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1. Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective

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Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective

Management Decision

September 12, 2019

Copyright 2019 Emerald Publishing Limited All Rights Reserved

Section: Pg. 1857-1882; Vol. 57; No. 8; ISSN: 0025-1747

Length: 9734 words

Byline: Shivam Gupta, Xiaoyan Qian, Bharat Bhushan, Zongwei Luo.

Body

ABSTRACT

Purpose

Technological developments have made it possible for organizations to use enterprise resource planning (ERP) services without indulging in heavy investments like IT infrastructure, trained manpower for implementation and maintenance and updating the systems regularly to maintain business competitiveness. Plug and play model offered by cloud ERP has led to a constant creation of large data sets which are structured, semi-structured and unstructured by nature. Thus, there has been a need to analyze such complex data sets and the purpose of this paper is to focus on how cloud ERP and big data predictive analytics (BDPA) will impact the performance of a firm.

Design/methodology/approach

A dynamic capability view (DCV) theory-based model was developed and the authors have collected data by using an online questionnaire from India. Thereafter, the authors have analyzed it by employing structural equation modeling.

Findings

SEM analysis of 231 respondents showcases that the use of DCV theory to define the relationships of cloud ERP and BDPA has been the right move. Out of the 13 hypotheses empirically tested, only 7 hypotheses were supported by the data.

Research limitations/implications

The study showcases cross-sectional data from India. It would be interesting for this study to see if the country-level differences would influence these relationships between cloud ERP and financial performance, BDPA and financial performance and cloud ERP and BDPA.

Originality/value

This study empirically tests the relationship of cloud ERP and BDPA through a model based on DCV theory.

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Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective

1.Introduction

Enterprise resource planning (ERP) systems provide firms extensive facilities and capabilities to share and transfer data and processes of organizations inside and outside the enterprise into a single system and single database (Peng and Nunes, 2013). Sharing data between firm’s departments or firms across the supply chain helps in many aspects (inter alia in the form of decision support) and aims to achieve the objectives of better firm performance (FP). Elmonem et al. (2016) commented ERP as a category of business management software system that aims to integrate all functional units, typically a suite of integrated applications in a cooperative way. It facilitates organizations to collect, record, manage and interpret data from these business activities. The fact is that, ERP has so far been widely implemented by different organizations with different sizes in many sectors and in many countries to seek competitive advantages in the market.

With time going by, ERP has revolutionized, and continuous up gradation has taken place to strengthen its functionality of resources sharing and integration capabilities of functional units. Scholars increasingly tout internet- enabled ERP systems as an important perspective for the performance of a firm or even firms across the supply chain. With the advent of cloud computing technologies in the late 2000s, Peng and Gala (2014) highlight that there exists an increasing trend for firms to move their ERP-based applications and database into the cloud. According to Salleh et al. (2012), cloud ERP as a concept has been a boon to the FP, inter alia for small and medium enterprises over large companies since they could conform to the infrastructure requirements of the on-premise ERP solution as well as the high cost. Cloud computing brings firms the very model that enables ubiquitous access to share data and resources to achieve coherence, get the application up and run faster, often over the internet.

Exploring the cloud enterprise resource planning (CERP) system enabled with predictivity ability may help to resolve high uncertainties and gain more competitive advantages than other competitors in the dynamically changing market. According to Duan et al. (2013), CERP systems give the enterprise a chance to access the advanced computing resources that are available over the cloud, and even support the firms to manage their business functions to achieve higher productivity. Beheshti (2006) also argued that CERP systems are capable to manage and handle the large volume of operations and information that is created daily within the firm. Besides the potential benefits for operational performance (OP), one of the main drivers from a CERP would be the technical and operational integrations of functional processes to harmonize the data and information stream based on product lifecycle (PLC) (material flow of goods or services) (Qian et al., 2016). In view of Beatty and Williams (2006), this would happen through integrating the values across PLC within a seamless business process streamlining, which could potentially precede the firm’s market competitiveness and responsiveness in the rapidly changing environment. Therefore, implementing ERP systems on the cloud platform is showcased for resolving the limitations of ERP systems and provides better scalability, reliability, availability and cost efficiency that are all the very components for a higher FP.

On the other hand, big data predictive analytics (BDPA) would be the next big frontier of innovation, efficiency, productivity and competitiveness of an organization (Srivastava, 2014; Waller and Fawcett, 2013). Reaping the benefits of big data, CERP systems enhanced with e-commerce capabilities and its ability of integration and sharing resources and capabilities, collaboration with corporate alliance (suppliers, partners and even customer portals), and tracking of incoming resources and outgoing final products extends the visibility and control from inside and outside with big data analytics (McAfee et al., 2012). In doing so, upstream and downstream firms in the supply chain could provide reporting capabilities to management via sharing information (i.e. data) needed to support strategic decision-making that is of huge importance for long-term benefits of firms in the supply chain. Organizations in various sizes from various areas are jumping on the bandwagon of big data and predictive analytics (BDPA) due to the data sharing and interactive nature of CERP systems for firm competitive advantages.

By definition, BDPA is a decision-making field which consists of big data; use various statistical tools and techniques and machine learning, deep learning artificial intelligence and even data mining to derive potential insights from huge data sets, improving the market performance (MP) and OP of a firm (Gupta et al., 2018a). Analyzing big data using predictive techniques would be a necessity for decision support, although big data alone is ubiquitous (Prescott, 2014; Duan and Xiong, 2015). Extracting potential insights from large data sets via analytics techniques are an all-encompassing term among the senior executives to make decisions for their enterprises.

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Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective

McAfee et al. (2012) highlight that firms of various sizes need to take the data-driven decision-making into strategy practices via which the top management decision-makers execute any plans based on data instead of gut feeling. Apart from data, Waller and Fawcett (2013) argue that the appropriate managerial skills (MS) and technical skills (TS) play a non-substitutable role in the success of predictive analytics initiatives. Matthias et al. (2017) include that the application and exploitation of BDPA would create first-mover competitive benefits for sustainable improvement for a firm. Consistent with the rich research around (surrounding) the extraction of valuable data and potential insights from a large database, we argue that BDPA may be a well-accepted technology for decision-making on the performance of a firm. For instance, organizations may react differently to the same CERP systems on the FP due to the differences in the ability of BDPA capability.

