Research paper

The Impact of IT Assets on Innovation Performance – The Mediating Role of Developmental Culture and Absorptive Capacity

Marius Stoffels*

University of Münster, Leonardo Campus 1, 48149 Münster, Germany.

E-mail: [email protected]

Jens Leker

University of Münster, Leonardo Campus 1, 48149 Münster, Germany.

* Corresponding author

Abstract: Firms have well adopted information technology (IT) in their innovation management practices. A plethora of benefits across various functions in the innovation process have been attributed to IT, while the research perspective on the organizational context in which IT is most valuable has shifted to a perspective that jointly considers social and material components. Taking a resource-based view on sociomateriality, we conduct structural equation modelling with a sample of 58 firms drawn from the water industry in Germany and find that a firm’s absorptive capacity and developmental culture fully mediate the relationship between IT and innovation performance. The article closes with ideas for further research and managerial implications.

Keywords: information technology; innovation management; innovation performance; absorptive capacity; organizational culture; developmental culture; sociomateriality; structural equation modelling.

1 Introduction

Continuous progress in information technology (IT) largely supports firms in their innovation activities (Kawakami, Barczak, & Durmusoglu, 2015; Mauerhoefer, Strese, & Brettel, 2017). As innovation activities are concerned with the generation and recombination of knowledge (Cohen & Levinthal, 1989, 1990), IT has been used to access and integrate previously unknown amounts of knowledge, often codified as data, in the new product development process, which substantially changed the innovation process and the architecture of the results (Nambisan, Lyytinen, & Song, 2017; Yoo, Henfridsson, & Lyytinen, 2010). IT has thus become an integral component of a firm’s absorptive capacity (Roberts, Galluch, Dinger, & Grover, 2012). Extant research has argued that the competitive advantage of a firm’s absorptive capacity resides in a complex value creation network of organizational resources that augment the value of this capability and prevent it from being easily imitated (Zahra & George, 2002). Likewise, the value of IT for innovation management unfolds (at least to date) solely in a social system with complex routines including several stakeholders (e.g. Barrett, Oborn, Orlikowski, & Yates, 2012). This notion is also known as sociomateriality (Orlikowski, 2007), indicating that social components and technological material are inevitably linked

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and need to be considered in conjunction for the explanation of innovation results. Taking a resource-based view (RBV) perspective (Barney, 1991), we follow this notion and investigate the nature of the relationship between material resources and social resources. In this work, we therefore set IT assets into context with organizational capabilities that describe knowledge utilization processes, and entrepreneurial beliefs within the company. In the next section, we review existing work on the mediating capabilities between IT and innovation performance and conclude with two resulting hypotheses.

2 Theory and Hypotheses

IT assets and innovation performance

A rich body of prior empirical research suggests that the use of information technology in innovation management is positively related to innovation outcomes. From a firm-level perspective, Kleis et al. (2012) studied US manufacturing firms and found that a 10% increase in IT input relates to a 1.7% increase in innovation output, measured as the amount of patents filed. Several empirical studies provide evidence for the mechanisms that translate IT investments into increases in innovation output. For example, IT has shown to be useful to manage project complexity (Ravichandran, Han, & Mithas, 2017), strengthen organizational knowledge management practices (Cepeda-Carrion, Cegarra- Navarro, & Jimenez-Jimenez, 2012), facilitate collaboration (Peng, Heim, & Mallick, 2014), and enable execution of dynamic capabilities (Pavlou & El Sawy, 2006). All of the above-mentioned functions mediate the effect of IT on various dimensions of new product development success and delineate practical implications for innovation managers. In the majority of research, the character of IT in innovation management has been conceived of as an enabling (operand) resource, while digital tools increasingly trigger the development of new products and services (operant resource) (Nambisan, 2013). As the mode of harnessing IT differs in the prior scenarios, both require strong IT assets as the foundation for other benefits to materialize. Following the resource-based view of the firm (Barney, 1991; Wernerfelt, 1984), we include IT infrastructure and IT capabilities in our theoretical framework. For the conceptualization of IT infrastructure, we follow Chakravarty et al. (2013, p. 977) and “define IT infrastructure as including not only physical assets, such as hardware platforms, software applications, data repositories, and other networking and object-based technologies, but also the quality and frequency of updates to all IT-related asset stocks”. Further, IT capabilities describe “a firm’s technical and management skills and IT practices” (Chakravarty et al., 2013, p. 977). Together, the two dimensions capture not only the materiality of IT, but also the utilization capabilities.

