Modelling willingness to pay for improved public transport services: The challenges of non-response to stated preference hypothetical questions

The paper focuses on the modelling attempt of willingness to pay for an improved bus service in selected cities and towns of Malaysia. Using responses from onboard intercept surveys, 1,130 samples of bus passengers have been analysed so as to arrive at a simplified model of how passengers trade off their money with possible upgrading of bus services elements. The willingness to pay among these bus riders was very low, despite the high expectation of improvements aspired by them. For service providers, fares are a function of travel time, travel distance and other operating costs. For passengers, the utility function is explained by costs, time, distance and various latent parameters. This paper highlights the significant results of chi-square analysis at various confidence levels. However, modelling the exact utility function of preferences for staggered increased in fares could not be carried out successfully at 95 percent confidence level, due to the relatively small number of respondents stating their and/or undecided response to willingness to pay for the additional fare rate. The issue of non-response to hypothetical survey questions is also raised, explaining the difficulties in modelling this choice behaviour

Study of Influencing Factors on Users’ Knowledge Contribution Behaviors in Social Q & A Communities

Social Q&A communities are important platforms for knowledge sharing among online users nowadays. Therefore, it is of theoretical and practical significance to understand the motivations behind users’ knowledge contribution behaviors in social Q&A communities. Drawing on a dataset from Stack Overflow, one of the largest Q&A sites worldwide, this paper aims to explore the factors that may influence users’ knowledge contribution behaviors. In particular, we examined factors related to three aspects: social interactivity, social capital and questions readability. Based on related theories we proposed several hypothesis and then tested these hypothesis using an econometric model. Our research results established the relationship between user’s knowledge contribution behaviors and factors related to social interactivity, social capital and questions readability. This paper contributes to literature related to studies on Social Q&A communities

FACTORS INFLUENCING USER’S CONTINUANCE INTENTION ON PAID QUESTION AND ANSWER SERVICE ----A STUDY ON WEIBO IN CHINA

This thesis addresses the research question “Why do users continue to use paid Q&A in China” by means showed below: First, this research introduces research background of paid Q&A in China and raises corresponding research question and highlights the research significance of this thesis topic; Second, the author concludes previous research on paid Q&A in aspects of Q&A system, paid subscription and sharing economy, and finds that most of prior research focuses on exploring the influence of usefulness but not enjoyment on the users’ willingness of continuing using a paid Q&A system; Third, the thesis introduces the VAM theory and build a modified model based on it, this modified model highlights the importance of pleasure on users’ continuance intention in using paid Q&A; Finally, the empirical study combining an Exploratory Factor Analysis and a Confirmatory Factor Analysis proves that, after integrating factors extracted from previous research and the proposed model, the research is tested to be explanatorily capable and hypotheses related to the model are mostly proved to be supported. As a conclusion, this study conducts an investigation on the constructs and related theories that influence users’ continuance intention to use paid Q&A, from a hedonic perspective. In this thesis, VAM theory is selected as the prototype of proposed research model which reveals factors affecting users’ continuance intention to use a Chinese paid Q&A product named Weibo Paid Q&A. In this thesis, the proposed model makes predictions that the constructs perceived fee and community atmosphere along with perceived enjoyment construct have critical effect on users’ continuance willingness in using Weibo Paid Q&A in China. With the assistance of PLS–SEM, this study analyzes data collected from users in WPQA, the empirical study verifies that users' continuance intention is assuredly dependent on perceived fee and community atmosphere along with perceived enjoyment. The study also reveals that quality of answerers and quality of answer positively exert significant influences on perceived enjoyment