The existing research and studies on CERP and BDPA, respectively are very rich, but the vast majority of them focus merely on its own capabilities and effects. In contrast, knowledge on the joint role of CERP and BDPA capabilities for the performance of a firm is scant. Very few scholars investigated on the specific relationships of CERP and BDPA, CERP and FP, BDPA and FP. Such a void leaves a significant gap between firm’s resources and capabilities and its performance. To address this gap, we propose a theory-driven and empirically proven model that aligns CERP and BDPA and could explain the impacts of CERP and BDPA on FP drawing on dynamic capability view (DCV) model (Teece et al., 1997). More specifically, our current research objectives of this paper are to address two main issues as follows: develop a theoretical framework based on DCV theory to understand the role of cloud-based ERP services and BDPA on the performance of a firm; andempirically validate this theoretical framework by employing structural equation modeling (SEM).

This paper is organized as follows. In Section 2, we begin with a brief review of the relevant literature pertaining to CERP and BDPA. In Section 3, we describe our theoretical model based on DCV and hypotheses development. In Section 4, we would outline the research methodology and data analytics for the empirical validation of this theoretical framework by employing SEM. Section 5 consists of our discussion related to our analysis results, including theoretical contribution, managerial implications and limitations and further directions. In Section 6, we conclude with our discussion results.

2.Theoretical background

2.1Dynamic capability view

In the strategic management area, the distinct mechanism of capability-building resource-based view (RBV) is for understanding how we could create our economic competitiveness for a firm. According to Teece et al. (1997), in order to locate a firm’s position in the market via its resources, the RBV theory was the first concept among the management strategy literature. Wernerfelt (1984) supplemented the conception that resources could be studied and examined as the source of a firm with competitive advantages. RBV tends to support firms to create more economic advantages than their competitors by being more effective at defining internal and outside resources and deploying strategic resources, and subsequently building capabilities (Makadok, 2001; Kim et al., 2015). However, Kraaijenbrink et al. (2010) asserted that RBV was not able to address how firms utilize resources and capabilities in a dynamic market as RBV is primarily a static theory that helps the firms to maintain a competitive advantage by employing resources at their disposal. Therefore, the DCV as an extension of RBV (Wang et al., 2016) was proposed which promotes innovation (Lawson and Samson, 2001).

Going back to the origins of capabilities, it began with the request that static nature of RBV could not fully showcase how the resources of a firm developed and integrated in a rapidly changing market (Teece et al., 1997; Winter et al., 2003; Smith et al., 2014). Teece et al. (1997) defined the dynamic capabilities as the abilities to deploy, integrate, build and reconfigure the competencies inside and outside a firm to resolve the dynamically changing market. Lawson and Samson (2001) highlighted dynamic capabilities support enterprises to improve the profits by managing firm’s capabilities (efficiency, quality, velocity, flexibility, etc.) in a dynamic and uncertain environment. Given the need of rapid responsiveness of a firm’s resources stock to increasingly dynamically changing environments, Vogel and Güttel (2013) indicated that dynamic capabilities would be of inherent strategic relevance to a firm, to keep pace with competitive dynamics. As such, DCV theory would be regarded as the distinct process that allows resources and focuses on learning and change capabilities to relate them to FP.

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Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective

Given that one of the main objectives of this study is to identify various resources that will enable firms to create CERP and BPDA capabilities, which in turn may lead to superior MP and OP, the choice of DCV as a theoretical framework for this study seems appropriate.

In this paper, DCV is used to conceptualize BDPA and CERP as capabilities that have an impact on FP. Resources like data (D), MS) and TS support BDPA (capability), which impacts on MP and OP. In a similar manner, dynamic capabilities grouped into organizational factors (OF), people factors (PF) and technological factors (TF) constitute CERP (capability) that has an impact on MP and OP of an organization (Figure 1).

2.2Cloud-based ERP (CERP)

Most recently, one of the most popular trends is cloud computing that has a huge potential to reshape the way ERP systems operate. Armbrust et al. (2010) defined cloud computing as both the applications and systems software in the information centers. Cloud computing would be a key computing paradigm for the next ten years, anticipated by Smith et al. (2014). Literature has highlighted the role of ERP in the cloud computing enables many applications of web services via the internet (Chen et al., 2015).

It is not surprising that toward developing a fully functional CERP system has been a center of focus by both firms and researchers, given that CERP is built to provide firms with huge benefits of flexibility, improved accessibility, scalability, lower upfront and operating costs, rapid implementation, cost transparency, sales automation, higher security standards and free trials (Gupta and Misra, 2016). The power of CERP usually refers to the performance of an organization, including MP and OP. These benefits derived from CERP system not only saves the cost of operation when improving the effective productivity but also supports the changed business size into the dynamically changing market when satisfying the firm’s needs of new markets shares more quickly than competitors.

2.3Toward the conceptualization of CERP capability

The key competitive edge for a firm now is its capability to define, standardize and adapt its processes and information with supplier, partner, customers based on PLC in a dynamically changing environment (Chen et al., 2015). Cloud computing allows firms convenient and on-demand access to share a pool of configurable resources. Therefore, cloud computing supports to improve the operational efficiency and help firms to achieve dynamic capabilities (Battleson et al., 2016).

In this paper, we argue that CERP could be conceptualized as a capability with the aid of cloud computing. This paper would identify the dynamic capabilities that explain how firms could effectively respond to market dynamism by developing CERP capability. CERP capability is created by the intrinsic factors combination of OF, PF and TF (Gupta and Misra, 2016) for they are under the control of cloud user. In terms of the determinants of CERP capability, OF, PF and TF are briefly discussed below.

