EBM723B20 Master’s Thesis BA SIM MSc BA Strategic Innovation Management The Role of Information Technology Capability in Enhancing the Impact of R&D Alliances on Firm Performance

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EBM723B20

Master’s Thesis BA SIM

MSc BA Strategic Innovation Management

The Role of Information Technology Capability in Enhancing

the Impact of R&D Alliances on Firm Performance

Manqing Tan S2548143 m.tan.1@student.rug.nl Supervisor: Dr. Qi. (John) Dong Co-assessor: Pro. dr. ir. Jo van Engelen

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Abstract

The effective strategic alliances contribute to the development of innovation and superior firm performance. Barriers such as cultural differences, intangible knowledge transfer hamper the R&D cooperation. However, it may be eliminated and enhanced the cooperative barriers in practice. Information technology has changed the way that firms to transfer and learn knowledge, yet little research focuses on exploring whether or not the information technology (IT) capability that the firm developed can help mitigate barriers in the alliance context, in turn contributing to the performance of the focal firm. This study investigates the effect of information technology capability on R&D alliances. Invoking resource-based view, we test the impact of R&D alliances on firm performance for firms with and without superior IT capability. Our sample included large-scale panel data from U.S. public firms in the period of 2007 to 2009. We find that R&D alliances increase firm performance in firms with superior IT capability, while without such capability, the result shows that R&D alliances do not have significant effect on firm performance. These findings enrich strategy and innovation literature, by showing that firms should develop appropriate IT capability in order to capture more value from R&D alliances and outperform competitors.

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1. Introduction

Currently, in response to rapid development of technology, shorter product life cycle as well as rising expense of capital, strategic alliance has been proved an alternative option, which helps firms to access complementary resources, speed up time to markets, or share risks (Sampson, 2007), contributing to firm’s performance in the end. However, barriers such as technological diversity and the differences in goals, values and resources between partnering firms hamper the performance of strategic alliances (Song and Song, 2010). Because of this barrier, the outcome of alliances is often disappointing even though more and more firms choose this form to collaborate (Park and Ungson, 2001).

Previous researches have proved that those barriers can be mitigated and correspondingly contribute to the firm-level performance, not only by optimizing the efficiency of transaction, but by mobilizing knowledge and information that are not easily codified (Tafti et al, 2013; Zollo et al, 2002). There are various mechanisms that have been proposed, such as selecting partners with moderate knowledge base or in the related industries (Sampson, 2007; Noseleit and de Faria, 2013), organizational structural design (Song and Song, 2010), and alliance management capabilities (Rothaermel and Deeds, 2006) to overcome the barriers. These mechanisms, nevertheless, sometimes are implausible in practice. For example, developing alliance management capabilities may be expensive since it requires sufficient relevant experience in forming and managing alliances (Anand and Khanna, 2000). Such experience is able to enhance the efficiency of knowledge integration or learning of the organizations (Liu and Ravichandran, 2015). Without the accumulated experience, allying firms are inability to develop strong capabilities to effectively manage alliances.

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exhibit superior and sustained firm performance (Santhanam and Hartono, 2003). But research regarding the role of IT capability on alliance context needs to be further explored even if the fact, that information technology has transformed the way that firms collaborate, is commonly understood.

Different theories, such as transaction cost theory (Gulati and Singh, 1998; Teece, 1992), the resource-based view (Eisenhardt and Schoonhoven, 1996; Mowery, Oxley and Silvermam, 1996; Chen and Chen 2003), and organizational learning theory (Kale, Singh and Perlmutter, 2000), have identified different partnering forms according to diverse interests of allying firms. As a result, the effects of IT capability on diverse alliances may not be the same. Surprisingly, little research has explored the role of IT capability on the impact of specific alliances on firm performance (Tafti et al, 2013). Two notable examples are Dong and Yang (2015) and Liu and Ravichandran (2015). Dong and Yang (2015) studied the role of IT in two organizational learning process—the knowledge alliance and the knowledge network. Liu and Ravichandran (2015) investigated the moderating effects of IT-enabled knowledge integration capability on the relationship between related and diversity alliance experience and a firm’s ex ante value gains. Yet, their studies focus on general alliances instead of R&D alliances.