Business process improvement and the knowledge flows that cross a private online social network: An insurance supply chain case

[EN] This paper analyses how the knowledge shared between employees and suppliers within a private enterprise social network affects process improvement. Data was collected from internal documents, and the internal and external enterprise social networks used by an international insurance company; the average cycle time for handling 8494 claims and 3240 messages posted on the internal and external social networks was analysed. Social network analysis techniques were combined with principal component analysis and structural equation modeling, and the results demonstrate that the knowledge shared within the internal and external social network can explain 35.10% of process improvement variability, while the knowledge shared within the internal social network explains 89.90% of external social network variability. The analysis also demonstrates that: (i) the knowledge shared among employees positively affects process improvement; (ii) the knowledge shared among suppliers negatively affects process improvement; and (iii) the knowledge shared among employees positively affects the knowledge shared among supply chain members. These findings have theoretical and practical implications. They extend the literature in the knowledge management and information management field by offering empirical evidence of how the knowledge shared through an enterprise social network affects business process improvement, using the objective data provided by Yammer. They also provide a strategic tool for managers that will allow them to better understand how they can use the enterprise social network for business processes improvement.The research reported in this paper is supported by the European Commission for the project "Engaging in Knowledge Networking via an interactive 3D social Supplier Network (KNOWNET)" (FP7-PEOPLE-2013-IAPP 324408)".Leon, R.; Rodríguez Rodríguez, R.; Gómez-Gasquet, P.; Mula, J. (2020). Business process improvement and the knowledge flows that cross a private online social network: An insurance supply chain case. Information Processing & Management. 57(4):1-16. https://doi.org/10.1016/j.ipm.2020.102237S116574Aboelmaged, M. G. (2018). Knowledge sharing through enterprise social network (ESN) systems: motivational drivers and their impact on employees’ productivity. Journal of Knowledge Management, 22(2), 362-383. doi:10.1108/jkm-05-2017-0188Al Saifi, S. A., Dillon, S., & McQueen, R. (2016). The relationship between face to face social networks and knowledge sharing: an exploratory study of manufacturing firms. Journal of Knowledge Management, 20(2), 308-326. doi:10.1108/jkm-07-2015-0251Alavi, M., & Leidner, D. E. (2001). Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues. MIS Quarterly, 25(1), 107. doi:10.2307/3250961Al-Thuhli, A., Al-Badawi, M., Baghdadi, Y., & Al-Hamdani, A. (2017). A Framework for Interfacing Unstructured Data Into Business Process From Enterprise Social Networks. International Journal of Enterprise Information Systems, 13(4), 15-30. doi:10.4018/ijeis.2017100102Amaratunga, D., Baldry, D., Sarshar, M., & Newton, R. (2002). Quantitative and qualitative research in the built environment: application of «mixed» research approach. Work Study, 51(1), 17-31. doi:10.1108/00438020210415488Aslam, U., Muqadas, F., Imran, M. K., & Ubaid-Ur-Rahman, U.-U.-R. (2018). Exploring the sources and role of knowledge sharing to overcome the challenges of organizational change implementation. International Journal of Organizational Analysis, 26(3), 567-581. doi:10.1108/ijoa-07-2017-1189Bakotic, D., & Krnic, A. (2017). Exploring the relationship between business process improvement and employees’ behavior. Journal of Organizational Change Management, 30(7), 1044-1062. doi:10.1108/jocm-06-2016-0116Balbi, S., Misuraca, M., & Scepi, G. (2018). Combining different evaluation systems on social media for measuring user satisfaction. Information Processing & Management, 54(4), 674-685. doi:10.1016/j.ipm.2018.04.009Barney, J. B. (1996). The