2.3.1Organizational factors (OF)

The research proposed that the success of CERP capability building was affected by OF (Law and Ngai, 2007) and suggests that OF act as the major determinants of FP (Hansen and Wernerfelt, 1989). Determining the appropriate organizational factor or the construct of performance involves communicating the top management strategy to front- line subordinates and organizational structures and systems to people simultaneously. The OF are considered here to capture the multi-dimensional phenomena – strategic goals and objective, implementation strategy, business process re-engineering, organizational resistance, project management and budget and even communication.

The important organizational factor in CERP is the alignment between CERP and organizational objectives. The fit between strategic goals and objectives and CERP systems is brutal critical to achieving FP (Law and Ngai, 2007). In terms of the implementation strategy, it would be set before the implementation of CERP for it guides the future function and capability for our CERP capability. The project budget has a positive impact on better outcome for the enterprise as it is in sync with the implementation strategy (Hasibuan and Dantes, 2012; Somers and Nelson, 2004). Business process re-engineering is the extent to which organizations have to change and re-engineer the

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Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective

existing business process to fit the coming new system in line with the requirements of customization or new markets development (Saleh et al., 2013). What is asserted by prior research is that more the abilities an organization holds for business process re-engineering to change, more powerful could be its ERP systems (Grover et al., 1995). Consequent with re-engineering business process in CERP solutions, organizational resistance may be also be a hindrance factor to operate smoothly (Utzig et al., 2013). Project management is indispensable role for the CERP capability, which refers to set the vision and directions for business process and harness the cooperation and potentials of employees to exploit the technological capabilities of CERP (Al-Mashari et al., 2003; Esteves and Pastor-Collado, 2001), as well as efficient execution of implementation strategy (Gupta and Misra, 2016). Communication has to be in sync with the project management for better understanding the roles between superior–subordinate and among employees with good working relationships across the project. Most projects failed due to its communication, either a lack of thereof or miscommunication. The need for effective communication is permanent and would affect all the factors listed above. All these measurement constructs would be in the collaboration as OF in executing CERP system.

2.3.2People factors

People are the biggest potential power of a firm. According to Lalsing et al. (2012), PF have been proven to be the most critical success factors for development systems. From the employee’s perspective, there would be many measure constructs identified to cover the PF. The key components which are being created as PF are user involvement, vendor selection, project team, top management support, training of user and trust on vendor (Gupta and Misra, 2016).

During building CERP capability, there would be a non-substitutable consideration that actively involves user (Françoise et al., 2009; Hasibuan and Dantes, 2012). User involvement and participation could help to ensure user’s requirements with our better quality and user-friendly CERP systems since user involvement in the decision- making process result in greater attachment to the CERP capability and functionality (Lalsing et al., 2012; Françoise et al., 2009). In addition, training of user is also perceived. Needless to say, our CERP system would not create any values and profits if the employees do not know how to operate it. The need for user training and support is crucial in building CERP capability.

Vendor selection is an important decision regarding building CERP capability since most organizations purchase ERP packages from vendors. Based on the end-users requirements and owners’ consultants, organizations could take guidance to map the needs of the organizations with the respective vendor (Saleh et al., 2013; Françoise et al., 2009). In addition, trust on the vendor is a necessity to keep the good working relationship between firms and cloud vendor. Vendor support provides extended technical assistance for emergency aid and maintenance, updates, service responsiveness and user training (Somers and Nelson, 2001) during the period of CERP existence.

Project teamwork refers to the amount of knowledge and skills that are responsible for CERP capability building and business operation process. Umble and Umble (2002) suggested ERP teams should be composed of cross- functional members who possess skills, good reputations on past accomplishments and decision-making responsibility. In addition, the team would be supported by the leadership of the company.

Literature underlines the role of top management in orchestrating resources and creating capabilities and subsequently helping to achieve the competitive advantages of a firm (Hitt et al., 2016; Chadwick et al., 2015). Top management not only defines new objectives that could provide employees a clear vision of the orientation the organization is taking with careful consideration to the objectives but also supports all the decisions that need to be made to handle any conflicts that may happen. Thus, top management is required to commit and support for CERP system with enthusiasm, full consideration and even continuously monitoring among all the process (Prajogo and Olhager, 2012).

2.3.3Technological factors (TF)

Factors related to technologies that shall be affecting building CERP capability would be considered for the selection of ERP packages, IT infrastructure, data integrity and system testing, and its functionality of cloud ERP

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Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective

modules. Selecting ERP packages based on different cloud layers from a vendor should be strategic in nature in such a way that it matches the required business process (Gupta and Misra, 2016) and enhances the organization’s competitiveness and efficiency. IT infrastructure refers to the important components required for the CERP’s existence, operation and management in the form of hardware and software (Alaskari et al., 2012; Somers and Nelson, 2001), which is more significant to CERP vendor rather than the user. Data integrity is a critical aspect to the design, implementation and usage of CERP systems that are required to store, process and even retrieve data, as well as for the maintenance and the assurance of the accuracy and consistency of data over its entire lifecycle (Boritz, 2005). To make sure that CERP system would operate quickly and smoothly after the go-live, it is often tenable that system testing would continue as long as errors remain. Functionality is supposed to be in the sync with the selection of ERP vendor since it makes sure the consistency between ERP modules and organization business requirements. The decision for all these TF needs to be done before building the implementation of CERP (Gupta and Misra, 2016).

2.4Big data and predictive analytics

Given business processes with CERP capability and data sharing with the up and down partners are moving online, large-scale data would be created from these applications. As what most researchers asserted, BDPA would be the next big thing for firms to gain competitiveness in the dynamically changing market (Akter et al., 2016; Wamba et al., 2015). BDPA is actually an interdisciplinary field due to leveraging not only the statistical technologies such as regression, time-series analysis, etc., but also the computer and data science tools including data mining, machine learning, etc. (Dubey and Gunasekaran, 2015). It would be defined as a systematic process of descriptive analytics for explaining the data rules, predictive analytics for picture future insights and the final prescriptive analytics for optimizing or simulating the outcomes of organizational decisions.