The mediating role of absorptive capacity

A firm’s capability to acquire, assimilate, transform, and exploit valuable external knowledge is known as a firm’s absorptive capacity (Cohen & Levinthal, 1990; Zahra & George, 2002). While knowledge acquisition and assimilation are often considered as potential absorptive capacity (PACAP), knowledge transformation and exploitation are termed realized absorptive capacity (RACAP) (Zahra & George, 2002). IT has long been recognized as a substantial supportive function for all stages in the execution process of this capability (for a review, see Roberts et al., 2012). IT facilitates searching for external information, allows internal knowledge storage, as well as exchange of information

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throughout and interpretation in the company, while the usefulness of IT in knowledge exploitation has gained importance in recent years (Nambisan et al., 2017). In this respect, complementary technologies such as computer aided design (CAD), cloud-based collaboration platforms, and visualization tools facilitate the exploitation of organizational knowledge resources across intra- and inter-organizational boundaries (Shneiderman, 2007). As a classical process industry (Lager, Blanco, & Frishammar, 2013), the water industry knows many examples in which IT enabled the exploitation of knowledge that eventually produced innovative solutions for the optimization of complex and interdependent water systems (GWP, 2016).

While there exists a plethora of studies that cover the impact of either IT on absorptive capacity, IT and innovation performance, or absorptive capacity and innovation performance, empirical evidence that spans all three subjects is not numerous. Among the studies that comprise all domains, Pavlou and El Sawy (2006) show that new product development capabilities including absorptive capacity fully mediate the impact of a newly proposed IT leveraging competence on competitive advantage in new product development. Joshi, Shi, Datta and Han (2010) construct a hybrid capacity which they term IT absorptive capacity and distinguish between potential and realized forms of that construct (Zahra & George, 2002). Using structural equation modeling, they find that ideated innovation in form of patent application counts partially mediates the impact of IT absorptive capacity on the creation of commercialized innovation, operationalized as the number of new product and service introductions. Equally following the notion of potential and realized absorptive capacity, Cepeda-Carrion et al. (2012) argue that IS capabilities mediate the effect of potential ACAP on realized ACAP. While they confirm a partial mediation effect of IS capability between potential and realized ACAP, the change is R2 for realized ACAP is small (0.02), indicating that the explanatory value of IS capabilities as a mediator between PACAP and RACAP is not substantial. As the studies above show, the role of ACAP in translating IT assets into innovation has been conceptualized in multiple different ways. We follow Pavlou and El Sawy (2006) and argue that a firm’s IT assets contribute to the absorptive capacity of the firm, which, in turn, improve innovation performance.

For the theoretical underpinnings of our study, we adopt a resource-based view on absorptive capacity and conceptualize it as a dynamic capability that facilitates taking advantage of external knowledge to produce or react to environmental change through innovation (Lichtenthaler & Lichtenthaler, 2009; Teece, Pisano, & Shuen, 1997; Wang & Ahmed, 2007). We conclude with the following hypothesis.

Hypothesis 1: A firm’s absorptive capacity mediates the effect of IT assets

on firm innovation performance.

The mediating role of developmental culture

Organizational culture is known as a potential source of competitive advantage (J. Barney, 1986) and a driver of innovation performance (De Brentani & Kleinschmidt, 2004). In their review on the relationship between IT and organizational culture, Leidner and Kayworth (2006, p. 370) point out that “IS culture research has focused on culture's impact on IT. Relatively few of the studies explicitly examined the potential impact of IT on culture“. Further, they conclude that “the overwhelming focus in both national and organizational culture IS research has been to treat culture as being stable, persistent, and difficult to change” (Leidner & Kayworth, 2006, p. 370). Among the studies that investigate the impact of IT on organizational culture, Doherty and Doig (2003) outline

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that IT-induced changes in employees’ working practices can unfold cultural implications over time. In a qualitative study of workflow management systems (WMS) in the financial services sector in UK, Doherty and Perry (2001) found that the cultural dimensions of customer orientation, flexibility, and quality focus can be enhanced by WMS.