Resource-Based Theory of the Firm. Organization Science, 7(5), 469-469. doi:10.1287/orsc.7.5.469Bassano, C., Piciocchi, P., Spohrer, J. («Jim») C., & Pietronudo, M. C. (2018). Managing value co-creation in consumer service systems within smart retail settings. Journal of Retailing and Consumer Services, 45, 190-197. doi:10.1016/j.jretconser.2018.09.008Behrend, F. D., & Erwee, R. (2009). Mapping knowledge flows in virtual teams with SNA. Journal of Knowledge Management, 13(4), 99-114. doi:10.1108/13673270910971860Berraies, S. (2019). The effect of enterprise social networks use on exploitative and exploratory innovations. Journal of Intellectual Capital, 20(3), 426-452. doi:10.1108/jic-02-2019-0030boyd, danah m., & Ellison, N. B. (2007). Social Network Sites: Definition, History, and Scholarship. Journal of Computer-Mediated Communication, 13(1), 210-230. doi:10.1111/j.1083-6101.2007.00393.xBrajer-Marczak, R. (2016). Elements of knowledge management in the improvement of business processes. Management, 20(2), 242-260. doi:10.1515/manment-2015-0063Capó‐Vicedo, J., Mula, J., & Capó, J. (2011). A social network‐based organizational model for improving knowledge management in supply chains. Supply Chain Management: An International Journal, 16(5), 379-388. doi:10.1108/13598541111155884Carter, C. R., Ellram, L. M., & Tate, W. (2007). THE USE OF SOCIAL NETWORK ANALYSIS IN LOGISTICS RESEARCH. Journal of Business Logistics, 28(1), 137-168. doi:10.1002/j.2158-1592.2007.tb00235.xCetto, A., Klier, M., Richter, A., & Zolitschka, J. F. (2018). «Thanks for sharing»—Identifying users’ roles based on knowledge contribution in Enterprise Social Networks. Computer Networks, 135, 275-288. doi:10.1016/j.comnet.2018.02.012Chandra, R., Iyer, R. S., & Raman, R. (2015). Enabling organizations to implement smarter, customized social computing platforms by leveraging knowledge flow patterns. Journal of Knowledge Management, 19(1), 95-107. doi:10.1108/jkm-11-2014-0455Charoensukmongkol, P. (2014). Effects of support and job demands on social media use and work outcomes. Computers in Human Behavior, 36, 340-349. doi:10.1016/j.chb.2014.03.061Choo, A. S., Linderman, K. W., & Schroeder, R. G. (2007). Method and Psychological Effects on Learning Behaviors and Knowledge Creation in Quality Improvement Projects. Management Science, 53(3), 437-450. doi:10.1287/mnsc.1060.0635Chreim, S., Williams, B. E. (Bernie), & Hinings, C. R. (Bob). (2007). Interlevel Influences on the Reconstruction of Professional Role Identity. Academy of Management Journal, 50(6), 1515-1539. doi:10.5465/amj.2007.28226248Coleman, J. S. (1988). Social Capital in the Creation of Human Capital. American Journal of Sociology, 94, S95-S120. doi:10.1086/228943Collinson, S., & Wilson, D. C. (2006). Inertia in Japanese Organizations: Knowledge Management Routines and Failure to Innovate. Organization Studies, 27(9), 1359-1387. doi:10.1177/0170840606067248Dartmouth, U.M.ass (2018). 2018 Fortune 500 Social Media Research. Retrieved fromhttps://www.umassd.edu/cmr/socialmediaresearch/2018fortune500/.El Ouirdi, A., El Ouirdi, M., Segers, J., & Henderickx, E. (2015). Employees’ use of social media technologies: a methodological and thematic review. Behaviour & Information Technology, 34(5), 454-464. doi:10.1080/0144929x.2015.1004647Eling, M., & Jia, R. (2019). Efficiency and profitability in the global insurance industry. Pacific-Basin Finance Journal, 57, 101190. doi:10.1016/j.pacfin.2019.101190Fang, R., Chi, L., Chen, M., & Baron, R. A. (2014). Bringing Political Skill into Social Networks: Findings from a Field Study of Entrepreneurs. Journal of Management Studies, 52(2), 175-212. doi:10.1111/joms.12107Fronzetti Colladon, A., & Vagaggini, F. (2017). Robustness and stability of enterprise intranet social networks: The impact of moderators. Information Processing & Management, 53(6), 1287-1298. doi:10.1016/j.ipm.2017.07.001Fulk, J., & Yuan, Y. C. (2013). Location, Motivation, and Social Capitalization via Enterprise Social Networking. Journal of Computer-Mediated Communication, 19(1), 20-37. doi:10.1111/jcc4.12033Galinsky, A. D., Seiden, V. L., Kim, P. H., & Medvec, V. H. (2002). The Dissatisfaction of Having Your First Offer Accepted: The Role of Counterfactual Thinking in Negotiations. Personality and Social Psychology Bulletin, 28(2), 