In addition, recent scholars have acknowledged that BDPA is an organizational capability that they would process and exploit to know how organizations could achieve and sustain competitiveness regarding MP and OP of a firm (Gupta and George, 2016; Wamba et al., 2017). So how organizations could exploit resources and capabilities to build a BDPA capability would be examined next, which is defined as a firm’s edge to assemble, integrate and deploy its big data-specific resources to gain market share or improve profitability. Drawing upon DCV logic, we suggest that firms need a unique combination of data, MS and TS as the resources of building a firm-specific BDPA capability for making operational decisions or predictions.

2.5Toward the conceptualization of BDPA capability

2.5.1Data (D)

The world is witnessing an unprecedented huge interest in big data that is heralded as the next big hit for firms to gain the competitive edge (Frisk and Bannister, 2017; Rajput and Singh, 2018). The term “big data” is often used to describe a resource that features big in volume, big in forms (structured data, unstructured data and often semi- structured data) and big in velocity (fast-changing and real-time streaming), which most firms could approach (Lamba et al., 2018). Gupta and George (2016) highlighted that data are also the premise of deriving usable information for improved decision-making, action and positive change, besides labor and capital. Nevertheless, data by itself do little value to organizations. In other words, big data on its own are unlikely to be a source of competitive edge, since most firms have likely collected hordes of structured, unstructured or semi-structured data from various sources (Lamba and Singh, 2018a). It is imperative to have sophisticated data administration, data analytics and processing techniques to extract inherited insights (Beyer and Laney, 2012). Data are one of such immense resources, which are necessary but not sufficient to create a BPDA capability. It is imperative for firms to be aware of the various resources that are required to build BPDA capability (Lamba and Singh, 2018b).

2.5.2Managerial skills

MS are developed as a result of long years working experiences, which play a non-substitutable role for analytics projects as managers. The success of BDPA projects greatly depends on how well managers could infuse employees the common goals and assemble a team with right skills (Lamba and Singh, 2017; Dubey et al., 2018).

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Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective

The essential quality to predict market behavior and the interpersonal skills to develop swift have been regarded as the critical parts to the successful use of BDPA for FP. Big data analytics managers should be enabled to work with functional managers, suppliers and customers, to coordinate big data-related activities, to anticipate the future business needs with the good sense of where to apply big data and to understand and evaluate the output extracted from big data (Gupta and George, 2016).

2.5.3Technical skills

TS commonly refers to the know-how to possess specific skills and ability to extract intelligence from big data with the knowledge in statistics, computer and data science, as well as problem-solving skills and strong people skills (Lamba and Singh, 2016; Jeble et al., 2018). In terms of developing TS, firms could hire new talented employees with BDPA capability or conduct some big data analytics training for current employees. Big data analysts need are supposed to have the rights skills to accomplish their jobs smoothly with the suitable education and work experience (Schoenherr and Speier-Pero, 2015). More specifically, these right skills involve competencies and proficiency in statistics analytics, data cleaning, extraction analytics, data mining, machine learning and master of programming paradigms (Davenport, 2014).

2.6Organizational culture

Previous researchers have acknowledged the intangible resource of organizational culture is a source of sustained FP since it would be built over a long period and varies from organization to organization, which could not be duplicated by other competitors or coordinators (Teece, 2015; Jeble et al., 2018). Along similar lines, recent work regarding big data has identified organizational culture as a critical success factor to inhibit an organization’s ability to benefit from big data for analytics and predictive projects (Ross et al., 2013), urging firms to develop data-driven culture (LaValle et al., 2011; Ross et al., 2013; Gupta and George, 2016). To fully realize the potential of big data origins from firms, it is critical and necessary to develop a data-driven organizational culture.

Organizational culture can be viewed as “corporate personality” as it encompasses collective values, beliefs, behaviors and principles of organizational members that contribute to an independent enterprise with unique environment (Needle, 2010). Though organizational knowledge would never wear out, but it tends to become outdated. In line with the data-driven culture, organizational members (including top decision-maker and executives, middle managers and lower-level employees) ought to concert efforts to exploit their existing knowledge and explore new knowledge that would upgrade them. Their effort needs to be in place to keep learning the latest knowledge from internal and external environment. It is essential because the competitive environment keeps changing in the economics, technology, management, politics and even society. Based on the works of Gupta and George (2016) and Ross et al. (2013), organizational culture is the key intangible resource to contribute to building BDPA capabilities.

Prior literatures have proposed several ways to classify organizational culture into either relation-oriented or transaction-oriented culture (McAfee et al., 2002) or control and flexible orientation regarding organizational culture (Khazanchi et al., 2007). Control orientation values the predictability and efficiency, leveraging core competencies, profile valuation, value–practice interactions and value congruence (Liu et al., 2010; Dubey, Gunasekaran, Childe, Papadopoulos, Luo, Wamba and Roubaud, 2017). In contrast, flexible orientation emphasizes innovation paradoxes, creativity, spontaneity and risk-taking (Liu et al., 2010; Dubey, Gunasekaran, Childe, Papadopoulos, Hazen, Giannakis and Roubaud, 2017; Dubey, Gunasekaran, Childe, Papadopoulos, Luo, Wamba and Roubaud, 2017). Following with the works of Dubey, Gunasekaran, Childe, Papadopoulos, Hazen, Giannakis and Roubaud (2017) and Liu et al. (2010), our study adopted the paradoxical orientations of control and flexibility for the moderating effects of organizational culture study.

