We can work it out: : A multilevel examination of relationships among group and individual technology workarounds, and performance
Pages 1008 - 1038
Abstract
Despite the operational nature of enterprise system (ES) implementation and use, individual employees or work groups may deploy technology workarounds to circumvent inflexibility in or obstacles to using the ES. However, our understanding of the multilevel nature of technology workarounds and their performance implications remains limited. Drawing upon the multilevel theory of system usage and adaptive structuration theory, the current study examines the conditions under which group technology workarounds affect group performance, individual technology workarounds, and individual performance. Based on two studies with different research designs, we find that group technology workarounds have distinctive effects on short‐ and on long‐term group performance. Specifically, while the impact of group technology workarounds on group performance is significantly positive in the short term, such effect diminishes over time. System failure and competition intensity strengthen the positive effect of group technology workarounds on short‐term performance, whereas system failure and task nonroutineness lessen the negative effect of group technology workarounds on long‐term performance. Our study further confirms the multilevel nature of technology workarounds, finding that group technology workarounds can influence individual technology workarounds and thereby individual performance. Our results support the view that technology workarounds as a group action should be considered alongside individual technology workarounds, as well as their positive and negative effects on both group and individual performance, in both the short‐ and long‐term.
References
[1]
Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Sage.
[2]
Alavi, M., & Leidner, D. E. (2001). Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25, 107–136.
[3]
Alblas, A. (2022). Globally distributed technology projects thrive on experience working together apart: Impacts of team balance, task balance, and task heterogeneity. Journal of Operations Management, 68, 728–754.
[4]
Alyakoob, M., Rahman, M. S., & Wei, Z. (2021). Where you live matters: Local bank competition, online marketplace lending, and disparity in borrower benefits. Information Systems Research, 32, 1390–1411.
[5]
Armstrong, J., & Overton, T. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14, 396–402.
[6]
Bala, H., & Venkatesh, V. (2015). Adaptation to information technology: A holistic nomological network from implementation to job outcomes. Management Science, 62, 156–179.
[7]
Bamberger, B., Homburg, C., & Wielgos, D. M. (2021). Wage inequality: Its impact on customer satisfaction and firm performance. Journal of Marketing, 85, 24–43.
[8]
Benaroch, M., & Chernobai, A. (2017). Operational IT failures, IT value‐destruction, and board‐level IT governance changes. MIS Quarterly, 41, 729–762.
[9]
Bendoly, E., & Cotteleer, M. J. (2008). Understanding behavioral sources of process variation following enterprise system deployment. Journal of Operations Management, 26, 23–44.
[10]
Bendoly, E., Croson, R., Goncalves, P., & Schultz, K. (2010). Bodies of knowledge for research in behavioral operations. Production and Operations Management, 19, 434–452.
[11]
Bendoly, E., Donohue, K., & Schultz, K. L. (2006). Behavior in operations management: Assessing recent findings and revisiting old assumptions. Journal of Operations Management, 24, 737–752.
[12]
Bendoly, E., Perry‐Smith, J. E., & Bachrach, D. G. (2010). The perception of difficulty in project‐work planning and its impact on resource sharing. Journal of Operations Management, 28, 385–397.
[13]
Bendoly, E., Rosenzweig, E. D., & Stratman, J. K. (2009). The efficient use of enterprise information for strategic advantage: A data envelopment analysis. Journal of Operations Management, 27, 310–323.
[14]
Bhakoo, V., & Choi, T. (2013). The iron cage exposed: Institutional pressures and heterogeneity across the healthcare supply chain. Journal of Operations Management, 31, 432–449.
[15]
Bliese, P. D. (2000). Within‐group agreement, non‐independence, and reliability: Implications for data aggregation and analysis. In K. J. Klein & S. W. J. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions (pp. 349–381). Jossey‐Bass.
[16]
Block, J. (1956). A comparison of the forced and unforced Q‐sorting procedures. Educational and Psychological Measurement, 16, 481–493.