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2. Theoretical Background and Hypotheses Development

2.1. R&D Alliances and Firm Performance

Strategic alliances are voluntary and formal agreement among two or more than two allying firms in order to exchange and share resources as well as knowledge (Gulati, 1998). In contemporary competitive environment, many firms choose to collaborate because the intensive competition enforces them to create and commercialize knowledge in a timely and cost-efficient manner (Sampson, 2007; Anand and Khanna, 2000).

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A large body of literatures in the management domain has explored the effects of R&D cooperation on firm performance, but the findings are kind of mixed (Belderbos et al, 2004; Sampson, 2007). Earlier research pays attention to the role of knowledge spillover between allying partners. Without cooperation and coordination, the knowledge stock tends to increase for competing firms because knowledge spillovers are considered involuntary at the time. Consequently, the firm’s R&D efforts may be disrupt and is unable to appropriate all of the returns, further lowering R&D investment levels. When firms collaborate on R&D, the problem is alleviated. R&D alliances help internalize the knowledge spillovers and reduce the disincentive effect of it (Belderbos et al, 2004; Amir et al, 2003). This stream does not consider that the level of knowledge spillovers can be affected by the R&D cooperation itself, making these studies rather implausible. Follow the analysis, recent research takes into account that alliances enable to increase knowledge transfers voluntarily among partners (Katsoulacos and Ulph, 1998). Cassiman et al. (2002) find that firms are ability to manage the knowledge spillovers through maximizing incoming spillovers by R&D collaboration and minimizing outgoing spillovers by investment in knowledge protection. But the limitation of this line of research is evident, for which mainly focus on the R&D cooperation with competitors while ignoring possible collaboration with non-direct competitors such as universities. More lately, many literatures in the management domain tend to learn R&D alliances in terms of knowledge exchange between partnering firms instead of internalizing involuntary knowledge spillovers. It is widely accepted that R&D cooperation helps firms defray costs and share risks when they undertake high-cost projects or very speculative strategic initiatives (Hagedoorn, 1993; Das and Teng, 2000). It has also been suggested that collaborative R&D as a source of competitive advantage allows for shortening product life cycle and accessing complementary resources, leading to a long lasting effects on firm performance (Belderbos et al, 2004).

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mentioned above. As Tafti et al. (2013) claim that information technology has already changed the way that firms to collaborate.

2.2. IT Capability

Adopting a resource-based perspective, resources tend to be a sustained competitive advantage to a firm if are valuable, rare, inimitable and non-substitutable and enable to keep firm differences stable over time (Barney, 1991). More broadly, resources can be distinguished into tangible, intangible, and personnel-based resources (Grant, 1991). Tangible resources refer to the financial capital and physical assets that are easily to be codified, transferred, and stored. Intangible resources involve assets such as brands and personnel-based resources encompass technical know-how, employee training, and the like. When all the resources are assembled to work together, the firm may create organizational capabilities which are benefit for developing competitive advantage (Bharadwaj, 2000). Regarding to Amit and Schoemaker (1993), organizational capability is associated with the firm’s ability to assimilate, integrate as well as exploit competences, and can be specified into marketing, operational, and IT capability (Grant, 1991). Also, because of rare and firm specific, IT capability serves as one of the potential source of firm’s competitive advantage (Mata et al, 1995).

Bharadwaj (2000) defines IT capability as a firm’s ability to mobilize and deploy IT-based resources to adjust other resources and capabilities. Combining Grant’s (1991) classification on resources, IT capability can be differentiated into IT infrastructure, IT-enable intangible resources, and human IT skills, respectively, each of which is a unique source for firms to create competitive advantage. Consequently, IT capability might have a mixed impact on the R&D alliances, depending on which underlying mechanisms of IT capability that the firm conducts to affect the firm performance.

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access customer information and their preferences with lower search costs (Straub and Watson, 2001).

2.3. The Role of IT Capability in R&D Alliances

Consistent with strategy and IT literatures, information technology has been found as an efficient and effective tool to reduce transaction and coordination costs in inter-organizational relationship (Mithas et al, 2012). R&D alliances that are more collaborative in nature include the sharing or tacit exchange of knowledge, and they need transformation and integration of products or processes which can be leveraged by IT capability (Tafti et al, 2013). IT cannot only be used to transfer knowledge and process information, but to produce knowledge from external sources such as network partners (Kleis et al, 2012), facilitating organizational learning consequently (Dong and Yang 2015). This IT-enabled knowledge capability is able to help firms codify information and knowledge and translate into the learning process (Liu and Ravichandran, 2015). During the process of organizational learning, firms are able to accumulate valuable knowledge and experience for the future need (Tippins and Sohi, 2003), thereby becoming a part of the entire organizational memory (Joshi et al, 2010).