2.7Firm performance

To fully measure the difference in the FP among organizations, we have considered two distinct factors, namely, MP and OP. Based on prior academic literature, FP comprises these specific actual outcomes of a firm: MP (market share, etc.), OP (profits, return on assets, etc.), shareholder return, customer service, social responsibility and even

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Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective

employee stewardship (Richard et al., 2009; Upadhaya et al., 2014). MP and OP are the two important distinct dimensions or components standard to account for organizational effectiveness or to measure the performance of a firm (Gupta and George, 2016; Rai and Tang, 2010; Wu et al., 2015; Wang et al., 2012). Consistent with the previous literature on FP, we select these two separate dimensions of FP in our study (i.e. MP and OP).

2.7.1Market performance

In this study, we consider MP as an important role that acts on FP. The control variables to MP will be embodied in the abilities to explore market more quickly, to introduce new products or services into the market faster, and to gain higher success rate of new products or service and more market share than other competitors (Ji-fan Ren et al., 2017).

2.7.2Operational performance

In our study, we consider OP as another important part. The control variables of OP will be presented on the benchmark of productivity, profit rate, return on investment (ROI) and sales revenue. An organization with higher OP would mean more effective productivity, higher profit rate and ROI and more sales revenue than that of other competitor.

3.Theoretical framework and hypotheses development

In order to develop a theoretical framework to evaluate how BDPA and CERP act on FP, this study began by investing commonly cited resources that influence CERP and BDPA capabilities perception following DCV logic that explains an organization’s competencies and competitiveness in dynamically changing environments. Resources and capabilities are the core components of DCV: resources refer to technology, people and organization whereas capabilities represent a special type of resource that aims to improve the productivity of other resources. By which, a firm could depend on it to effectively manage its all critical resources to achieve PF.

BDPA capability is one of the key organizational capabilities identified as the big competitiveness in the big data era with the help of CERP capability. The theoretical background identified three dimensions that support and reflect BDPA that features a higher-order construct, that is, data (D), MS and TS based on DCV. On top of that, CERP capability was frequently identified as a multi-dimensional construct with OF, PF and TF throughout our review and theoretical exploration. Further, we directly linked the relationship between BDPA and FP, CERP and FP (including MP and OP), respectively. Furthermore, we developed our hypotheses of these relationships (Figure 1).

3.1Positive effect of cloud-based ERP on BDPA

Cloud-based ERP (CERP) links the internal departments of enterprise and the external enterprises across the supply chain, which shares a holistic view of all the data and information that impacts enterprises’ performance. Data in CERP are becoming increasingly voluminous in size, form and velocity from business and transactional activities. CERP systems are increasingly exposed to big data wherein the data analytics take place in a short moment of time with huge amount of data in the various form. Big data enables CERP to have a contextual view regarding all the processes. Big data in CERP system originates from organizational process details, purchase histories, business interactions, web behavior and even social media (Sastry and Babu, 2013). According to Babu and Sastry (2014) and McAfee et al. (2012), big data analytics require common and great use of predictive analytics to unfold potential rules or the relationships to explore current data and historical facts and even visualize the hidden pattern for the decision management in high-volume and front-line operational decisions and future probabilities and trends in market (Matthias et al., 2017; Beatty and Williams, 2006). In other words, predictive analytics help firms to generate their weekly or even daily forecasts for material requirements planning for the sales goals in CERP system with the real-time analysis (John Lu, 2010; Babu and Sastry, 2014).

BDPA in CERP system provide forward-looking decisions to yield better operational effectiveness and more- informed market, improving the competitiveness of an enterprise among competitors and determining the likelihood of a future opportunity. It makes CERP system reflects not only what has happened, but also what is happening and

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Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective

will happen in the soon future for decision-making (Sistla and Babu, 2013; Salleh et al., 2012; Duan et al., 2013). From CERP system data, predictive analytics is the key branch of data mining and exploitation concerned with the optimization of the operational process and the prediction of future probabilities in the changing market (Snijders et al., 2012). Following what the literature indicated, we hypothesize the first on as: H1. CERP service is positively related to the BDPA.

3.2Positive effect of BDPA capability on FP

BDPA capability is widely acknowledged to play a vital role in improving FP in the changing markets (Akter et al., 2016; Wamba et al., 2017). The literature provides plenty of evidences of a relationship between BDPA and FP, including productivity optimization, profit, ROI and sales revenue maximization on OP (Schroeck et al., 2012; Gupta and George, 2016; Wamba et al., 2017); and market share and new markets, be more quicker in responding advantages of new products or services into market than other competitors on MP (Ramaswamy, 2013; Gupta and George, 2016; Wamba et al., 2017). More specifically, Srinivasan and Arunasalam (2013) concluded BDPA could enhance firms with a great benefit in healthcare of a firm by reducing the cost such as the fewer wastes and fraud and improving the quality of care such as operation and transaction safety and the efficacy of re-engineering and treatment. Thus, effectively exploiting these resources and capabilities into BDPA capability is phenomenal to maximize FP by facilitating the pervasive use of insights and support firms to achieve and sustain competitive advantages among competitors.

Drawing on DCV logic, we argued that superior FP (MP and OP) emerges from the effective exploitation of data, human such as MS and physical resources such as TS that are defined as valuable and so hard to replicate treasures of a firm. Validated by the BDPA literature, we argued that effective BDPA capability differentiates FP, creates firm precious competencies and advantages in the dynamically changing big data environment. Hence, we propose our second and third research hypotheses as: H2. BDPA (BDPA) capability have a positive impact on the MP.H3. BDPA (BDPA) capability have a positive impact on the OP.

3.3Positive effect of cloud-based ERP on FP

CERP, as an integrated computer based application, has been increasingly concentrated toward by most firms and hosted in cloud platform over the internet over the last few years due to its capability to handle high-volume data sets in the centralized database, flexibility, scalability, friendly-use, low cost of setup, improved accessibility, lower upfront and operating costs, rapid implementation, cost transparency, higher security standards and free trials (Beheshti, 2006; Duan et al., 2013; Salleh et al., 2012). By the record, CERP features managing information for decision-makers at right time, faster response, smoothing and routinizing flow of data and information across departments and enterprises, supporting third-party software, developing wide database regarding supplier, manufacturer and customer behaviors for future market competences (Johansson et al., 2015; Okezie et al., 2012; Appandairajan et al., 2012). Jain and Sharma (2016) found that adoption of CERP service had a critical impact on the day-to-day operations of a firm and simultaneously enhanced the efficiency of the operational processes. Yu et al. (2018) argued CERP provides enterprise effective interaction of cross-functional operation with the real-time integration of business operations.