In a different review on IT and culture, Gallivan and Srite (2005) describe an evolution of research perspectives on culture and IT across several stages. In contrast to the deterministic perspective, in which IT impacts organizational culture, current viewpoints allow for interdependence of social and technological components in an integrated framework of sociomateriality (Cecez-Kecmanovic, Galliers, Henfridsson, Newell, & Vidgen, 2014; Orlikowski, 2007). The sociomateriality perspective acknowledges that organizational processes involve material resources and social interactions with them in organizational processes and routines (Orlikowski, 2007). We share the idea that competitive advantages residing in IT are inevitably linked to the social processes that make use of them and therefore include both into our research model. In this respect, we draw on the entrepreneurial alertness of employees (Sambamurthy, Bharadwaj, & Grover, 2003), conceptualized as the developmental culture of the firm (Lau & Ngo, 2004).

Thus, we argue that developmental organizational culture links IT and organizational innovativeness.

Hypothesis 2: A firm’s developmental culture mediates the effect of IT

assets on firm innovation performance.

3 Research Method

Data collection and sample

Research on the relationship between IT and innovation management primarily draws on data samples from manufacturing industries (Trantopoulos, Krogh, Wallin, & Woerter, 2017) or cross-industry samples (Joshi et al., 2010). In order to strengthen evidence from underresearched industries, we base our research in the water industry, a classical process industry which is characterized by research intensity, complex value chains, and strong integration into physical assets (Lager et al., 2013). The water industry is well suited for our research because of the high degree of automation and the integral part that IT plays for the core competencies in this industry. Data gathering was conducted within the German Water Partnership (GWP), which is the leading industry association and a major driver of the debate about digitalization in the water industry (Stoffels & Ziemer, 2017).

The research design included the following two major steps. First, we conducted three qualitative interviews with industry experts involving open and closed questions, in order to confirm the relevance of our research objective for practice. Interviewees stressed the challenges of assimilating, acquiring, transforming, and exploiting relevant technology- related knowledge as well as the role of organizational culture as a possible impediment towards digitalization. At the same time, we assessed the comprehensibility of the Likert type items we intended to use in a consecutive online questionnaire. Second, in the subsequent data collection period in early 2017, GWP forwarded our online questionnaire survey to the corresponding individuals inside each of the 350 member companies and reminded them with two mails to participate. After five weeks, 67 respondents completed the survey, resulting in a response rate of 19.14% calculated as the relation of participants over the total number of members in the association.

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Key informant check

In order to assure that respondents are knowledgeable about the questions in our survey, we applied several key informant criteria such as the respondents’ position in the company, their industry experience, and their organizational tenure. Respondents mainly hold the position of chief executive officer, head of department, or senior management functions. On average, respondents have over 16.7 years of industry experience, while they spent 13.3 years in the company they currently work for. Further, being a member of GWP indicates that respondents are involved in actively scanning the industrial environment, augmenting their ability to rate the innovation performance of the own firm in relation to competitors in the network. In total, nine respondents did not comply with our key respondent criteria, which leaves a sample of 58 useable surveys for further analysis.

Measures

For the conceptualization of our research constructs, we draw on items that have been established in prior research (e.g. Mauerhoefer et al., 2017). Starting with the independent variables, the constructs of IT infrastructure and IT capabilities, which include four and five items respectively, were adopted from Chakravarty et al. (2013). IT assets was operationalized as the second order reflective construct of the former two. As the mediators, we operationalized absorptive capacity by borrowing a five item scale from Lusch et al. (2011), and developmental culture was measured following Lau and Ngo (2004), using four items. As the dependent variable, we used a three item construct to measure innovation performance, which includes a slightly adapted wording of the original scale for firm performance from Olson et al. (2005) by exchanging ‘firm performance’ with ‘innovation performance’ in the respective indicators. Throughout all questions, we employed a seven point Likert scare anchoring “do not agree at all” (1) and “fully agree” (7).

