Abstract
Interfirm relationships are crucial to our understanding of firms’ collective and interactive behavior. Many information systems-related phenomena, including the diffusion of innovations, standard alliances, technology collaboration, and outsourcing, involve a multitude of relationships between firms. This study proposes a latent space approach to model temporal change in a dual-view interfirm network. We assume that interfirm relationships depend on an underlying latent space; firms that are close to each other in the latent space are more likely to develop a relationship. We construct the latent space by embedding two dynamic networks of firms in an integrated manner, resulting in a more comprehensive view of an interfirm relationship. We validate our approach by introducing three business measures derived from the latent space model to study alliance formation and stock comovement. We illustrate how the trajectories of firms provide insights into alliance activities. We also show that our proposed measures have strong predictive power on stock comovement. We believe the proposed approach enriches the methodology toolbox of IS researchers in studying interfirm relationships.
- K. Chen, P. Luo, D. Xu, and H. Wang. 2016. The dynamic predictive power of company comparative networks for stock sector performance. Info. Manage. 53, 8 (2016), 1006--1019. DOI:10.1016/j.im.2016.07.005Google ScholarDigital Library
- H. R. Greve, J. A. C. Baum, H. Mitsuhashi, and T. J. Rowley. 2010. Built to last but falling apart: Cohesion, friction, and withdrawal from interfirm alliances. Acad. Manage. J. 53, 2 (2010), 302--322. DOI:10.5465/amj.2010.49388955Google ScholarCross Ref
- A. Rai, P. A. Pavlou, G. Im, and S. Du. 2004. Interfirm IT capability profiles and communications for cocreating relational value: Evidence from the logistics industry. MIS Q. 36, 1 (2004), 233--262, 2012, DOI:10.2307/41410416Google ScholarCross Ref
- D. Straub, A. Rai, and R. Klein. 2004. Measuring firm performance at the network level: A nomology of the business impact of digital supply networks. J. Manage. Info. Syst. 21, 1 (2004), 83--114, 2004, DOI:10.1080/07421222.2004.11045790Google ScholarDigital Library
- F. Wu and S. T. Cavusgil. 2006. Organizational learning, commitment, and joint value creation in interfirm relationships. J. Bus. Res. 59, 1 (2006), 81--89, 2006, DOI:10.1016/j.jbusres.2005.03.005Google ScholarCross Ref
- P. D. Hoff, A. E. Raftery, and M. S. Handcock. 2002. Latent space approaches to social network analysis. J. Am. Stat. Assoc. 97, 460 (2002), 1090--1098. DOI:10.1198/016214502388618906.Google ScholarCross Ref
- I. Gollini and T. B. Murphy. 2016. Joint modeling of multiple network views. J. Comput. Graph. Stat. 25, 1 (2016), 246--265. DOI:10.1080/10618600.2014.978006Google ScholarCross Ref
- M. Salter-Townshend and T. H. McCormick. 2017. Latent space models for multiview network data. Ann. Appl. Stat. 11, 3 (2017), 1217--1244, 2017, DOI:10.1214/16-AOAS955.Google ScholarCross Ref
- T. Chuluun, A. Prevost, and A. Upadhyay. 2017. Firm network structure and innovation. J. Corp. Financ. 44, (2017), 193--214. DOI:10.1016/j.jcorpfin.2017.03.009Google ScholarCross Ref
- X. Zhang and V. Venkatesh. 2013. Explaining employee job performance: The role of online and offline workplace communication networks. MIS Q. 37, 3 (2013), 695--722. DOI:10.25300/MISQ/2013/37.3.02Google ScholarDigital Library
- T. A. Sykes and V. Venkatesh. 2017. Explaining post-implementation employee system use and job performance: Impacts of the content and source of social network ties. MIS Q. 41, 3 (2017), 917--936, 2017, DOI:10.25300/MISQ/2017/41.3.11Google ScholarDigital Library