[17]
Boudreau, J., Hopp, W., McClain, J. O., & Thomas, L. J. (2003). On the interface between operations and human resources management. Manufacturing & Service Operations Management, 5, 179–202.
[18]
Boudreau, M.‐C., & Robey, D. (2005). Enacting integrated information technology: A human agency perspective. Organization Science, 16, 3–18.
[19]
Bulkeley, W. M. (1996). Technology (a special report): Working together–when things go wrong: FoxMeyer drug took a huge high‐tech gamble: It didn't work. Wall Street Journal.
[20]
Burton‐Jones, A., & Gallivan, M. J. (2007). Toward a deeper understanding of system usage in organizations: A multilevel perspective. MIS Quarterly, 31, 657–679.
[21]
Cantor, D. E., & Jin, Y. (2019). Theoretical and empirical evidence of behavioral and production line factors that influence helping behavior. Journal of Operations Management, 65, 312–332.
[22]
Cao, Q., & Dowlatshahi, S. (2005). The impact of alignment between virtual enterprise and information technology on business performance in an agile manufacturing environment. Journal of Operations Management, 23, 531–550.
[23]
Carmines, E. G., & Zeller, R. A. (1979). Reliability and validity assessment. Sage.
[24]
Choudrie, J., & Zamani, E. D. (2016). Understanding individual user resistance and workarounds of enterprise social networks: The case of service Ltd. Journal of Information Technology, 31, 130–151.
[25]
Clottey, T., & Benton, W. C. (2013). Guidelines for improving the power values of statistical tests for nonresponse bias assessment in OM research. Decision Sciences, 44, 797–812.
[26]
Clottey, T., & Benton, W. C. (2020). Technical note: Recommendations for assessing unit nonresponse bias in dyadic focused empirical supply chain management research. Decision Sciences, 51, 423–447.
[27]
Daft, R. L., & Macintosh, N. B. (1981). A tentative exploration into the amount and equivocality of information processing in organizational work units. Administrative Science Quarterly, 26, 207–224.
[28]
Davis, S., & Albright, T. (2004). An investigation of the effect of balanced scorecard implementation on financial performance. Management Accounting Research, 15, 135–153.
[29]
De Vries, J., Roy, D., & De Koster, R. (2018). Worth the wait? How restaurant waiting time influences customer behavior and revenue. Journal of Operations Management, 63, 59–78.
[30]
Debono, D. S., Greenfield, D., Travaglia, J. F., Long, J. C., Black, D., Johnson, J., & Braithwaite, J. (2013). Nurses' workarounds in acute healthcare settings: A scoping review. BMC Health Services Research, 13, 175.
[31]
Dekker, S. (2016). Drift into failure: From hunting broken components to understanding complex systems. CRC Press.
[32]
DeSanctis, G., & Poole, M. S. (1994). Capturing the complexity in advanced technology use: Adaptive structuration theory. Organization Science, 5, 121–147.
[33]
Devaraj, S., Krajewski, L., & Wei, J. C. (2007). Impact of eBusiness technologies on operational performance: The role of production information integration in the supply chain. Journal of Operations Management, 25, 1199–1216.
[34]
Ding, X., Peng, X., Heim, G. R., & Jordan, V. S. (2019). Service mix, market competition, and cost efficiency: A longitudinal study of U.S. hospitals. Journal of Operations Management, 66, 176–198.
[35]
Dong, S., Xu, S. X., & Zhu, K. X. (2009). Research note—Information technology in supply chains: The value of it‐enabled resources under competition. Information Systems Research, 20, 18–32.
[36]
Ejnefjäll, T., & Ågerfalk, P. J. (2019). Conceptualizing workarounds: Meanings and manifestations in information systems research. Communications of the Association for Information Systems, 45, 340–363.