271-283. doi:10.1177/0146167202282012Granovetter, M. (1985). Economic Action and Social Structure: The Problem of Embeddedness. American Journal of Sociology, 91(3), 481-510. doi:10.1086/228311Grant, S. B., & Preston, T. A. (2019). Using social power and influence to mobilise the supply chain into knowledge sharing: A case in insurance. Information & Management, 56(5), 625-639. doi:10.1016/j.im.2018.10.004Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. doi:10.1108/ebr-11-2018-0203Hayes, A. F., & Krippendorff, K. (2007). Answering the Call for a Standard Reliability Measure for Coding Data. Communication Methods and Measures, 1(1), 77-89. doi:10.1080/19312450709336664Heimbach, I., Gottschlich, J., & Hinz, O. (2015). The value of user’s Facebook profile data for product recommendation generation. Electronic Markets, 25(2), 125-138. doi:10.1007/s12525-015-0187-9Holsapple, C. W., Hsiao, S.-H., & Pakath, R. (2018). Business social media analytics: Characterization and conceptual framework. Decision Support Systems, 110, 32-45. doi:10.1016/j.dss.2018.03.004Hu, Q., Williams, S., Mason, R., & Found, P. (2019). Knowledge management in consultancy-involved process improvement projects: cases from Chinese SMEs. Production Planning & Control, 30(10-12), 866-880. doi:10.1080/09537287.2019.1582095Hung, S.-W., Chen, P.-C., & Chung, C.-F. (2013). Gaining or losing? The social capital perspective on supply chain members’ knowledge sharing of green practices. Technology Analysis & Strategic Management, 26(2), 189-206. doi:10.1080/09537325.2013.850475Huybrechts, B., & Haugh, H. (2017). The Roles of Networks in Institutionalizing New Hybrid Organizational Forms: Insights from the European Renewable Energy Cooperative Network. Organization Studies, 39(8), 1085-1108. doi:10.1177/0170840617717097Idrees, I. A., Vasconcelos, A. C., & Ellis, D. (2018). Clique and elite: inter-organizational knowledge sharing across five star hotels in the Saudi Arabian religious tourism and hospitality industry. Journal of Knowledge Management, 22(6), 1358-1378. doi:10.1108/jkm-07-2017-0276Jones, C., Hesterly, W. S., & Borgatti, S. P. (1997). A General Theory of Network Governance: Exchange Conditions and Social Mechanisms. Academy of Management Review, 22(4), 911-945. doi:10.5465/amr.1997.9711022109Kane, G. C. (2017). The evolutionary implications of social media for organizational knowledge management. Information and Organization, 27(1), 37-46. doi:10.1016/j.infoandorg.2017.01.001Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59-68. doi:10.1016/j.bushor.2009.09.003Khanbabaei, M., Alborzi, M., Sobhani, F. M., & Radfar, R. (2019). Applying clustering and classification data mining techniques for competitive and knowledge‐intensive processes improvement. Knowledge and Process Management, 26(2), 123-139. doi:10.1002/kpm.1595Kogut, B., & Zander, U. (1992). Knowledge of the Firm, Combinative Capabilities, and the Replication of Technology. Organization Science, 3(3), 383-397. doi:10.1287/orsc.3.3.383Kogut, B., & Zander, U. (1996). What Firms Do? Coordination, Identity, and Learning. Organization Science, 7(5), 502-518. doi:10.1287/orsc.7.5.502Kolleck, N. (2013). Social network analysis in innovation research: using a mixed methods approach to analyze social innovations. European Journal of Futures Research, 1(1). doi:10.1007/s40309-013-0025-2Krumeich, J., Weis, B., Werth, D., & Loos, P. (2014). Event-Driven Business Process Management: where are we now? Business Process Management Journal, 20(4), 615-633. doi:10.1108/bpmj-07-2013-0092Kuegler, M., Smolnik, S., & Kane, G. (2015). What’s in IT for employees? Understanding the relationship between use and performance in enterprise social software. The Journal of Strategic Information Systems, 24(2), 90-112. doi:10.1016/j.jsis.2015.04.001Lee, I. (2018). Social media analytics for enterprises: Typology, methods, and processes. Business Horizons, 61(2), 199-210. doi:10.1016/j.bushor.2017.11.002Lee, S., Ha, T., Lee, D., & Kim, J. H. (2018). Understanding the majority opinion formation process in online environments: An exploratory approach to Facebook. Information Processing & Management, 54(6), 1115-1128. doi:10.1016/j.ipm.2018.08.002Leon, R. – D., Rodríguez-Rodríguez, R., Gómez-Gasquet, P., & Mula, J. (2017). Social network analysis: A tool for evaluating and predicting future knowledge flows from an insurance organization. Technological Forecasting and Social Change, 114, 103-118. doi:10.1016/j.techfore.2016.07.032Li, Y.