- Z. Ma, O. R. L. Sheng, and G. Pant. 2009. Discovering company revenue relations from news: A network approach. Decis. Support Syst. 47, 4 (2009), 408--414, 2009, DOI:10.1016/j.dss.2009.04.007Google ScholarDigital Library
- G. Pant and O. R. L. Sheng. 2015. Web footprints of firms: Using online isomorphism for competitor identification. Info. Syst. Res. 26, 1 (2015), 188--209. DOI:10.1287/isre.2014.0563Google ScholarDigital Library
- Z. Shi, G. M. Lee, and A. B. Whinston. 2016. Toward a better measure of business proximity: Topic modeling for industry intelligence. MIS Q. 40, 4 (2016), 1035--1056, 2016, DOI:10.25300/misq/2016/40.4.11Google ScholarDigital Library
- R. C. Basole, T. Major, and A. Srinivasan. 2017. Understanding alliance portfolios using visual analytics. ACM Trans. Manage. Info. Syst. 8, 4, 12 2017. DOI:10.1145/3086308Google ScholarDigital Library
- H. Mitsuhashi and H. Greve. 2009. A matching theory of alliance formation and organizational success: Complementarity and compatibility. Acad. Manage. J. 52, 5 (2009), 975--995. DOI:10.5465/AMJ.2009.44634482Google ScholarCross Ref
- M. A. Schilling and C. C. Phelps. 2007. Interfirm collaboration networks: The impact of large-scale network structure on firm innovation. Manage. Sci. 53, 7 (2007), 1113--1126, 2007, DOI:10.1287/mnsc.1060.0624Google ScholarDigital Library
- T. E. Stuart. 1998. Network positions and propensities to collaborate: An investigation of strategic alliance formation in a high-technology industry. Adm. Sci. Q. 43, 3 (1998), 668--698, 1998, DOI:10.2307/2393679Google ScholarCross Ref
- N. Barberis, A. Shleifer, and J. Wurgler. 2005. Comovement. J. Financ. Econ. 75 (2005), 283--317. DOI:10.1016/j.jfineco.2004.04.003Google ScholarCross Ref
- H. Chen, R. H. L. Chiang, and V. C. Storey. 2012. Business intelligence and analytics: From big data to big impact. MIS Q. 36, 4 (2012), 1165--1188. DOI:10.2307/41703503Google ScholarCross Ref
- M. Chau and J. Xu. 2012. Business intelligence in blogs: Understanding consumer interactions and communities. MIS Q. 36, 4 (2012), 1189--1216, 2012, DOI:10.2307/41703504Google ScholarCross Ref
- Hu Zhao, Hua and Wong. 2012. Network-based modeling and analysis of systemic risk in banking systems. MIS Q. 36, 4 (2012), 1269--1291, 2012, DOI:10.2307/41703507Google ScholarCross Ref
- K. Zhang, S. Bhattacharyya, and S. Ram. 2016. Large-scale network analysis for online social brand advertising. MIS Q. 40, 4 (2016), 849--868. DOI:10.25300/misq/2016/40.4.03Google ScholarCross Ref
- R. Agarwal and V. Dhar. 2014. Big data, data science, and analytics: The opportunity and challenge for IS research. Info. Syst. Res. 25, 3 (2014), 443--448. DOI:10.1287/isre.2014.0546Google ScholarDigital Library
- L. Qiu, H. Rui, and A. Whinston. 2014. The impact of social network structures on prediction market accuracy in the presence of insider information. J. Manage. Info. Syst. 31, 1 (2014), 235--268. DOI:10.2753/MIS0742-1222310107Google ScholarCross Ref
- L. Qiu, H. Rui, and A. Whinston. 2014. Effects of social networks on prediction markets: Examination in a controlled experiment. J. Manage. Info. Syst. 30, 4 (2014), 235--268, 2014, DOI:10.2753/MIS0742-1222300409Google ScholarCross Ref
- L. Yan, J. Peng, and Y. Tan. 2015. Network dynamics: How can we find patients like us? Info. Syst. Res. 26, 3 (2015), 496--512. DOI:10.1287/isre.2015.0585Google ScholarDigital Library
- J. M. Goh, G. G. Gao, and R. Agarwal. 2016. The creation of social value: Can an online health community reduce rural-urban health disparities?. MIS Q. 40, 1 (2016), 247--263. DOI:10.25300/MISQ/2016/40.1.11Google ScholarDigital Library