In general, the authors found that CERP service helped most of the enterprises to lead more efficient business activities and new opportunities in the market armed by the seamless flow of integrated business operation and information (Dwayne Whitten et al., 2012; Battleson et al., 2016; Bruque Camara et al., 2015; Gupta and George, 2016; Gupta et al., 2018b). Hence, the literature leads to our fourth and fifth hypotheses as: H4. CERP services have a positive impact on the MP of an organization.H5. CERP services have a positive impact on the OP of an organization.

3.4Moderating effects of organizational culture

Organizational culture correlates positively with the business performance that involves market share, product or service quality and responsivity, the source of competitive advantages in profit and sales (Jeble et al., 2018; Deshpandé et al., 1993). From the business perspective, organizational culture plays a vital role to support

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organizations in seeking competitive advantages for sustainable growth and thrive. As identified by many scholars (Hogan and Coote, 2014; Dubey, Gunasekaran, Childe, Papadopoulos, Hazen, Giannakis and Roubaud, 2017; Liu et al., 2010; Dubey, Gunasekaran, Childe, Papadopoulos, Hazen, Giannakis and Roubaud, 2017), organizational culture classified by control orientation (rational hand hierarchical culture) and flexible orientation (group and development culture) play their roles on different effects on organizational performance, respectively. Control- oriented culture commonly designs disciplines and regulations, focusing innovation initiatives (i.e. long-term strategies and goals), applying its core competencies and meeting business budgets. On the other hand, flexible- oriented culture enables firm’s creativity, empowerment and change vital for the exploration. Given flexibility-control would operate and originate from multiple levels. Control-oriented culture tends to descend from the top as the top management would come out with the strategy and goal, guidelines, disciplines and even constraints. By contrast, flexibility emerges in the model of bottom-up for most creative ideas or thoughts are popped up by the front-line subordinates. However, Khazanchi et al. (2007) argued that flexibility and control are not simple opposites and rather they contribute a more intricate paradox. Control is not the absence of flexibility and vice versa. In a nutshell, they would complement each other as the cross-fertilizations between the two orientations.

More specifically speaking, control-oriented culture emphasizes the predictability and efficiency, stability and productivity, cooperation, obeying disciplines and regulations and focusing on innovation initiatives (Dubey, Gunasekaran, Childe, Papadopoulos, Hazen, Giannakis and Roubaud, 2017; Khazanchi et al., 2007). That conforms to CERP systems with BDPA capability that enables organization to ensure the business activities intelligible, interoperable, normative and legitimacy for normal and high-efficiency process operation, predictable and high-productive for more competences regarding market competitions (Deshpandé et al., 1993; Dubey, Gunasekaran, Childe, Papadopoulos, Luo, Wamba and Roubaud, 2017; Liu et al., 2010; Khazanchi et al., 2007). Firms with a high control orientation value efficiency, which is indeed a well-touted advantage for operation processing in CERP systems. In addition, focusing predictability features from control-oriented culture would be a high-level support for BDPA working, particularly in the market decision-making. Hence, the firm with a control orientation would be more likely to embrace CERP systems and BDPA capability. We argue that an enterprise with higher control orientation would be the one which shall probably focus on great operational and market benefit that is endowed with by such a seamless and timely BDPA capability on CERP platform and collaborate with internal and external partners, comparing with the lower control orientation competitors. Therefore, we suggest our hypotheses as: H6. An organizational control orientation positively moderates the relationship between BDPA and MP.H7. An organizational control orientation positively moderates the relationship between BDPA and OP.H8. An organizational control orientation positively moderates the relationship between CERP and MP.H9. An organizational control orientation positively moderates the relationship between CERP and OP.

By comparison, flexible orientation tends to refer to the group and development culture such as the creativity, innovation paradoxes, spontaneity, embracing changes and risk-taking (Liu et al., 2010; Dubey, Gunasekaran, Childe, Papadopoulos, Hazen, Giannakis and Roubaud, 2017). Consequently, a flexible orientation may support aligning organizational strategies and goals in the direction of the new market (new product or service) and unique operating systems. On top of that, firms with the flexibility-oriented culture tend to leverage various resources in CERP systems to develop distinct and competitive capabilities to differentiate from their competitors gain more advantages from heterogeneity across the market (Dubey, Gunasekaran, Childe, Papadopoulos, Luo, Wamba and Roubaud, 2017). In doing so, BDPA passively related to CERP would be enhanced with a more direct effect on the performance of a firm as flexible orientation enables firms more creative, open for embracing new opportunities or changes in the dynamically changing environment. Thus, we may argue that flexible orientation would positively moderate the relationships between CERP/BDPA and OP and MP respectively. Hence, we hypothesize: H10. An organization’s flexible orientation positively moderates the relationship between cloud ERP and OP.H11. An organization’s flexible orientation positively moderates the relationship between cloud ERP and MP.H12. An organization’s flexible orientation positively moderates the relationship between BDPA and OP.H13. An organization’s flexible orientation positively moderates the relationship between BDPA and MP.

4.Research methodology

The research methodology used in our study includes: developing a theoretical framework based on DCV theory to understand the role of cloud-based ERP services and BDPA on the performance of a firm and empirically validating

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Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective

this theoretical framework by employing SEM. In the third section, we have presented our theoretical framework as above. We would discuss the empirical validity next.

4.1Construct operationalization

Appropriate scales from our literature review were employed to develop the survey instrument. The operationalization of each construct, measurement and derivation is shown in Table AI.