[37]
Elie‐Dit‐Cosaque, C., Pallud, J., & Kalika, M. (2011). The influence of individual, contextual, and social factors on perceived behavioral control of information technology: A field theory approach. Journal of Management Information Systems, 28, 201–234.
[38]
Faraj, S., & Yan, A. (2009). Boundary work in knowledge teams. Journal of Applied Psychology, 94, 604–617.
[39]
Farrell, D. (2003). The real new economy. Harvard Business Review, 81, 104–112.
[40]
Feng, H., Morgan, N. A., & Rego, L. L. (2015). Marketing department power and firm performance. Journal of Marketing, 79, 1–20.
[41]
Ferneley, E. H., & Sobreperez, P. (2006). Resist, comply or workaround? An examination of different facets of user engagement with information systems. European Journal of Information Systems, 15, 345–356.
[42]
Fiol, C. M., & Lyles, M. A. (1985). Organizational learning. Academy of Management Review, 10, 803–813.
[43]
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.
[44]
Froehle, C. M., & White, D. L. (2014). Interruption and forgetting in knowledge‐intensive service environments. Production and Operations Management, 23, 704–722.
[45]
Gao, G. Y., Xie, E., & Zhou, K. Z. (2015). How does technological diversity in supplier network drive buyer innovation? Relational process and contingencies. Journal of Operations Management, 36, 165–177.
[46]
Gardner, J. W., Boyer, K. K., & Gray, J. V. (2015). Operational and strategic information processing: Complementing healthcare IT infrastructure. Journal of Operations Management, 33, 123–139.
[47]
Gasser, L. (1986). The integration of computing and routine work. ACM Transactions on Information Systems, 4, 205–225.
[48]
Gerbing, D. W., & Anderson, J. C. (1988). An updated paradigm for scale development incorporating unidimensionality and its assessment. Journal of Marketing Research, 25, 186–192.
[49]
Gligor, D. (2018). Performance implications of the fit between suppliers' flexibility and their customers' expected flexibility: A dyadic examination. Journal of Operations Management, 58, 73–85.
[50]
Gong, Q., Liu, C., Peng, Q., & Wang, L. (2020). Will CEOs with banking experience lower default risks? Evidence from P2P lending platforms in China. Finance Research Letters, 36, 101461.
[51]
González‐Romá, V., & Hernández, A. (2014). Climate uniformity: Its influence on team communication quality, task conflict, and team performance. Journal of Applied Psychology, 99, 1042–1058.
[52]
Halbesleben, J. R. B., Rathert, C., & Bennett, S. F. (2013). Measuring nursing workarounds. Journal of Nursing Administration, 43, 50–55.
[53]
Hald, K. S., & Mouritsen, J. (2013). Enterprise resource planning, operations and management: Enabling and constraining ERP and the role of the production and operations manager. International Journal of Operations & Production Management, 33, 1075–1104.
[54]
Heim, G. R., Nair, A., & Bendoly, E. (2021). Aims and criteria for advancing technology management research at the journal of operations management. Journal of Operations Management, 67, 920–925.
[55]
Hendricks, K. B., & Singhal, V. R. (2003). The effect of supply chain glitches on shareholder wealth. Journal of Operations Management, 21, 501–522.
[56]
Higgins, E. T. (2012). Regulatory focus theory. In P. A. M. Van Lange, A. W. Kruglanski, & E. T. Higgins (Eds.), Handbook of theories of social psychology (pp. 483–504). Sage Publications Ltd.
[57]
Hitt, M. A., Beamish, P. W., Jackson, S. E., & Mathieu, J. E. (2007). Building theoretical and empirical bridges across levels: Multilevel research in management. Academy of Management Journal, 50, 1385–1399.
[58]
Hofmann, D. A. (2002). Issues in multilevel research: Theory development, measurement, and analysis. In S. G. Rogelberg (Ed.) Handbook of research methods in industrial and organizational psychology (pp. 247–274). Blackwell.
[59]
Hoggy, M. A., & Terry, D. J. (2000). Social identity and self‐categorization processes in organizational context. Academy of Management Review, 25, 121–140.