- Z. Shi and A. B. Whinston. 2013. Network structure and observational learning: Evidence from a location-based social network. J. Manage. Info. Syst. 30, 2 (2013), 185--212. DOI:10.2753/MIS0742-1222300207Google ScholarCross Ref
- P. Bhattacharya, T. Q. Phan, X. Bai, and E. M. Airoldi. 2019. A coevolution model of network structure and user behavior: The case of content generation in online social networks. Info. Syst. Res. 30, 1 (2019), 117--132. DOI:10.1287/isre.2018.0790Google ScholarCross Ref
- G. M. Lee, L. Qiu, and A. B. Whinston. 2016. A friend like me: modeling network formation in a location-based social network. J. Manage. Info. Syst. 33, 4 (2016), 1008--1033. DOI:10.1080/07421222.2016.1267523Google ScholarCross Ref
- B. Zhang, P. A. Pavlou, and R. Krishnan. 2018. On direct vs. indirect peer influence in large social networks. Info. Syst. Res. 29, 2 (2018), 292--314. DOI:10.1287/isre.2017.0753Google ScholarDigital Library
- W. Zhang, R. Y. K. Lau, Y. Xia, C. Li, and W. M. Li. 2012. Latent business networks mining: A probabilistic generative model. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI’12). DOI:10.1109/WI-IAT.2012.195Google ScholarCross Ref
- K. Li, J. Qiu, and J. Wang. 2019. Technology conglomeration, strategic alliances, and corporate innovation. Manage. Sci. 65, 11 (2019), 5065--5090. DOI:10.1287/mnsc.2018.3085Google ScholarDigital Library
- I. P. Mahmood, H. Zhu, and E. J. Zajac. 2011. Where can capabilities come from? Network ties and capability acquisition in business groups. Strateg. Manage. J. 32, 8, (2011), 820--848. DOI:10.1002/smj.911Google ScholarCross Ref
- T. Rowley, D. Behrens, and D. Krackhardt. 2000. Redundant governance structures: An analysis of structural and relational embeddedness in the steel and semiconductor industries. Strateg. Manage. J. 21, 3 (2000), 369--386. DOI:10.1002/(SICI)1097-0266(200003)21:3<369::AID-SMJ93>3.0.CO;2-MGoogle ScholarCross Ref
- R. S. Burt. 1995. Structural Holes: The Social Structure of Competition. Harvard University Press, Cambridge, MA.Google Scholar
- A. Tiwana. 2008. Do bridging ties complement strong ties? An empirical examination of alliance ambidexterity. Strateg. Manage. J. 29, 3 (2008), 251--272, 2008, DOI:10.1002/smj.666Google ScholarCross Ref
- H. Yang, Z. Lin, and Y. Lin. 2010. A multilevel framework of firm boundaries: Firm characteristics, dyadic differences, and network attributes. Strateg. Manage. J. 31, 3 (2010), 237--261, 2010, DOI:10.1002/smj.815Google ScholarCross Ref
- J. Yu, B. A. Gilbert, and B. M. Oviatt. 2011. Effects of alliances, time, and network cohesion on the initiation of foreign sales by new ventures. Strateg. Manage. J. 32, 4 (2011), 434--446. DOI:10.1002/smj.884Google ScholarCross Ref
- Z. Ma, G. Pant, and O. R. L. Sheng. 2011. Mining competitor relationships from online news: A network-based approach. Electron. Commer. Res. Appl. 10, 4 (2011), 418--427. DOI:10.1016/j.elerap.2010.11.006Google ScholarDigital Library
- A. Agarwal, A. C. M. Leung, P. Konana, and A. Kumar. 2017. Cosearch attention and stock return predictability in supply chains. Info. Syst. Res. 28, 2 (2017), 265--288. DOI:10.1287/isre.2016.0656Google ScholarDigital Library
- A. C. M. Leung, A. Agarwal, P. Konana, and A. Kumar. 2017. Network analysis of search dynamics: The case of stock habitats. Manage. Sci. 63, 8 (2017), 2667--2687. DOI:10.1287/mnsc.2016.2470Google ScholarDigital Library
- D. C. Mowery, J. E. Oxley, and B. S. Silverman. 1998. Technological overlap and interfirm cooperation: Implications for the resource-based view of the firm. Res. Policy 27, 5 (1998), 507--523. DOI:10.1016/S0048-7333(98)00066-3Google ScholarCross Ref