Control variables During PLS-SEM, we included firm age, firm size, and industry segment as control variables because of their potential influence on the variables in our model. Firm age might influence our results in that younger firms might possess stronger developmental organizational cultures but, in turn, might lack absorptive capacities due to fewer network connections and prior knowledge. Further, we control for firm size because the availability and specialization of IT assets might increase with the number of employees, while it is more challenging to conserve a developmental culture in large companies with diverse specialized functions. Finally, although all firms operate in the water industry, differences between industry segments might exist such as that different cultures are needed in different segments.

4 Analyses and results

Common method variance

Due to our key informant approach, in which dependent and independent variables were assessed simultaneously, our results are potentially inflated by common method bias (Podsakoff & Organ, 1986). To address this issue, we follow the instructions provided by Liang et al. (2007) and included a common method factor, which was connected to all converted single-indicator constructs. The results displayed in Appendix B

This paper was presented at The ISPIM Innovation Conference – Innovation, The Name of The Game, Stockholm, Sweden on 17-20 June 2018. The publication is available to ISPIM members at

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Appendix A Constructs, Scores, and Item Loadings show that most factor loadings from the common method factor were insignificant and on average, the constructs explain 78.1% of the variance while the method factor only accounted for 2.9%. Thus, the ratio of substantive variance explained over the variance explained by the method factor is 26.9, indicating that the explanatory power of the method factor is not likely to threaten the validity of the model. Further, Table 2 demonstrates that inter-construct correlations are below the suggested threshold of .90 (Bagozzi, Yi, & Phillips, 1991). Although the existence of common method variance can never be ruled out completely, the tests regarding common method bias suggest that it does not substantially influence the results.

Evaluation of the measurement model

Prior to assessing the structural model and testing hypothesis, the psychometric properties including item reliability, convergent validity, and discriminant validity were analyzed (Hulland, 1999). Item reliability refers to how well a common underlying construct loads on all related items, in which respect loadings above .7 indicate that the construct determines the majority of the items’ variance (Hulland, 1999). For higher-order constructs, structural coefficients between the second and first order constructs are regarded as a proxy for item loadings (Doll, Xia, & Torkzadeh, 1994). The item loadings presented in Appendix A all exceed this threshold. Next, Table 2 presents the assessment of convergent validity using Cronbach’s Alpha (CA) and composite reliability (CR). All constructs fulfill the suggested thresholds of 0.7 for both measures. Finally, discriminant validity is supported when the square root of the average variance extracted (AVE) of a given construct exceeds all correlations with other constructs (Fornell & Larcker, 1981; Peng & Lai, 2012). We additionally apply the heterotrait-monotrait ratio (HTMT) criterion to assess discriminant validity (Henseler, Ringle, & Sarstedt, 2015). The HTMT values are presented in Appendix C and lie below the suggested threshold of .9 (Henseler et al., 2015), which demonstrates that constructs are sufficiently distinct from each other.

In order to test whether our sample size is large enough to allow for targeted significance levels, we applied the gamma-exponential method proposed by Kock and Hadaya (2016), which takes into account the power, significance level, and beta coefficient of the respective relationships in the model. In comparison to other sample size estimations like the 10-times rule (Joe F. Hair, Ringle, & Sarstedt, 2011), or the minimum R-squared method (Joseph F. Hair, Hult, Ringle, & Sarstedt, 2017, p. 25) the gamma-exponential method yields more precise estimates (Kock & Hadaya, 2016). Considering a power level of 0.8., a significance level of p ≤ 0.01 and a path coefficient of 0.40 (cf. Figure 1) for the lowest hypothesized relationship, the size of our sample exceeds the calculated minimum sample size of 46 cases.

Results of structural model

The relationship between the IT assets and innovation performance is investigated by applying partial least squares structural equation modelling (PLS-SEM), which is especially suited if the sample size is between 50 and 100 and complex models including second-order constructs are analyzed (Peng & Lai, 2012). Figure 1 and Table 1 show the results of the structural model, including path coefficients, significance levels, and squared multiple correlations (R2). Employing SmartPLS 3 (Ringle, Wende, & Becker, 2015) we used the path method to estimate path coefficients and obtained standard errors and significance levels via bootstrapping with 500 resamples. Stability tests using 1000 and 2000 resamples showed no changes in significance levels.