[60]
Holweg, M., & Pil, F. K. (2008). Theoretical perspectives on the coordination of supply chains. Journal of Operations Management, 26, 389–406.
[61]
Huckman, R. S., & Staats, B. R. (2011). Fluid tasks and fluid teams: The impact of diversity in experience and team familiarity on team performance. Manufacturing & Service Operations Management, 13, 310–328.
[62]
Hult, G. T. M., Ketchen, D. J., Jr., & Nichols, E. L., Jr. (2003). Organizational learning as a strategic resource in supply management. Journal of Operations Management, 21, 541–556.
[63]
James, L. R., Demaree, R. G., & Wolf, G. (1984). Estimating within‐group interrater reliability with and without response bias. Journal of Applied Psychology, 69, 85–98.
[64]
Jansen, J. J. P., Van Den Bosch, F. A. J., & Volberda, H. W. (2006). Exploratory innovation, exploitative innovation, and performance: Effects of organizational antecedents and environmental moderators. Management Science, 52, 1661–1674.
[65]
Jehn, K. A. (1995). A multimethod examination of the benefits and detriments of intragroup conflict. Administrative Science Quarterly, 40, 256–282.
[66]
Jia, L., Shaw, J., Tsui, A., & Park, T.‐Y. (2014). A social‐structural perspective on employee‐organization relationships and team creativity. Academy of Management Journal, 57, 869–891.
[67]
Johns, G. (2006). The essential impact of context on organizational behavior. Academy of Management Review, 31, 386–408.
[68]
Ke, W., Kang, L., Tan, C.‐H., & Peng, C.‐H. (2021). User competence with enterprise systems: The effects of work environment factors. Information Systems Research, 32, 860–875.
[69]
Keller, R. T. (1994). Technology‐information processing fit and the performance of R&D project groups: A test of contingency theory. Academy of Management Journal, 37, 167–179.
[70]
Ketokivi, M. (2019). Avoiding bias and fallacy in survey research: A behavioral multilevel approach. Journal of Operations Management, 65, 380–402.
[71]
Kim, Y. J., & Toh, S. M. (2019). Stuck in the past? The influence of a leader's past cultural experience on group culture and positive and negative group deviance. Academy of Management Journal, 62, 944–969.
[72]
Koopman, P., & Hoffman, R. R. (2003). Work‐arounds, make‐work, and kludges. IEEE Intelligent Systems, 18, 70–75.
[73]
Kozlowski, S. W. J., & Klein, K. J. (2000). A multilevel approach to theory and research in organizations: Contextual, temporal, and emergent processes. In K. J. Klein & S. W. J. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations (pp. 3–90). Jossey‐Bass.
[74]
Kraimer, M. L., Wayne, S. J., Liden, R. C., & Sparrowe, R. T. (2005). The role of job security in understanding the relationship between employees' perceptions of temporary workers and employees' performance. Journal of Applied Psychology, 90, 389–398.
[75]
Krishnakumar, S. K., Kishore, R., & Suresh, N. C. (2022). Expansive or focused attention? An exploration–exploitation perspective on e‐business systems and firm performance. Production and Operations Management, 31, 2038–2066.
[76]
Kumar, S., Mookerjee, V., & Shubham, A. (2018). Research in operations management and information systems interface. Production and Operations Management, 27, 1893–1905.
[77]
Lam, H. K., Yeung, A. C., & Cheng, T. E. (2016). The impact of firms' social media initiatives on operational efficiency and innovativeness. Journal of Operations Management, 47‐48, 28–43.
[78]
Laumer, S., Maier, C., & Weitzel, T. (2017). Information quality, user satisfaction, and the manifestation of workarounds: A qualitative and quantitative study of enterprise content management system users. European Journal of Information Systems, 26, 333–360.