-M., Liao, T.-F., & Lai, C.-Y. (2012). A social recommender mechanism for improving knowledge sharing in online forums. Information Processing & Management, 48(5), 978-994. doi:10.1016/j.ipm.2011.10.004Lin, Z. (John), Yang, H., & Demirkan, I. (2007). The Performance Consequences of Ambidexterity in Strategic Alliance Formations: Empirical Investigation and Computational Theorizing. Management Science, 53(10), 1645-1658. doi:10.1287/mnsc.1070.0712Linderman, K., Schroeder, R. G., & Sanders, J. (2010). A Knowledge Framework Underlying Process Management*. Decision Sciences, 41(4), 689-719. doi:10.1111/j.1540-5915.2010.00286.xLiu, C., Liu, Y.-H., Gedeon, T., Zhao, Y., Wei, Y., & Yang, F. (2019). The effects of perceived chronic pressure and time constraint on information search behaviors and experience. Information Processing & Management, 56(5), 1667-1679. doi:10.1016/j.ipm.2019.04.004Liu, Z., & Jansen, B. J. (2017). Identifying and predicting the desire to help in social question and answering. Information Processing & Management, 53(2), 490-504. doi:10.1016/j.ipm.2016.05.001Mäntymäki, M., & Riemer, K. (2016). Enterprise social networking: A knowledge management perspective. International Journal of Information Management, 36(6), 1042-1052. doi:10.1016/j.ijinfomgt.2016.06.009Massa, F. G. (2016). Guardians of the Internet: Building and Sustaining the Anonymous Online Community. Organization Studies, 38(7), 959-988. doi:10.1177/0170840616670436Massingham, P., & Al Holaibi, M. (2017). Embedding Knowledge Management into Business Processes. Knowledge and Process Management, 24(1), 53-71. doi:10.1002/kpm.1534Modrák, V. (2013). On the Measurement of Business Process Centralization. Procedia Technology, 9, 547-552. doi:10.1016/j.protcy.2013.12.061Nakano, M., & Oji, N. (2016). Success factors for continuous supply chain process improvement: evidence from Japanese manufacturers. International Journal of Logistics Research and Applications, 20(3), 217-236. doi:10.1080/13675567.2016.1219704Neshati, M., Fallahnejad, Z., & Beigy, H. (2017). On dynamicity of expert finding in community question answering. Information Processing & Management, 53(5), 1026-1042. doi:10.1016/j.ipm.2017.04.002Nisar, T. M., Prabhakar, G., & Strakova, L. (2019). Social media information benefits, knowledge management and smart organizations. Journal of Business Research, 94, 264-272. doi:10.1016/j.jbusres.2018.05.005Nonaka, I., & Konno, N. (1998). The Concept of «Ba»: Building a Foundation for Knowledge Creation. California Management Review, 40(3), 40-54. doi:10.2307/41165942Nourani, M., Devadason, E. S., Kweh, Q. L., & Lu, W.-M. (2016). Business excellence: the managerial and value-creation efficiencies of the insurance companies. Total Quality Management & Business Excellence, 28(7-8), 879-896. doi:10.1080/14783363.2015.1133244Olfat, M., Tabarsa, G. A., Ahmadi, S., & Shokouhyar, S. (2019). Disclosing the bright side of SNs in the workplace. Journal of Enterprise Information Management, 32(3), 390-412. doi:10.1108/jeim-10-2018-0215Oostervink, N., Agterberg, M., & Huysman, M. (2016). Knowledge Sharing on Enterprise Social Media: Practices to Cope With Institutional Complexity. Journal of Computer-Mediated Communication, 21(2), 156-176. doi:10.1111/jcc4.12153Panahi, S., Watson, J., & Partridge, H. (2016). Conceptualising social media support for tacit knowledge sharing: physicians’ perspectives and experiences. Journal of Knowledge Management, 20(2), 344-363. doi:10.1108/jkm-06-2015-0229Parise, S. (2009). Social Media Networks: What Do They Mean for Knowledge Management? Journal of Information Technology Case and Application Research, 11(2), 1-11. doi:10.1080/15228053.2009.10856156Pathak, S. D., Wu, Z., & Johnston, D. (2014). Toward a structural view of co-opetition in supply networks. Journal of Operations Management, 32(5), 254-267. doi:10.1016/j.jom.2014.04.001Patroni, J., von Briel, F., & Recker, J. (2016). How Enterprise Social Media Can Facilitate Innovation. IT Professional, 18(6), 34-41. doi:10.1109/mitp.2016.102Putnam, R. D. (1995). Bowling Alone: America’s Declining Social Capital. Journal of Democracy, 6(1), 65-78. doi:10.1353/jod.1995.0002Qi, C., & Chau, P. Y. K. (2017). Will enterprise social networking systems promote knowledge management