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Table 1 Results of structural equation modelling with PLS.

Absorptive

capacity

Developmental

culture

Innovation

performance

β-value p-value β-value p-value β-value p-value

Main effects

IT assets 0.67 ≤ 0.01 0.73 ≤ 0.01 0.05 0.71

Absorptive capacity 0.40 ≤ 0.01

Developmental culture 0.43 ≤ 0.01

Control variables

Firm age -0.07 0.61 -0.185 0.16 0,102 0.37

Firm size -0.17 0.25 -0,088 0.48 0,101 0.39

Component

manufacturer

-0.03 0.80 0,078 0.54 -0,154 0.23

Plant construction 0.05 0.65 0,014 0.88 -0,125 0.21

Consulting 0.02 0.87 -0,115 0.33 -0,081 0.54

R-square 0.51 0.61 0.63

All tests are two-tailed. N = 58.

Figure 1. Results of structural equation modelling analyses.

IT Assets

IT

Infrastructure

R2 = 0.81

IT Capabilities

R2 = 0.91

Innovation

Performance

R2 = 0.63

Developmental

Culture

R2 = 0.61

Absorptive

Capacity

R2 = 0.51

Notes: * p ≤ 0.10; ** p ≤ 0.05; *** p ≤ 0.01 All tests are two-tailed. N = 58

Control Variables: Firm Size, Firm Age, Industry Segment

Significant path

Non-significant path

0.67***

0.73*** 0.43***

0.40***

0.05

0.95***

0.90***

This paper was presented at The ISPIM Innovation Conference – Innovation, The Name of The Game, Stockholm, Sweden on 17-20 June 2018. The publication is available to ISPIM members at

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Table 2. Psychometric properties of first-order measurement scales and correlations.

ME SD CR CA AVE 1 2 3 4 5 6 7 8 9 10

1 IT Infrastructure 5.32 1.16 0.92 0.88 0.73 0.85a

2 IT Capabilities 4.90 1.26 0.94 0.92 0.77 0.72** 0.88a

3 Developmental Culture 5.10 1.32 0.94 0.92 0.81 0.68** 0.69** 0.90a

4 Absorptive Capacity 5.04 1.04 0.91 0.87 0.66 0.57** 0.67** 0.72** 0.81a

5 Innovation Performance 4.66 1.39 0.95 0.92 0.86 0.52** 0.61** 0.69** 0.68** 0.93a

6 Firm Size 2.21 1.18 - - - -0.07 0.09 -0.16 -0.22 0.03 -

7 Firm Age 58.54 48.63 - - - -0.10 0.10 -0.26* -0.24 -0.08 0.65** -

8 Engineer/Consulting 0.34 0.48 - - - 0.07 0.23 0.04 0.19 0.08 -0.19 -0.30* -

9 Plant Construction/System Integration 0.31 0.47 - - - 0.00 -0.12 0.00 0.02 -0.15 -0.21 0.11 0.25 -

10 Component Manufacturer 0.45 0.50 - - - 0.11 -0.01 0.11 -0.05 -0.07 0.17 0.27* 0.51** 0.22 -

Note: ME = mean. SD = standard deviation. CR = composite reliability. CA = Cronbach's alpha. AVE = average variance extracted.

a Value on the diagonal is the square root of the AVE (bold). * p ≤ 0.05; ** p ≤ 0.01.

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Table 1 Specific indirect effects.

Beta coefficient St.Dev. T Statistic p-Values Hypothesis

IT assets – innovation performance (absorptive capacity)

0.27 0.09 2.88 0.004 1

IT assets – innovation performance (developmental culture)

0.32 0.12 2.71 0.007 2

Mediation analysis

In a subsequent analysis, we assessed whether the mediators fully mediate the effect of IT assets on innovation performance. Full mediation implies that the direct link between the antecedent and the dependent variable is not statistically significant, whereas the path via the mediator is statistically significant (Baron & Kenny, 1986). To evaluate the type of mediation, we followed the guidelines provided by Zhao et al. (2010) and conducted mediation analysis in two steps. First, we tested whether a significant indirect effect from the independent variable on the dependent variable via the mediator was present. Second, we analyzed whether the direct effect was significant, which determines if the mediators fully or partially mediate the respective relationship (Baron & Kenny, 1986; Zhao et al., 2010). The results in Table 3 demonstrate that the indirect effects via both mediators are statistically significant. Simultaneously, the structural model in Figure 1 shows that the direct path from IT assets to innovation performance is not significant, indicating a full mediation (indirect-only) relationship (Baron & Kenny, 1986; Zhao et al., 2010).