[79]
Liang, H., Xue, Y., & Wu, L. (2013). Ensuring employees' it compliance: Carrot or stick? Information Systems Research, 24, 279–294.
[80]
Liao, H., & Rupp, D. E. (2005). The impact of justice climate and justice orientation on work outcomes: A cross‐level multifoci framework. Journal of Applied Psychology, 90, 242–256.
[81]
Liberman, N., Sagristano, M. D., & Trope, Y. (2002). The effect of temporal distance on level of mental construal. Journal of Experimental Social Psychology, 38, 523–534.
[82]
Liberman, N., Trope, Y., McCrea, S. M., & Sherman, S. J. (2007). The effect of level of construal on the temporal distance of activity enactment. Journal of Experimental Social Psychology, 43, 143–149.
[83]
Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross‐sectional research designs. Journal of Applied Psychology, 86, 114–121.
[84]
Linderman, K., Schroeder, R. G., & Choo, A. S. (2006). Six sigma: The role of goals in improvement teams. Journal of Operations Management, 24, 779–790.
[85]
Linderman, K., Schroeder, R. G., Zaheer, S., Liedtke, C., & Choo, A. S. (2004). Integrating quality management practices with knowledge creation processes. Journal of Operations Management, 22, 589–607.
[86]
Majchrzak, A., Malhotra, A., & John, R. (2005). Perceived individual collaboration know‐how development through information technology–enabled contextualization: Evidence from distributed teams. Information Systems Research, 16, 9–27.
[87]
Malaurent, J., & Karanasios, S. (2020). Learning from workaround practices: The challenge of enterprise system implementations in multinational corporations. Information Systems Journal, 30, 639–663.
[88]
Mason, C. H., & Perreault, W. D. (1991). Collinearity, power, and interpretation of multiple regression analysis. Journal of Marketing Research, 28, 268–280.
[89]
Massimino, B., & Lawrence, B. (2019). Supersize me? Franchisee size and voluntary compliance with corporate brand‐building initiatives. Journal of Operations Management, 65, 659–684.
[90]
McGrath, J. E., Arrow, H., & Berdahl, J. L. (2014). The study of groups: Past, present, and future. Personality and Social Psychology Review, 4, 95–105.
[91]
Menor, L. J., & Roth, A. V. (2007). New service development competence in retail banking: Construct development and measurement validation. Journal of Operations Management, 25, 825–846.
[92]
Moliterno, T. P., Aguinis, H., Boyd, B. K., Pierce, C. A., Short, J. C., & Mahony, D. M. (2010). Network theory of organization: A multilevel approach. Journal of Management, 37, 443–467.
[93]
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2, 192–222.
[94]
Morgeson, F. P., & Hofmann, D. A. (1999). The structure and function of collective constructs: Implications for multilevel research and theory development. Academy of Management Review, 24, 249–265.
[95]
Morrison, B. (2015). The problem with workarounds is that they work: The persistence of resource shortages. Journal of Operations Management, 39, 79–91.
[96]
Oliva, R., & Sterman, J. D. (2001). Cutting corners and working overtime: Quality erosion in the service industry. Management Science, 47, 894–914.
[97]
Pagell, M., & LePine, J. A. (2002). Multiple case studies of team effectiveness in manufacturing organizations. Journal of Operations Management, 20, 619–639.
[98]
Park, S., & Gupta, S. (2012). Handling endogenous regressors by joint estimation using copulas. Marketing Science, 31, 567–586.
[99]
Park, S., Mathieu, J. E., & Grosser, T. J. (2020). A network conceptualization of team conflict. Academy of Management Review, 45, 352–375.
[100]
Patterson, E. S., Rogers, M. L., Chapman, R. J., & Render, M. L. (2006). Compliance with intended use of bar code medication administration in acute and long‐term care: An observational study. Human Factors, 48, 15–22.
[101]
Pavlou, P. A., & Gefen, D. (2004). Building effective online marketplaces with institution‐based trust. Information Systems Research, 15, 37–59.