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We propose that firms with superior IT capability in turn help R&D alliances facilitate firm performance by mitigate communication barriers and leverage value gains based on the assimilation and integration of related knowledge and information. Firms that fail to develop an IT capability will be at a comparative disadvantage. Thus, this leads us to the hypothesis:

H1: R&D alliances increase firm performance in firms with superior IT capability.

Despite the fact that R&D alliances help firms reduce costs and share risks when they undertake highly uncertain project (Das and Teng, 2000), information technology is another prerequisite for knowledge exchange and information processing especially for the arrival of the Internet (Tafti et al, 2013). Within an R&D alliance, cooperating partners not only collaborate, but also compete for acquiring as much resources as possible from other companies. In the previous section, we discuss a lot about the IT capability can promote firm performance by leveraging barriers that may hinder R&D alliances. The better the firm with strong IT capability, the more assets they can transfer and assimilate. However, if the firm fails to develop superior IT capability than its partners, this may influence the firm performance to some extent. Of course the most essential problem is associated with the knowledge transfer across as well as within the firm boundaries. In this situation, organizational barriers such as physical separation or cultural differences will hamper the efficiency or accuracy of transactions, and perhaps interrupt the tacit knowledge sharing in interfirm relationship (Song and Song, 2010). Particular in competition intensive industry, short product life cycles and the rapid pace of technological development require firms to create and commercialize knowledge in an efficient manner (Sampson, 2007). The lack of strong IT capability will become a handicap for the firm in the collaborative alliances since the knowledge exchange and information processing are largely influenced. Thus, this leads us to the hypothesis:

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3. Methodology

3.1. Data and Sample

In order to test our hypotheses, we separate the samples to compare the split two groups. As we need to explore whether firms with strong IT capability can contribute to the financial performance of R&D alliances. At the same time, we have to estimate the impact of R&D alliances on firm performance when firms without strong IT capability. So in our case, one sample refers to the sampled firms with superior IT capabilities. And the other sample of firms should not have strong IT capability. Further, we used this methodology was because the IT capability dummy we used, which we will explained in the following part. In this way, it was easy for us to split the sample to test and compare. In this study, we collected secondary data from several sources in the period of 2007 and 2009. According to Sabherwal and Sabherwal (2005), secondary data collection offers us with firms’ actual use of IT to support knowledge processing in a more objective way comparing to primary data collection, which involves the perception and preference of individual, leading to certain bias. Moreover, since we are intending to test IT across several years, and it is unreliable to collect the primary data when asking users about IT use in pastime because of difficulty in recalling experience over time.

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3.1.1. R&D alliances

As previous mentioned, SDC involves description about alliances, therefore we can collect the data such as types, or the number of specific alliance in certain year. In this research, we focus on the R&D alliances and the impact of IT capability on it, counting the number of continuous variables R&D and Non-R&D alliances for the firm in one year. For those firms with superior IT capability but not listed in the SDC data, we considered them that neither developing R&D alliances nor Non-R&D alliances for the three years, namely, the number is 0.

3.1.2. IT Capability

Combining IW 500 and SDC databases, we created IT capability dummy by coding firms in the sample list of IW 500 from 2007 to 2009 as 1 and coding other firms without information technology but in SDC as 0.

3.1.3. Firm Performance

This study measured the impact of IT on firm’s financial performance from the standpoint of selected partnering firms. Specifically, Return on Assets (ROA) is an accounting performance indicator widely used in management and finance literatures (Lin and Wu, 2010; Greve, 2003). Thus, we took operating income before depreciation as the firm’s annual earnings and then divide by its total assets as a result.