and organizational learning? An empirical study. Journal of Organizational Computing and Electronic Commerce, 28(1), 31-57. doi:10.1080/10919392.2018.1407081Rafique, M., Hameed, S., & Agha, M. H. (2018). Impact of knowledge sharing, learning adaptability and organizational commitment on absorptive capacity in pharmaceutical firms based in Pakistan. Journal of Knowledge Management, 22(1), 44-56. doi:10.1108/jkm-04-2017-0132Reagans, R., Singh, P. V., & Krishnan, R. (2015). Forgotten Third Parties: Analyzing the Contingent Association Between Unshared Third Parties, Knowledge Overlap, and Knowledge Transfer Relationships with Outsiders. Organization Science, 26(5), 1400-1414. doi:10.1287/orsc.2015.0976Riemer, K., Stieglitz, S., & Meske, C. (2015). From Top to Bottom. Business & Information Systems Engineering, 57(3), 197-212. doi:10.1007/s12599-015-0375-3Roberts, P. W., & Sterling, A. D. (2012). Network Progeny? Prefounding Social Ties and the Success of New Entrants. Management Science, 58(7), 1292-1304. doi:10.1287/mnsc.1110.1484Rodriguez, R. R., Saiz, J. J. A., & Bas, A. O. (2009). Quantitative relationships between key performance indicators for supporting decision-making processes. Computers in Industry, 60(2), 104-113. doi:10.1016/j.compind.2008.09.002Roelens, I., Baecke, P., & Benoit, D. F. (2016). Identifying influencers in a social network: The value of real referral data. Decision Support Systems, 91, 25-36. doi:10.1016/j.dss.2016.07.005Ashok Sarkar, S., Ranjan Mukhopadhyay, A., & Ghosh, S. K. (2013). Improvement of claim processing cycle time through Lean Six Sigma methodology. International Journal of Lean Six Sigma, 4(2), 171-183. doi:10.1108/20401461311319347Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation issues with PLS and CBSEM: Where the bias lies! Journal of Business Research, 69(10), 3998-4010. doi:10.1016/j.jbusres.2016.06.007Melo, A. C. S., Netto, M. A. C., Ferreira Filho, V. J. M., & Fernandes, E. (2010). Knowledge management for improving business processes: an analysis of the transport management process for indivisible exceptional cargo. Pesquisa Operacional, 30(2), 305-330. doi:10.1590/s0101-74382010000200004TOPAL, B., & SAHIN, H. (2019). The Influence of Information Sharing in the Supply Chain Process on Business Performance: An Empirical Study. Studies in Informatics and Control, 27(2), 201-212. doi:10.24846/v27i2y201808Tsai, W., Su, K., & Chen, M.-J. (2011). Seeing Through the Eyes of a Rival: Competitor Acumen Based on Rival-Centric Perceptions. Academy of Management Journal, 54(4), 761-778. doi:10.5465/amj.2011.64870138Turban, E., Bolloju, N., & Liang, T.-P. (2011). Enterprise Social Networking: Opportunities, Adoption, and Risk Mitigation. Journal of Organizational Computing and Electronic Commerce, 21(3), 202-220. doi:10.1080/10919392.2011.590109Wetzels, Odekerken-Schröder, & van Oppen. (2009). Using PLS Path Modeling for Assessing Hierarchical Construct Models: Guidelines and Empirical Illustration. MIS Quarterly, 33(1), 177. doi:10.2307/20650284White, G. R. T., Cicmil, S., Upadhyay, A., Subramanian, N., Kumar, V., & Dwivedi, A. (2019). The soft side of knowledge transfer partnerships between universities and small to medium enterprises: an exploratory study to understand process improvement. Production Planning & Control, 30(10-12), 907-918. doi:10.1080/09537287.2019.1582098Xue, B., Liu, B., & Sun, T. (2018). What Matters in Achieving Infrastructure Sustainability through Project Management Practices: A Preliminary Study of Critical Factors. Sustainability, 10(12), 4421. doi:10.3390/su10124421Xue, Y., Bradley, J., & Liang, H. (2011). Team climate, empowering leadership, and knowledge sharing. Journal of Knowledge Management, 15(2), 299-312. doi:10.1108/13673271111119709Yates, J., Orlikowski, W. J., & Okamura, K. (1999). Explicit and Implicit Structuring of Genres in Electronic Communication: Reinforcement and Change of Social Interaction. Organization Science, 10(1), 83-103. doi:10.1287/orsc.10.1.83Yousfi, A., Batoulis, K., & Weske, M. (2019). Achieving Business Process Improvement via Ubiquitous Decision-Aware Business Processes. ACM Transactions on Internet Technology, 19(1), 1-19. doi:10.1145/3298986Yuen, K. F., & Thai, V. (2017). Barriers to supply chain integration in the maritime logistics industry. Maritime Economics & Logistics, 19(3), 551-572. doi:10.1057/