- D. M. Blei, A. Y. Ng, and M. I. Jordan. 2003. Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993--1022. DOI:10.1016/b978-0-12-411519-4.00006-9Google ScholarCross Ref
- A. Pehrsson. 2006. Business relatedness and performance: A study of managerial perceptions. Strateg. Manage. J. 27, 3 (2006), 265--282, 2006, DOI:10.1002/smj.516Google ScholarCross Ref
- J. Tang and W. G. Rowe. 2012. The liability of closeness: Business relatedness and foreign subsidiary performance. J. World Bus. 47, 2 (2012), 288--296. DOI:10.1016/j.jwb.2011.04.016Google ScholarCross Ref
- R. El-Khatib, K. Fogel, and T. Jandik. 2015. CEO network centrality and merger performance. J. Financ. Econ. 116, 2 (2015), 349--382. DOI:10.1016/j.jfineco.2015.01.001Google ScholarCross Ref
- S. Paruchuri. 2010. Intraorganizational networks, interorganizational networks, and the impact of central inventors: A longitudinal study of pharmaceutical firms. Organ. Sci. 21, 1 (2010), 63--80. DOI:10.1287/orsc.1080.0414Google ScholarDigital Library
- K. G. Provan, A. Fish, and J. Sydow. 2007. Interorganizational networks at the network nevel: A review of the empirical literature on whole networks. J. Manage. 33, 3 (2007), 479--516. DOI:10.1177/0149206307302554Google ScholarCross Ref
- W. Stam and T. Elfring. 2008. Entrepreneurial orientation and new venture performance: The moderating role of intra- and extraindustry social capital. Acad. Manage. J. 51, 1 (2008), 97--111. DOI:10.5465/AMJ.2008.30744031Google ScholarCross Ref
- S. P. Borgatti. 2005. Centrality and network flow. Soc. Netw. 27, 1 (2005), 55--71. DOI:10.1016/j.socnet.2004.11.008Google ScholarCross Ref
- P. Bonacich. 1987. Power and centrality: A family of measures. Am. J. Sociol. 92, 5 (1987), 1170--1182. DOI:10.1086/228631Google ScholarCross Ref
- A. Anagnostopoulos, R. Kumar, and M. Mahdian. 2008. Influence and correlation in social networks. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 7—15. DOI:10.1145/1401890.1401897Google ScholarDigital Library
- D. K. Sewell and Y. Chen. 2015. Latent space models for dynamic networks. J. Am. Stat. Assoc. 110, 512 (2015), 1646--1657. DOI:10.1080/01621459.2014.988214Google ScholarCross Ref
- R. Huggins and A. Johnston. 2010. Knowledge flow and inter-firm networks: The influence of network resources, spatial proximity and firm size. Entrep. Reg. Dev. 22, 5 (2010), 457--484. DOI:10.1080/08985620903171350Google ScholarCross Ref
- J. He, H. Huang, and W. Wu. 2018. Influence of interfirm brand values congruence on relationship qualities in B2B contexts. Ind. Mark. Manage. 72, 161--173, 2018, DOI:10.1016/j.indmarman.2018.02.015Google ScholarCross Ref
- H. C. Chang and C. H. Lin. 2008. Interfirm influence strategies and their impact on developing buyer-supplier relationships. Int. J. Commer. Manage. 18, 1 (2008), 10--30. DOI:10.1108/10569210810871461Google ScholarCross Ref
- G. L. Frazier and J. O. Summers. 1984. Interfirm influence strategies and their application within distribution channels. J. Mark. 48, 3 (1984), 43--55. DOI:10.2307/1251328Google ScholarCross Ref
- D. K. Sewell and Y. Chen. 2016. Latent space models for dynamic networks with weighted edges. Soc. Netw. 44, 105--116, 2016, DOI:10.1016/j.socnet.2015.07.005Google ScholarCross Ref
- M. O. Jackson, T. Rodriguez-Barraquer, and X. Tan. 2012. Social capital and social quilts: Network patterns of favor exchange. Am. Econ. Rev. 102, 5 (2012), 1857--1897. DOI:10.1257/aer.102.5.1857Google ScholarCross Ref
- P. J. Green, K. Łatuszyński, M. Pereyra, and C. P. Robert. 2015. Bayesian computation: A summary of the current state, and samples backwards and forwards. Stat. Comput. 25, 4 (2015), 835--862, 2015, DOI:10.1007/s11222-015-9574-5Google ScholarDigital Library