3.1.4. Control Variables

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Table 1 Industry Distribution of Sampled Observations

SIC2 Industry Sectors Observations Percentage

01 Agriculture, Forestry, Fishing 16 0.43

10-14 Mining 269 7.15

15-17 Construction 10 0.26

20-39 Manufacturing 1610 42.77

42-49 Transportation, communication,

electronic, gas and sanitary services 367 9.75

50-51 Whole trade 82 2.17

52-59 Retail trade 79 2.10

60-67 Finance, insurance and real

estate 589 15.65

70-87 Services 703 18.68

99 Public administration 39 1.04

Total 3764 100

3.2. Analysis Procedure

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4. Results

4.1. Hypotheses Testing

The result seems to indicate support for the two hypotheses. Table 2 provides the descriptive statistics and correlations for all variables except the year and industry dummy variables. In order to assess the extent of multicollinearity, we computed the variance inflation factor (VIF). The VIFs of all variables are significantly below the cut-off value of 10, means that the multicollinearity is not a problem in our models.

Table 2 Descriptive Statistics and Correlations

Mean SD (1) (2) (3) (4) (5) (6) (1) R&D Alliances 0.17 0.49 1 (2) IT Capability 0.13 0.34 -0.09** 1 (3) Debt Ratio 0.20 0.38 0.03* -0.02 1 (4) R&D Intensity 2.62 73.55 0.03 -0.01 -0.00 1 (5) LNAT 7.50 3.01 0.02 0.23** -0.01 -0.06** 1 (6) ROA -0.30 14.82 -0.03 0.01 0.00 -0.00 0.09** 1 Note: *. Correlation is significant at 5%; **. Correlation is significant at 1%.

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difficulties for the focal firm to overcome certain barriers between partnering firms such as transferring and learning tacit knowledge from and to other firms. Connect with our results, H2 was supported.

Table 3 Hypotheses Testing for Performance Impact: OLS Regression and Compared Groups

DV: ROA (Firm Performance)

Firms with IT Capability (H1) Firms without IT Capability (H2)

Model 1 Model 2 Model 1 Model 2

R&D Alliances 0.025* (0.012) -0.865 (0.621) LNAT 0.003 (0.002) (0.002)0.002 0.630***(0.112) 0.651***(0.113) Debt Ratio 0.005 (0.021) (0.021)0.009 (0.747)0.318 (0.746)0.340 R&D Intensity -0.148** (0.057) -0.16** (0.057) 0.001 (0.004) 0.001 (0.004) Industry Dummies

Yes Yes Yes Yes

Year Dummies Yes Yes Yes Yes

R-square 0.485 0.490 0.013 0.014

N 483 483 3120 3120

Note: standard errors are reported in parentheses. The coefficients for IT are not shown in this model because all the observations are in the same subgroup, namely, firms with or without IT capability.

*. Correlation is significant at 5%; **. Correlation is significant at 1%; ***. Correlation is significant at 0.1%.

4.2. Robustness Checks

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when the focal firm has superior IT capability. Otherwise, R&D alliances may be not a good option for the firm to increase its financial output.

Table 4 Clustering Firms

DV: ROA (Firm Performance)

Firms with IT Capability (H1) Firms without IT Capability (H2)

Model 1 Model 2 Model 1 Model 2

R&D Alliances 0.025* (0.011) -0.865 (0.832) LNAT 0.003 (0.004) 0.002 (0.004) 0.630 (0.492) 0.651 (0.511) Debt Ratio 0.005 (0.033) 0.009 (0.033) 0.318 (0.643) 0.340 (0.663) R&D Intensity -0.148 (0.116) -0.158 (0.114) 0.001 (0.002) 0.001 (0.002) Industry

Dummies Yes Yes Yes Yes

Year Dummies Yes Yes Yes Yes

R-square 0.485 0.490 0.013 0.014

N 483 483 3120 3120

Note: robust standard errors are reported in parentheses. The coefficients for IT are not shown in this model because all the observations are in the same subgroup, namely, firms with or without IT capability.

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5. Discussion

5.1. Main Findings

Research in the strategy and innovation literature has increasingly focused on the ability of firms effectively use collaborative strategies to create new knowledge and capabilities in a timely and cost-effective manner, yet understanding of how the cooperation and coordination can be enhanced by the usage of IT lags far behind. Although prior work has shown that IT capabilities do matter for firm performance (Bharadwaj, 2000; Santhanam and Hartono, 2003), surprisingly little evidence has emerged on the performance effect concerning alliance context. This paper attempts to fill an important gap and answer a question: Does R&D alliances increase firm performance in firm with superior IT capability? We primarily pay attention to the R&D alliances is because this type of alliances face much more barriers than the other type of alliances (non-R&D alliances) that are used to transfer highly codified capability rather than tacit knowledge across firm boundaries (Schilling and Phelps, 2007). Information technology tends to be more useful for R&D alliances than non-R&D alliances for its information processing nature. We extend and update previous studies by theorizing IT capability as an enabler to mitigate barriers between allying partners, for example, boundary-spanning information system allows a focal firm to establish the digital connection with external network (Kleis et al, 2012).

