and Lioukas, S. (2001), “An examination into the causal logic of rent generation: contrasting Porter’s competitive strategy framework and the resource-based perspective”, Strategic Management Journal, Vol. 22 No. 10, pp. 907-934.

Suddaby, R. (2006), “From the editors: what grounded theory is not”, Academy of Management Journal, Vol. 49 No. 4, pp. 633-642.

Sun, T., Alles, M. and Vasarhelyi, M.A. (2015), “Adopting continuous auditing: a cross-sectional comparison between China and the United States”, Managerial Auditing Journal, Vol. 30 No. 2, pp. 176 -204.

Tarek, M., Mohamed, E.K.A., Hussain, M.M. and Basuony, M.A.K. (2017), “The implication of information technology on the audit profession in developing country: extent of use and

Big data and big data analytics

779

perceived importance”, International Journal of Accounting and Information Management, Vol. 25 No. 2, pp. 237-255.

Thompson, J.D. (1967), Organisations in Action, McGraw-Hill, New York, NY. Vasarhelyi, M.A., Kogan, A. and Tuttle, B.M. (2015), “Big data in accounting: an overview”, Accounting

Horizons, Vol. 29 No. 2, pp. 381-396. Venkatraman, N. (1989), “The concept of fit in strategy research: toward verbal and statistical

correspondence”, Academy of Management Review, Vol. 14 No. 3, pp. 423-444. Vera-Baquero, A., Palacios, R.C., Stantchev, V. and Molloy, O. (2015), “Leveraging big-data for business

process analytics”, The Learning Organization, Vol. 22 No. 4. Verma, S. and Bhattacharyya, S.S. (2017), “Perceived strategic value based adoption of big data

analytics in emerging economy: a qualitative approach for Indian firms”, Journal of Enterprise Information Management, Vol. 30 No. 3.

Wang, T. and Cuthbertson, R. (2015), “Eight issues on audit data analytics we would like researched”, Journal of Information Systems, Vol. 29 No. 1, pp. 155-162.

Warren, J.D., Jr, Moffitt, K.C. and Byrnes, P. (2015), “How big data will change accounting?”, Accounting Horizons, Vol. 29 No. 2, pp. 431-438.

Wickramasinghe, D. and Alawattage, C. (2007), Management Accounting Change: Approaches and Perspectives, Routledge, London.

Yin, R.K. (2003), Case Study Research: Design and Methods, Sage, Thousand Oaks, CA. Yoon, K., Hoogduin, L. and Zhang, L. (2015), “Big data as complementary audit evidence”, Accounting

Horizons, Vol. 29 No. 2, pp. 431-438. Zhang, J., Yang, X. and Appelbaum, D. (2015), “Toward effective big data analysis in continuous

auditing”, Accounting Horizons, Vol. 29 No. 2, pp. 469-476.

Further reading Collings, S. (2011), “Surviving the audit inspector”, Accountancy, Vol. 147 No. 1412, pp. 68-69. Griffin, P.A. and Wright, A.M. (2015), “Commentaries on big data’s importance for accounting and

auditing”, Accounting Horizons, Vol. 29 No. 2, pp. 377-379. Rayburn, J.M. and Rayburn, L.G. (1991), “Contingency theory and the impact of new accounting

technology in uncertain hospital environments”, Accounting Auditing and Accountability Journal, Vol. 4 No. 2, pp. 55-75.

Republic of Lithuania Law on Financial Statements of Entities (2001), Republic of Lithuania Law on Financial Statements of Entities No. IX-575 as last amended on 14 May 2015 – No. XII-1696, Vilnius.

Simons, R. (2000), Performance Measurement and Control Systems for Implementing Strategy, Prentice Hall, Upper Saddle River, NJ.

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Appendix

Table AI. Interview guide

Questions to ensure maintenance Enquiries

Why do you (not) use Big Data Analytics? What is the motivation behind this decision?

What is the corporate strategy regarding the use of modern data analytics (Big 4)? How long has the company been using Big Data Analytics and other data analytic tools? What are the benefits/costs of Big Data Analytics?

What internal factors drive your company to use Big Data Analytics? What are internal factors influencing the use of Big Data Analytics?

What is the influence of the company’s size and the client’s size? What is the influence on the auditing process in terms of: Understanding the client and its environment, Audit planning, Sampling methods, Other auditing techniques, Auditing conclusion/reports?

What external factors drive your company to use Big Data Analytics? Has external pressure influenced the use of Big Data Analytics?

What is the influence of the national regulative body? What is the influence of the audit market’s size/competitors? Which external groups - competitors, clients and other regulative authorities have the biggest influence on the use of Big Data Analytics?

How is (or how could) Big Data Analytics be implemented in the auditing process? Who is involved in the process of Big Data Analytics?

Who prepares the Big Data? Who analyses the Big Data? How do Big Data Analytics help to integrate non-traditional sources of data with financial data?

How did your company create and implement Big Data Analytics? Who created the Big Data Analytics tools?

Do you use the services of IT consultancy companies? Do you use your own capabilities?

Which changes do you expect in auditing? Do you think Big Data Analytics is a growing trend? Do you expect any changes in the regulatory framework?

What changes could there be concerning auditors’ competence? Could there be a change from sample-based auditing to continuous auditing? What changes could there be for professional and educational institutions?

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About the authors Dr. Lina Dagilien_e is a Professor at School of Economics and Business in Kaunas University of Technology, Lithuania. Her research interests include sustainability accounting and reporting, financial accounting and auditing issues. She is also interested in interdisciplinary projects due to accounting sciences and is a developer of interdisciplinary graduate study programme “Business Big Data”. Lina Dagilien_e is the corresponding author and can be contacted at: [email protected]

Dr. Lina Klovien_e is an Associate Professor at School of Economics and Business in Kaunas University of Technology, Lithuania. She joined Kaunas University of Technology in 2012, before she worked in a business company (Scandinavian capital bank in Lithuania) for nearly 8 years. Her main research interests include the intersection of performance measurement/management control systems and innovations.

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