- M. Quiroz, R. Kohn, M. Villani, and M. N. Tran. 2019. Speeding Up MCMC by efficient data subsampling. J. Am. Stat. Assoc. 114, 526 (2019), 1--13. DOI:10.1080/01621459.2018.1448827Google ScholarCross Ref
- X. Ding, Y. Zhang, T. Liu, and J. Duan. 2014. Using structured events to predict stock price movement: An empirical investigation. In –Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’14). 1415--1425. DOI:10.3115/v1/d14-1148Google ScholarCross Ref
- R. Cowan and N. Jonard. 2009. Knowledge portfolios and the organization of innovation networks. Acad. Manage. Rev. 34, 2 (2009), 320--342. DOI:10.5465/amr.2008.0052Google ScholarCross Ref
- U. Brandes. 2008. On variants of shortest-path betweenness centrality and their generic computation. Soc. Networks, 30, 136--145, 2008, DOI:10.1016/j.socnet.2007.11.001Google ScholarCross Ref
- C. Andrieu and J. Thoms. 2008. A tutorial on adaptive MCMC. Stat. Comput. 18, 4 (2008), 343--373. DOI:10.1007/s11222-008-9110-yGoogle ScholarDigital Library
- J. K. Kruschke and T. M. Liddell. 2018. The bayesian new statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychon. Bull. Rev. 25, 1 (2018), 178--206. DOI:10.3758/s13423-016-1221-4Google ScholarCross Ref
- J. Geweke. 1992. Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments. Clarendon Press, Oxford.Google Scholar
- J. J. Bosma, M. Koetter, and M. Wedow. 2019. Too connected to fail? inferring network ties from price co-movements. J. Bus. Econ. Stat. 37, 1 (2019), 67--80. DOI:10.1080/07350015.2016.1272459Google ScholarCross Ref
- D. Barker and T. Loughran. 2007. The geography of S8P 500 stock returns. J. Behav. Financ. 8, 4 (2007), 177--190. DOI:10.1080/15427560701684884Google ScholarCross Ref
- I. Cooper and R. Priestley. 2016. The expected returns and valuations of private and public firms. J. Financ. Econ. 120, 1 (2016), 41--57. DOI:10.1016/j.jfineco.2016.01.023Google ScholarCross Ref
- S. Bruque, J. Moyano, and J. Eisenberg. 2009. Individual adaptation to IT-induced change: The role of social networks. J. Manage. Info. Syst. 25, 3 (2009), 177--206. DOI:10.2753/MIS0742-1222250305Google ScholarDigital Library
- W. Van Osch and C. W. Steinfield. 2018. Strategic visibility in enterprise social media: Implications for network formation and boundary spanning. J. Manage. Info. Syst. 35, 2 (2018), 647--682, 2018, DOI:10.1080/07421222.2018.1451961Google ScholarCross Ref
- A. Suh, K. S. Shin, M. Ahuja, and M. Kim. 2011. The influence of virtuality on social networks within and across work groups: A multilevel approach. J. Manage. Info. Syst. 28, 1 (2011), 351--386. DOI:10.2753/MIS0742-1222280111Google ScholarDigital Library
- S. Wattal, P. Racherla, and M. Mandviwalla. 2010. Network externalities and technology use: A quantitative analysis of intraorganizational blogs. J. Manage. Info. Syst. 27, 1 (2010), 145--174. DOI:10.2753/MIS0742-1222270107Google ScholarDigital Library
- C. Andrieu, N. De Freitas, A. Doucet, and M. I. Jordan. 2003. An introduction to MCMC for machine learning. Mach. Learn. 50, 5--43, 2003, DOI:10.1023/A:1020281327116Google ScholarCross Ref
- P. Sarkar and A. W. Moore. 2005. Dynamic social network analysis using latent space models. ACM SIGKDD Explor. Newsl. 7, 2 (2005), 31--40. DOI:10.1145/1117454.1117459Google ScholarDigital Library
- A. E. Raftery, X. Niu, P. D. Hoff, and K. Y. Yeung. 2012. Fast inference for the latent space network model using a case-control approximate likelihood. J. Comput. Graph. Stat. 21, 901--919, 2012, DOI:10.1080/10618600.2012.679240Google ScholarCross Ref
Index Terms
- A Latent Space Modeling Approach to Interfirm Relationship Analysis
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