By comparing the business performance of IT leaders from 2007 to 2009 with that of control companies, we attempted to confirm the presence of the link between IT capability and business performance in R&D alliances. As our expectation, we find that IT capability has a positive impact on the R&D alliances through facilitating knowledge transfer and assimilate. Without utilizing superior IT capability, the positive effects of R&D alliances on firm performance may be weaker compare to that of firms with superior IT capability. One of important reasons is because cross-border boundaries knowledge sharing between partnering firms (Song and Song, 2010). Indeed, the knowledge-based view emphasizes tacit knowledge is difficult to transfer especially when it is complex and complicated, this is especially a problem when delivering and learning intangible knowledge from and to allying firms rather than within the firm (Kogut and Zander, 1992). Fortunately, those barriers or problems can be mitigated by leveraging the information technology and developing relevant capability, enhancing the financial performance of R&D alliances in the end.

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The research suggest that not all the firms efficiently and effectively take advantage of IT capability even if they have large investment on the area of IT. Consistent with Grant’s (1991) classification on resources, IT capability has been distinguished into IT infrastructure, IT-enable intangible resources, and human IT skills. IT infrastructure refers to the physical or tangible assets such as computer and communication technologies. In contrast, IT-enabled intangible involves knowledge assets, customer orientations and synergy. And the human IT skills usually comprise technical IT skills and managerial IT skills (Bharadwaj, 2000). As a result, IT capability might have a mixed impact on the R&D alliances, depending on which underlying mechanisms of IT capability that the firm conducts to affect the firm performance.

This paper thus contributes to the strategy and innovation literature by providing empirical support for the relationship between superior IT capability and the financial performance of R&D alliances. In particular, the finding that R&D alliances significantly increase the firm financial performance when the firm with superior IT capability, whereas compared to the other matched group, R&D alliances do not necessarily generate much higher revenue when the firm without superior IT capability.

5.2. Managerial Implications

By exploring the link between IT capability and the performance regarding R&D alliances, this study serves to inform business managers several managerial implications. In practice, many firms invest in R&D collaborations and information technology, but never capture the corresponding value in firm financial performance. They should initially identify which barriers that firms are facing, and then through which ways IT capability can be developed and used to mitigate those problems. From theoretical arguments, this study shows how complex the IT capability is and why such capability is difficult to create and takes time. In practice, there is little guidance for managers to develop IT capability even it captures attention recently (Bharadwaj, 2000). Solely investments in IT is not enough, managers should combine with other complementary resources so as to create IT capability better. For example, Freeny and Willcocks (1998) identify nine core elements as prerequisite for developing superior IT capability, that is, leadership, business systems thinking, relationship building, architecture planning, making technology work, informed buying, contract facilitation, contract monitoring, and vendor development.

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or functions embedded in the IT leaders. Finally, managers need to consider building IT capability as a continuous process on the basis of sustenance investment that firms like to make, since firms may fall into technological rigidity and fail to adapt the changing environment by quickly converting into a new IT system.

5.3. Limitations and Future Research

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6. Conclusion

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Acknowledgement

This paper is the final step in completing the master Strategic Innovation Management. During the process of finishing this master thesis, I was helped by several people who contribute the establishment of my thesis. I would like to use this opportunity to express my appreciation to these people who were involved in this project.

Firstly, I would like to thank supervisor Dr. Qi. (John) Dong for his continuous supports, excellent comment and the patience during this project. He gave me many good ideas in the beginning of this project, which helped me to establish the topic of the thesis. Additionally, Dr. Qi. (John) Dong enlightened me a lot during the data collection and analysis processes. He helped to ensure that my thesis was on the right track.

Next, I would like to thank Mr. Meng for his technical support during the data collection process. He provided me with step-by-step resources for organizing the large scale panel data by using the software Access. Finally, I would like to thank my parents and friends for their support and care while I was completing this master.

Manqing Tan

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References

Anand, B. N. and Khanna, T. (2000). Do Firms Learn to Create Value? The Case of Alliances. Strategic Management Journal, 21 (3), 295-315.