4.2Data collection

Our study employed a survey-based approach. An online survey was carried out to gather data to test our hypotheses and then qualify the role of CERP and BDPA on the performance of a firm. We designed a questionnaire that was administered to after-sales support executive, AVP/VP/EVP, consultant, corporate finance executive/analyst, director/CEO/founder, engineer, manager/senior manager, and the sales/marketing executive (Table I) throughout four types of cloud services and the cloud solutions (Table II) with total 231 respondents. These 231 fully filled questionnaires were applied for our final data collection and analysis, after continually modifying and improving its clarity and appropriateness base on respondents’ inputs and feedbacks. Our target sample was those firms operating CERP with BDPA capability.

The key respondents we approached were not only involved directly in CERP but also interested in BDPA for they would be likely to provide the better response to our questionnaire. You could see the domain of our respondents and their work experiences in Table III, which would be well validated that our respondents ranged over a variety of fields. Table IV represents the age and qualification of the respondents. This helps to guarantee the quality of the better response from the respondents with different fields and stages for our subsequent data analysis as well. Hence, we could see data collection is an extremely important part for our later procedures regarding our study.

4.3Data analysis and results

Our study puts efforts to assess the role of CERP and BDPA and even to explore the power of its combination on FP. Owing to the relationship between CERP and BPDA ant its joint efforts for a firm were not examined in the previous research, there would be a few specific theoretical foundations for reference to anticipate their associations on FP. However, the extant studies indicate partial least squares (PLS) makes sense to estimate a general model for an exploratory research (Gupta and George, 2016; Henseler et al., 2014; Moshtari, 2016). And we supposed our study is a more exploratory research in nature. By similar argument, both almost chime in easily. Hence, we employed PLS–SEM performed by Warp PLS version 6.0 to test our model and research hypotheses reaping the benefits of effectiveness to explain and predict the target constructs from PLS–SEM (Hair et al., 2016; Akter et al., 2017).

Examining measurement model would be required before analyzing the PLS–SEM model. Variance inflation factors (VIF) is a way of identifying the risk of multicollinearity (Peng and Lai, 2012). VIF values of 5 or above would be considered to have a certain risk of multicollinearity, where we would improve our model. Our result Average block VIF showed a value of 4.966 (see Table V) and indicated that multicollinearity may not be a risk for our study. Although average block VIF values of 3 or below would be ideal fit value for PLS analysis, average path coefficient (APC) and average R2 (ARS) would be mentioned regarding model fit indices for PLS as well. In Table V, APC (0.231, p<0.001) and ARS (0.567, p<0.001) show no evidence of fit problems. Hence, the results would be considered as a good fit to our model.

Apart from APC, ARS and AVIF, Sympson’s paradox ratio (SPR), R2 contribution ratio (RSCR), statistical suppression ratio (SSR) and Nonlinear bivariate causality direction ratio (NLBCDR) are commonly used model fit indices for PLS. Concretely, these three indices would be regarded as causality assessment indices which aim to test our hypothesis are correct or not. SPR (0.715, the acceptable score), RSCR (0.913, the acceptable score and close to ideal score), SSR (1, the ideal score) and NLBCDR (0.785, the acceptable score) indicate that our theoretical model is appropriate for our study (see Table VI).

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Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective

The result’s combined loadings and cross-loadings is also one part of the measurement model in PLS–SEM analysis and used with statistical tools Warp PLS version 6 to see our model is accepted or rejected (model fitness test). From the 231 fully filled questionnaire response, the combined loadings and cross-loading are developed to show the constructs of column and the indicators of row, as the indicators of reliability and validity of our theoretical model. Based on the results in the Appendix, it could group our model to qualify for the convergent validity into reflective indicators and significant factors. Loadings value of >0.50 would be grouped into significant factor. p-value of <0.05 are satisfied for reflective indicators. Therefore, our results (see the Appendix) indicate that most of the constructs are within or close to acceptable range in terms of reliability.

Cronbach’s α and composite reliability would be commonly used to test constructs reliability. In Table VII, the Cronbach’s α values are 0.966 for BDPA, 0.961 for CERP, 0.93 for MP, 0.943 for OP, 0.942 for CO and 0.914 for FO. In general, Cronbach’s α value of 0.7 and above is considered within the acceptable range (Gliner et al., 2001). We note that all the composite reliability is shown to arrive at the threshold value of 0.7 as well (Tellis et al., 2009). The average variances extracted (AVE) values satisfy the required value of >0.5 (Hair et al., 2006) for latent variable. The greater value of R2 coefficient means the better fitness to data. BDPA, MP and OP could be well explained by the constructs. All these results in Table VII could indicate the reliability of our constructs is acceptable.

Discriminant validity (irrelevance) tests whether an indicator that are not supposed to be related to a construct are actually unrelated. The acceptable discriminant validity is the case where square roots of AVE values are greater than the construct correlations (Hair et al., 2006). Table VIII reveals the relationship among the constructs and the square root of AVEs. Results indicate that the constructs are positively correlated to each other, and square roots of AVEs are greater than the correlations values on the same column and row for that construct. Table VIII indicates that discriminant validity is acceptable for our paper.

Table IX indicates our hypotheses developed in Section 4 would be considered to be appropriate for our paper.

5.Discussion

5.1Theoretical contribution

Our study is an early attempt to develop and explain the joint role of CERP and BDPA on the performance of a firm using the theoretical lens of DCV. From DCV’s perspective, our study extends the current understanding of concepts and the influence of BDPA and CERP as organizational capabilities on FP (Teece et al., 1997; Smith et al., 2014; Lawson and Samson, 2001; Kraaijenbrink et al., 2010). There exists remarkable contributions made by practitioners of CERP or BDPA to understand the effects of CERP or BDPA solely, but not from the prospective of integrated association of the two (Battleson et al., 2016; Chen et al., 2015; Akter et al., 2016; Wamba et al., 2015). We addressed the shortcoming by highlighting the joint effects of CERP and BDPA to improve the PF. Further, our empirical results using survey data from 231 executive-level technology respondents suggest that: for one, CERP is positively related to BDPA (H1), which supports our study on the joint effects of both; for another, BDPA directly affect MP and OP (H2 and H3) in dynamically changing environment, the same in CERP aspects (H4 and H5). Therefore, we could say our study further extend the extant CERP and BDPA literature regarding bringing organizations potential and sustainable development.