[102]
Perrow, C. (1967). A framework for the comparative analysis of organizations. American Sociological Review, 32, 194–208.
[103]
Petrides, L. A., McClelland, S. I., & Nodine, T. R. (2004). Costs and benefits of the workaround: Inventive solution or costly alternative. International Journal of Educational Management, 18, 100–108.
[104]
Pine, K. H., & Mazmanian, M. (2017). Artful and contorted coordinating: The ramifications of imposing formal logics of task jurisdiction on situated practice. Academy of Management Journal, 60, 720–742.
[105]
Ployhart, R. E., & Moliterno, T. P. (2011). Emergence of the human capital resource: A multilevel model. Academy of Management Review, 36, 127–150.
[106]
Podsakoff, P. M., MacKenzie, S. B., Lee, J.‐Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879–903.
[107]
Poole, M. S., & DeSanctis, G. (1990). Understanding the use of group decision support systems: The theory of adaptive structuration. In J. Fulk & C. Steinfield (Eds.), Organizations and communication technology (pp. 173–193). Sage.
[108]
Porter, M. E. (1985). Competitive advantage: Creating and sustaining superior performance. The Free Press.
[109]
Porter, M. E. (1991). Towards a dynamic theory of strategy. Strategic Management Journal, 12, 95–117.
[110]
Posen, H. E., & Chen, J. S. (2013). An advantage of newness: Vicarious learning despite limited absorptive capacity. Organization Science, 24, 1701–1716.
[111]
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Sage.
[112]
Ray, G., Barney, J. B., & Muhanna, W. A. (2004). Capabilities, business processes, and competitive advantage: Choosing the dependent variable in empirical tests of the resource‐based view. Strategic Management Journal, 25, 23–37.
[113]
Rice, R. E. (1992). Task analyzability, use of new media, and effectiveness: A multi‐site exploration of media richness. Organization Science, 3, 475–500.
[114]
Rice, R. E., & Cooper, S. D. (2010). Organizations and unusual routines: A systems analysis of dysfunctional feedback processes. Cambridge University Press.
[115]
Roth, A. V., & Van Der Velde, M. (1991). Operations as marketing: A competitive service strategy. Journal of Operations Management, 10, 303–328.
[116]
Sahi, G. K., Gupta, M. C., Cheng, T. C. E., & Lonial, S. C. (2019). Relating entrepreneurial orientation with operational responsiveness: Roles of competitive intensity and technological turbulence. International Journal of Operations & Production Management, 39, 739–766.
[117]
Salvador, F., Alba, C., Madiedo, J. P., Tenhiälä, A., & Bendoly, E. (2021). Project managers' breadth of experience, project complexity, and project performance. Journal of Operations Management, 67, 729–754.
[118]
Sanders, N. R. (2008). Pattern of information technology use: The impact on buyer–suppler coordination and performance. Journal of Operations Management, 26, 349–367.
[119]
Schoenherr, T., Hilpert, D., Soni, A. K., Venkataramanan, M. A., & Mabert, V. A. (2010). Enterprise systems complexity and its antecedents: A grounded‐theory approach. International Journal of Operations & Production Management, 30, 639–668.
[120]
Setia, P., & Patel, P. C. (2013). How information systems help create OM capabilities: Consequents and antecedents of operational absorptive capacity. Journal of Operations Management, 31, 409–431.
[121]
Shah, R., & Shin, H. (2007). Relationships among information technology, inventory, and profitability: An investigation of level invariance using sector level data. Journal of Operations Management, 25, 768–784.
[122]
Shalley, C. E., & Gilson, L. L. (2017). Creativity and the management of technology: Balancing creativity and standardization. Production and Operations Management, 26, 605–616.
[123]
Song, P., Wang, Q., Liu, H., & Li, Q. (2020). The value of buy‐online‐and‐pickup‐in‐store in omni‐channel: Evidence from customer usage data. Production and Operations Management, 29, 995–1010.