Amir, R., Evstigneev, I., and Wooders, J. (2003). Noncooperative versus Cooperative R&D with Endogenous Spillover Rates. Games and Economic Behavior, 42, 184-207.

Amit, R. and Schoemaker, P. H. (1993). Strategic Assets and Organizational Rent. Strategic Management Journal. 14, 33-46.

Barney, J. B. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management. 17, 99-120.

Belderbos, R., Carree, M., and Lokshin, B. (2004). Cooperative R&D and Firm Performance. Research Policy, 33, 1477-1492.

Bharadwaj, A. S. (2000). A Resource-Based Perspective on Information Technology Capability and Firm Performance: An Empirical Investigation. MIS Quarterly, 24 (1), 169-196.

Carr, N. G. (2003). It Doesn’t Matter. Harvard Business Review, 81 (5), 41-49.

Cassiman, B., Perez-Castrillo, D., and Veugelers, R. (2002). Endogeneizing Know-how Flows through the Nature of R&D Investments. International Journal of Industrial Organization, 20, 775-799.

Chae, H. C., Koh, C. E., and Prybutok, V. R. (2014). Information Technology Capability and Firm Performance: Contradictory Findings and Their Possible Causes. MIS Quarterly, 38 (1), 305-325.

Chen, H. and Chen, T, J. (2003). Governance Structures in Strategic Alliances: Transaction Cost versus Resource-Based Perspective. Journal of World Business, 38, 1-13.

Dacin, M. T., Oliver, C., and Roy, J. P. (2007). The Legitimacy of Strategic Alliances: An Institutional Perspective. Strategic Management Journal, 28, 169-187.

Das, T. K. and Teng, B. S. (2000). A Resource-based Theory of Strategic Alliances. Journal of Management, 26 (1), 31-60.

(23)

Eisenhardt, K. M. and Schoonhoven, C. B. (1996). Resource-based View of Strategic Alliances Formation: Strategic and Social Effects in Entrepreneurial Firms. Organization Science, 7, 136-150.

Freeny, D. E. and Willcocks, L. (1998). Core IS Capabilities for Exploiting Information Technology. Sloan Management Review, Spring, 9-21.

Gosain, S., Malhotra, A., and El Sawy, O. A. (2005). Coordinating for Flexibility in E-Business Supply Chains. Journal of Management Information Systems, 21 (2), 7-45. Grant, R. M. (1991). The Resource-Based Theory of Competitive Advantage. California

Management Review, 33 (3), 114-135.

Greve, R. M. (2003). Investment and the Behavioral Theory of the Firm: Evidence from Shipbuilding. Industrial and Corporate Change, 12 (5), 1051-1076.

Gulati, R. (1995). Does Familiarity Breed Trust? The Implications of Repeated Ties for Contractual Choice in Alliances. Academy of Management Journal, 38, 85-112. Gulati, R. (1998). Alliances and Networks. Strategic Management Journal, 19 (4),

293-317.

Gulati, R. and Singh, H. (1998). The Architecture of Cooperation: Managing Coordination Costs and Appropriation Concerns in Strategic Alliances. Administrative Science Quarterly, 43 (4), 781-914.

Hagedoorn, J. (1993). Understanding the Rationale of Strategic Technology Partnering: Interorganizational Modes of Cooperation and Sectoral Differences. Strategic Management Journal, 14 (5), 371-385.

Hemmings, R. (2014). Issues in the Planning, Analysis, and Interpretation of Subgroup Analyses in Confirmatory Clinical Trials. Journal of Biopharmaceutical Statistics, 24, 4-18.

Hitt, L. and Brynjolfsson, E. (1996). Productivity, Business Profitability, and Consumer Surplus: Three Different Measures of Information Technology Value. MIS Quarter, 20 (2), 121-142.

Jain, A. K. (2010). Data Clustering: 50 Years beyond K-Means. Pattern Recognition Letters, 31, 651-666.

(24)

Kale, P., Singh, H., and Perlmutter, H. (2000). Learning and Protection of Proprietary Assets in Strategic Alliances: Building Relational Capital. Strategic Management Journal, 21 (3), 217-237.

Kane, G. C. and Alavi, M. (2007). Information Technology and Organizational Learning: An Investigation of Exploration and Exploitation Process. Organization Science, 18 (5), 796-812.