Our study also asserted the organizational cultures (i.e. control-oriented and flexibility-oriented) were not positively moderates the relationship between BDPA and operation performance (H7 and H12), between CERP and MP/OP (i.e. H8/H9 and H10/H11). The results are contrary to our expectations, but we supposed the relationship between organization culture and BDPA and CERP, respectively would happen to be not consistent to the previous literature (Schroeck et al., 2012; Fosso Wamba et al., 2017; Liu et al., 2010; Dubey, Gunasekaran, Childe, Papadopoulos, Luo, Wamba and Roubaud, 2017). It would also be an interesting question that is worth pursing further for future research scholars.

Finally, another important contribution of our study is to develop a theoretically grounded construct of organization survey instrument to measure the organization’s ability with CERP services and BDPA capability that is different

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Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective

from digital capabilities (i.e. IT capability). The constructs and its measures would give references to further researchers devoted to studying CERP and BDPA on the performance of an organization in terms of further the emerging research stream in that regard.

5.2Managerial implications

Our study yields interesting insights for practice in business organizations for improving firm’s competencies and competitiveness in a dynamically changing environment. Based on our current study highlighting the positive influence of CERP and BDPA on the performance of a firm, it attempts to enlighten business organizations that catching competitiveness in a changing market is not only about making hordes of investment, employing more talents with sophisticated skills and techniques, but also having access to CERP services as a well-established and cost-effective platform for data and information generation, sharing, processing and subsequent predictive analysis, BDPA capability where potential insights extracted from data and analysis acted upon and data-driven organizational culture (i.e. control-oriented and flexibility-oriented) where employees from top to ground have awareness to cultivate and process data-specific technologies and skills.

In addition to this, our study contained empirical validation of the role of CERP and BDPA on FP. It has advanced the understanding for top management and executives regarding the positive impacts in MP and OP from CERP and BDPA to enhance a firm’s competitiveness and sustainability in the dynamically changing environment. Our study suggested that business firms implement CERP service and invest in very talent techniques and knowledge learning and sharing culture to build BDPA capability, which offers huge benefits to gain competitive advantages among competitors.

5.3Limitations and directions for further research

Notwithstanding the insights on the positive impacts of CERP and BDPA for organizational performance for researchers and industry practitioners, some limitations and future research directions would be outlined. First, our study is the investigation of the role of CERP and BDPA as organizational capabilities that impact on FP and bring competitive advantages for a firm. The final destination, we hope interested business organizations could understand and build CERP and BDPA capabilities for their firms. In such an attempt, further research may need to be explored through investigating the role of institutional pressures on implementing CERP systems and their commitment toward developing BDPA capability, which could help to make it possible to employ CERP systems with BDPA capability in firms. In this vein, our research concept is not only one scientific idea, but one kind of advanced firm operation type.

Second, both sets of data needed for the empirical validation in our study focused on the organizations from India. Furthermore, the study conducted the survey-based empirical validation at one point in time from 231 respondents. Hence, our study would be expanded by involving a broader sample outside of India, avoiding the limitation of our understanding of the role of CERP and BDPA on FP caused by gathering data from single source at a single point in time. It would be interesting for our study to see if the country-level differences would influence these relationships between CERP and FP, BDPA and FP, and CERP and BDPA.

Finally, our study relies on the survey-based approach for empirical validation. To advance better insights into CERP and BDPA capabilities, maybe the way of a mixed form approach could do better, such as telephone interview, semi-structured interviews with respondents. By which, the relationships between these constructs in our model could be further understood and lead to better empirical investigation.

6.Conclusion

This work developed a theoretical framework based on DCV theory to understand the role of CERP and BDPA on the performance of a firm. Drawing broadly on the DCV theory, we have conceptualized CERP and BDPA as organizational capabilities and tested the relationship between CERP and BDPA and their respective impacts on FP under the moderating effects of organizational culture. Through our empirical validation by employing SEM, it turned out that CERP which is positively related to BDPA has positive impacts on FP, and BDPA has the same positive

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impacts on FP, although the high or low control and flexible orientation do not positively moderate the impacts of BDPA on OP, CERP on MP and OP. By which, it supports to a certain degree to advance the understanding for business organizations to consider and build CERP and BDPA capabilities. Our study would be first theoretical framework to investigate and explain the joint impacts of CERP and BDPA on FP. And finally, we presented the empirical evidence that CERP and BDPA would be predictors of the organizational performance by developing a survey-based instrument. However, further research based on our limitations part would be explored to arrive to our final goal regarding convincing organizations to build CERP and BDAP capabilities.

Classification

Language: ENGLISH

Publication-Type: Newspaper

Journal Code: MD

Subject: COMPANY ACTIVITIES & MANAGEMENT (90%); BACK OFFICE OPERATIONS (89%); BUSINESS NEWS (79%); TRENDS (72%); SMALL BUSINESS (70%); Big data (%); Organizational culture (%); Structural equation modelling (%); Firm performance (%); Cloud ERP (%); Dynamic capability view (%)

Industry: CLOUD COMPUTING (87%); DECISION SUPPORT SYSTEMS (78%); BUSINESS SOFTWARE (75%); SYSTEMS INTEGRATION (74%); COMPUTER SOFTWARE (73%)

Load-Date: September 23, 2019

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Bibliography

1. Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective, Shivam Gupta, Xiaoyan Qian, Bharat Bhushan, Zongwei Luo., Management Decision, Pg. 1857-1882, (September 12, 2019)

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