Regarding the research question of our study, hypotheses 1 and 2 are supported at a significance level of p ≤ 0.01. Results from structural equation modelling and analysis of indirect effects support the hypotheses that a firm’s absorptive capacity and developmental culture fully mediate the influence of IT assets on innovation performance. Medium effect sizes of absorptive capacity (f 2 = 0.17) and developmental culture (f 2 = 0.15) further indicate the explanatory power of the two mediators on innovation performance (Chin, 1998).

5 Discussion, implications, and limitations

This study adds to the debate of how information technology contributes to firm innovation performance. The full mediation character in the structural model indicates that investments in IT are not self-sufficient but require complementary organizational capabilities in order to allow positive effects on corporate innovation. This finding underscores the importance of empirical research that jointly considers technology resources and the social structures and working processes in which they are embedded, such as sociomateriality (Gaskin, Berente, & Lyytinen, 2014). The mediating role of absorptive capacity unveils that a firm’s ability to acquire, assimilate, transform, and exploit external knowledge benefits from IT assets, which might eventually augment the innovation output of the firm. Further, an organizational culture that encourages innovation and entrepreneurship equally mediates the translation of IT assets to superior innovation outcomes. In conclusion, our results lend support for the interactionistic view on IT and innovation (Gallivan & Srite, 2005) and sociomateriality (Orlikowski, 2007). While we confirm that organizational routines such as absorptive capacity mediate the effect of IT on innovation performance, the full mediation of developmental culture calls

This paper was presented at The ISPIM Innovation Conference – Innovation, The Name of The Game, Stockholm, Sweden on 17-20 June 2018. The publication is available to ISPIM members at

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This paper was presented at The ISPIM Innovation Conference – Innovation, The Name of The Game, Stockholm, Sweden on 17-20 June 2018. The publication is available to ISPIM members at

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10

for further research on how the impact of IT on organizational culture can be embedded in the ongoing debate about sociomateriality.

Managerial implications

While the application of IT is a pervasive and continuous driver in innovation management, a firm’s ability to reap the benefits from IT investments is contingent on additional organizational characteristics. Specifically, managers might benefit from evaluating how investments in IT improve the firm’s capacity to acquire, assimilate, transform, and exploit external knowledge. Assessing the technological and software components for each function might pinpoint unrealized potential for further improvement of making use of external information. Further, it is useful to be aware that IT has the potential to influence organizational culture, while in return culture determines how technology is used. Allowing successful internal entrepreneurship in terms of a developmental culture is thus related to equipping potential entrepreneurs with basic IT knowledge and IT workers with entrepreneurial skills. In this context, the technology is only as useful as the entrepreneurial ideas that the technology users pursue.

Limitations and future research

A valuable extension of the deterministic view according to which developmental culture and absorptive capacity being influenced by IT assets, is the adoption of a more interactionist view that allows interaction between the independent and mediating constructs (Gallivan & Srite, 2005). This perspective of sociomateriality would benefit from additional empirical evidence and methodological approaches that enable the investigation of interdependencies between the material and social components (Cecez- Kecmanovic et al., 2014). Novel qualitative and quantitative approaches might illuminate the interplay between social and material components in digital-human networks of value creation, which are eventually the source of competitive advantages.

Acknowledgements

We would like to thank the reviewers for their insightful and valuable comments on the outline of this research paper.

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This paper was presented at The ISPIM Innovation Conference – Innovation, The Name of The Game, Stockholm, Sweden on 17-20 June 2018. The publication is available to ISPIM members at

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15

Appendix A Constructs, Scores, and Item Loadings.