[124]
Spierings, A., Kerr, D., & Houghton, L. (2017). Issues that support the creation of ICT workarounds: Towards a theoretical understanding of feral information systems. Information Systems Journal, 27, 775–794.
[125]
Tang, X., & Rai, A. (2012). The moderating effects of supplier portfolio characteristics on the competitive performance impacts of supplier‐facing process capabilities. Journal of Operations Management, 30, 85–98.
[126]
Tenhiälä, A., & Helkiö, P. (2015). Performance effects of using an ERP system for manufacturing planning and control under dynamic market requirements. Journal of Operations Management, 36, 147–164.
[127]
Tomsho, R. (1994). Real dog: How greyhound lines re‐engineered itself right into a deep hole. Wall Street Journal, 20, A1–A6.
[128]
Tucker, A. L. (2004). The impact of operational failures on hospital nurses and their patients. Journal of Operations Management, 22, 151–169.
[129]
Tucker, A. L. (2007). An empirical study of system improvement by frontline employees in hospital units. Manufacturing & Service Operations Management, 9, 492–505.
[130]
Tucker, A. L., & Edmondson, A. C. (2003). Why hospitals don't learn from failures: Organizational and psychological dynamics that inhibit system change. California Management Review, 45, 55–72.
[131]
Tucker, A. L., Zheng, S., Gardner, J. W., & Bohn, R. E. (2020). When do workarounds help or hurt patient outcomes? The moderating role of operational failures. Journal of Operations Management, 66, 67–90.
[132]
Van de Vijver, F., & Leung, K. (1997). Methods and data analysis for cross‐cultural research. Sage Publications.
[133]
van Mierlo, H., Vermunt, J. K., & Rutte, C. G. (2008). Composing group‐level constructs from individual‐level survey data. Organizational Research Methods, 12, 368–392.
[134]
Vaughan, D. (1999). The dark side of organizations: Mistake, misconduct, and disaster. Annual Review of Sociology, 25, 271–305.
[135]
Velmurugan, R. S., & Dhingra, T. (2015). Maintenance strategy selection and its impact in maintenance function: A conceptual framework. International Journal of Operations & Production Management, 35, 1622–1661.
[136]
Wang, Y., Meister, D. B., & Gray, P. H. (2013). Social influence and knowledge management systems use: Evidence from panel data. MIS Quarterly, 37, 299–313.
[137]
Welbourne, T. M., Johnson, D. E., & Erez, A. (1998). The role‐based performance scale: Validity analysis of a theory‐based measure. Academy of Management Journal, 41, 540–555.
[138]
Wong, L. H. M., Hurbean, L., Davison, R. M., Ou, C. X. J., & Muntean, M. (2022). Working around inadequate information systems in the workplace: An empirical study in Romania. International Journal of Information Management, 64, 102471.
[139]
Xu, E., Huang, X., & Robinson, S. L. (2017). When self‐view is at stake: Responses to ostracism through the lens of self‐verification theory. Journal of Management, 43, 2281–2302.
[140]
Zahra, S. A. (1995). Corporate entrepreneurship and financial performance: The case of management leveraged buyouts. Journal of Business Venturing, 10, 225–247.
[141]
Zhang, X. (2017). Knowledge management system use and job performance: A multilevel contingency model. MIS Quarterly, 41, 811–840.
[142]
Zhang, Z., Hu, W., & Chang, T. (2019). Nonlinear effects of P2P lending on bank loans in a panel smooth transition regression model. International Review of Economics & Finance, 59, 468–473.
[143]
Zhou, K. Z., Zhang, Q., Sheng, S., Xie, E., & Bao, Y. (2014). Are relational ties always good for knowledge acquisition? Buyer–supplier exchanges in China. Journal of Operations Management, 32, 88–98.
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© 2023 Association for Supply Chain Management, Inc.
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John Wiley & Sons, Inc.
United States
Publication History
Published: 26 July 2023
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