Katsoulacos, Y. and Ulph, D. (1998). Endogenous Spillovers and the Performance of Research Joint Ventures. Journal of Industrial Economics, 46, 333-357.

Kleis, L., Chwelos, P., Ramirez, R. V., and Cockburn, I. (2012). Information Technology and Intangible Output: The Impact of IT Investment on Innovation Productivity. Information Systems Research, 23 (1), 42-59.

Kogut, B. and Zander, U. (1992). Knowledge of the Firm, Combinative Capabilities and the Replication of Technology. Organization Science, 3, 383-397.

Lin, B. W. and Wu, C. H. (2010). How Does Knowledge Depth Moderate the Performance of Internal and External Knowledge Sourcing Strategies. Technovation, 30, 582-589.

Liu, Y. and Ravichandran, T. (2015). Alliance Experience, IT-Enabled Knowledge Integration and Ex Ante Value Gains. Organization Science, 26 (2), 511-530.

Mata, F. J., Fuerst, W. L., and Barney, J. B. (1995). Information Technology and Sustained Competitive Advantage: A Resource-based Analysis. MIS Quarterly, 19 (4), 487-505.

Mithas, S., Tafti, A., Bardhan, I., and Goh, J. M. (2012). Information Technology and Firm Profitability: Mechanisms and Empirical Evidence. MIS Quarter, 36 (1), 205-224.

Mowery, D. C., Oxley, J. E., and Silverman, B. (1996). Strategic Alliances and Interfirm Knowledge Transfer. Strategic Management Journal, 17, 77-91.

Noseleit, F. and de Faria, P. (2013). Complementarities of Internal R&D and Alliances with Different Partner Types. Journal of Business Research, 66, 2000-2006.

Oxley, J. E. (1997). Appropriability Hazards and Governance in Strategic Alliances: A Transaction Cost Approach. Journal of Law, Economics and Organization, 13, 387-409.

(25)

Porter, M. (2001). Strategy and the Internet. Harvard Business Review, 79 (3), 62-78. Power, W. W., Koput, K. W., and Smith-Doerr, L. (1996). Interorganizational

Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology. Administrative Science Quarterly, 41, 116-145.

Rothaermel, F. T. and Deeds, D. L. (2006). Alliance Type, Alliance Experience and Alliance Management Capability in High-Technology Ventures. Journal of Business Venturing, 21, 429-460.

Sabherwal, R. and Sabherwal, S. (2005). Knowledge Management Using Information Technology: Determinants of Short-Term Impact on Firm Value. Decision Sciences, 36 (4), 531-567.

Sampson, R. (2007). R&D Alliances and Firm Performance: The Impact of Technological Diversity and Alliance Organization on Innovation. Academy of Management Journal, 50 (2), 364-386.

Santhanam, R. and Hartono, E. (2003). Issues in Linking Information Technology Capability to Firm Performance. MIS Quarterly, 27 (1), 125-153.

Schilling, M. A. and Phelps, C. C. (2007). Interfirm Collaboration Networks: The Impact of Large-Scale Network Structure on Firm Innovation. Management Science, 53 (7), 1113-1126.

Song, M., Berends, H., van der Bij, H., and Weggeman, M. (2007). The Effect of IT and Co-location on Knowledge Dissemination. The Journal of Product Innovation Management, 24, 52-68.

Song, L. Z. and Song, M. (2010). The Role of Information Technology in Enhancing R&D-Marketing Integration: An Empirical Investigation. The Journal of Product Innovation Management, 27, 382-401.

Stoel, L. Z. and Muhanna, W. (2009). IT Capabilities and Firm Performance: A Contingency Analysis of the Role of Industry and IT Capability Type. Information and Management, 46 (3), 181-189.

Straub, D. W. and Watson, R. T. (2001). Research Commentary: Transformational Issues in Researching IS and Net-Enabled Organizations. Information Systems Research, 12 (4), 337-345.

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Teece, D. J. (1992). Competition, Cooperation, and Innovation: Organizational Arrangements for Regimes of Rapid Technological Progress. Journal of Economic Behavior and Organization, 18, 1-25.

Tippins, M. J. and Sohi, R. S. (2003). IT Competency and Firm Performance: Is Organizational Learning a Missing Link? Strategic Management Journal, 24 (8), 745-761.