IT Capabilities Chakravarthy et al. (2013)

ITC1 We have strong technical IT skills. 0.91

ITC2 We have adequate knowledge about IT. 0.87

ITC3 Our IT skills are comparable with the best in the industry. 0.91

ITC4 We invest heavily in our IT human resources. 0.90

ITC5 We have a good understanding of the possible benefits of IT

applications. 0.79

IT Infrastructure

ITI1 We have strong IT planning capabilities. 0.81

ITI2 We have extensively invested in building our IT infrastructure. 0.87

ITI3 We have a state-of-the-art IT infrastructure. 0.87

ITI4 We regularly update our IT assets. 0.87

Absorptive Capacity Zacharia, Nix, Lüsch (2011) / Cohen and Levinthal (1990)

In general, my organization has the ability to...

ACAP1 recognize valuable new knowledge 0.86

ACAP2 absorb new knowledge useful to the organization 0.83

ACAP3 take advantage of new knowledge to improve performance 0.83

ACAP4 adapt to change and adopt new ideas to improve performance 0.77

ACAP5 identify and adopt new and useful ideas 0.77

Developmental Culture

Lau and Ngo (2004)

DC1 Our firm is a very dynamic and entrepreneurial place. 0.90

DC2 The head of our firm is generally considered to be an entrepreneur, an

innovator, or a risk-taker. 0.91

DC3 The glue that holds our firm together is commitment to innovation and

development. 0.88

DC4 Our firm emphasizes growth and acquiring new resources. 0.90

Innovation Performance Olson, Slater, Hult (2005, adapted)

Last year…

IP1 our innovation performance met the expectations. 0.93

IP2 our innovation performance exceeded that of our competitors. 0.90

IP3 our firm was very satisfied with the innovation performance of the

business. 0.95

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16

Appendix B Analysis of Common Method Variance.

Construct Construct Loading (CL) CL2 Method-Factor Loading (MFL) MFL2

ITC1 0.931 0.867 -0.022 0.000

ITC2 1.019 1.038 -0.165 0.027

ITC3 1.174 1.378 -0.300 0.090

ITC4 0.779 0.607 0.131 0.017

ITC5 0.440 0.194 0.400 0.160

ITI1 0.505 0.255 0.354 0.125

ITI2 0.983 0.966 -0.132 0.017

ITI3 0.940 0.884 -0.074 0.005

ITI4 0.975 0.951 -0.128 0.016

ACAP1 0.928 0.861 -0.072 0.005

ACAP2 0.886 0.785 -0.066 0.004

ACAP3 0.655 0.429 0.199 0.040

ACAP4 0.684 0.468 0.093 0.009

ACAP5 0.910 0.828 -0.161 0.026

DC1 0.714 0.510 0.203 0.041

DC2 1.040 1.082 -0.146 0.021

DC3 0.928 0.861 -0.054 0.003

DC4 0.911 0.830 -0.006 0.000

IP1 0.796 0.634 0.159 0.025

IP2 0.989 0.978 -0.104 0.011

IP3 0.994 0.988 -0.006 0.000

Average 0.866 0.781 0.135 0.029

All constructs are significant at p < .01; method factor loading of ITI is significant at p < .05; ITC3 < 0.01;

ITC5 < 0.05; IP1 < .10.

ITC = IT capabilities; ITI = IT infrastructure; ACAP = absorptive capacity; DC = developmental culture; IP =

innovation performance.

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Appendix C Heterotrait-monotrait ratio (HTMT).

1 2 3 4 5 6 7 8 9 10

1. IT Infrastructure -

2. IT Capabilities 0.799 -

3. Absorptive Capacity 0.643 0.745 -

4. Developmental Culture 0.749 0.750 0.808 -

5. Innovation Performance 0.585 0.663 0.770 0.758 -

6. Firm Age 0.135 0.125 0.297 0.293 0.103 -

7. Firm Size 0.122 0.092 0.235 0.162 0.030 0.645 -

8. Component Manufacturer 0.122 0.035 0.115 0.119 0.088 0.252 0.166 -

9. Plant Construction 0.041 0.133 0.100 0.036 0.155 0.090 0.214 0.220 --

10. Consulting 0.104 0.238 0.208 0.085 0.088 0.295 0.190 0.508 0.251 -

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