The impact of improvisational and dynamic capabilities on business model innovation during COVID-19: a composite-based approach Rogier van de Wetering1, Joshua Doe2, Ronald van den Heuvel1 and Hussam Al Hal- busi3 1 Faculty of Science, Open University, Heerlen, the Netherlands rogier.vandewetering@ou.nl 2 Marketing department, Central University, Accra, Ghana 3 Department of Management, Ahmed bin Mohammed Military College, Doha, Qatar Abstract. Dynamic capabilities embody various capabilities that drive the organ- ization’s adaptiveness and are studied from management and information sys- tems perspectives. However, the impact of specific dynamic and organizational capabilities, i.e., management system adaptability and improvisational capabili- ties, on business model innovation under tumultuous times still has to be un- folded. Therefore, this study investigates the role of these capabilities during the COVID-19 crisis. This study presents the results of analyses on obtained survey data (N=105) from Ghana and shows that these two strategic capabilities signifi- cantly influence business model innovation. Also, this study shows that business model innovation positively influences organizational performance under COVID-19. These results extend the current knowledge base of dynamic and or- ganizational capabilities while offering implications for practice. We also offer various practical recommendations that help overcome business model innova- tion challenges during tumultuous times. Keywords: management system adaptability, improvisational capabilities, dy- namic capability, business model innovation, organizational performance under COVID-19, composite-based SEM 1 Introduction At the beginning of the COVID-19 pandemic, many organizations faced a downturn due to multiple challenges. Some notable adverse effects of this downturn include shareholder distress, a substantial drop in customer and consumer demand, an increase in the cost of capital in tightening credit markets, and even a decline in asset value due to a lack of visibility when employees do not come to locations as much as before. Under these tumultuous conditions, organizations typically focus on improving plans and actions on tactical or functional levers [1]. However, many organizations miss substantial business opportunities through struc- tural complexity reduction and effective and innovative business model use [2]. 2 Business models depict the content, structure, and governance of transactions de- signed to create value by exploiting business opportunities [3, 4]. Each organization has a business model [5]. Some, however, have not clearly articulated and documented this. Typically, a business model describes the organization’s value propositions (prod- ucts/services, offerings), profit formula, the organization’s key resources (e.g., people, process, technology, and support) the processes needed for execution and collaboration [6]. In essence, business models can be considered a driving force in the organization’s business operations that are in line with what customers want, how people can do their work most efficiently and with the proper behavior [7] It is well known that different levels of innovation ambition require different people, motivational factors, and organizational support systems [8, 9]. A key focal point in innovation concerns business model innovation [10]. Business model innovation allows firms to address down-turn events by changing their core value proposition to custom- ers and their underlying operating model [3]. The degree to which an organization’s business model innovation is being rolled out can be boldly classified into three levels of ambition. The first is the primary ‘creation’ of incremental enhancement of products and services. The second is adjacent ‘sustaining innovation,’ which focuses on lever- aging offerings and value propositions into a new space. Finally, the third one is the ‘transformational’ level that embraces the development of new offers to customers and possibly even new businesses to serve markets and customer needs that may not yet exist [6]. Many organizations cannot achieve business benefits from their business model in- novation when they re-balance the innovation portfolio from core to fundamental and subsequently to the transformational level. One reason why this is so is that these firms lack essential organizational resources, capabilities, and key processes to be truly trans- formative in their business model [4, 6, 10]. Hence, we argue that organizations capable of orchestrating their organizational resources and addressing down-turn events by changing their core value proposition will successfully achieve business model innova- tion and high levels of organizational performance. To this end, we embrace the suitable dynamic capabilities view (DCV) as a lens to investigate this particular claim [11]. Moreover, Teece [4] argues that it is crucial to understand the contribution of dynamic capabilities to achieving business model inno- vation, as current scholarly contributions are predominately theoretical. Hence, this study investigates the key role of two particular organizational capabili- ties, i.e., management system adaptability, as dynamic capability [12, 13] and improvi- sational capabilities [3, 14], as drivers of business model innovation. These crucial ca- pabilities enable a firm’s offerings and competitive position, and these capabilities are created through configurations of assets and activities where processes and people (pro- fessionals) are combined, controlled, and interconnected. These organizational capabil- ities bridge the firm’s strategic objectives, ambitions, and day-to-day activities. Against this background, we define the main research question: “To what extent do management system adaptability and improvisational capabilities influence business model innovation, and what is the subsequent impact on organizational performance under COVID-19?” 3 This paper is organized as follows. First, we outline the theory and position the framework with associated hypotheses. Then, we outline the methods, after which we present the core results of this study. Finally, the current paper ends with a discussion, including theoretical and practical contributions. 2 Theory and the study’s framework 2.1 Capability perspective, dynamic and improvisational capabilities We ground this study in the DCV. This theory provides scholars and leaders with in- sights into how to adapt firms under turbulent conditions and adjust their operating base with processes and technologies in line with the market demands [4, 14]. This theory starts with the notion of a ‘capability.’ A capability can be regarded as aggregating several underlying elements that refer to tangible and intangible assets firms use to de- velop and implement the business strategy [15]. Think, for instance, about competen- cies (i.e., individual employee skills), business processes that produce a particular out- put, knowledge systems, and partnerships (i.e., the interfaces with key participants an organization needs to produce outputs) [15, 16]. The combination of these individual aspects gives an organization a particular capa- bility. However, unfortunately, there is often confusion between capabilities and com- petencies. This is because capabilities are always associated with the organizational level; competencies and skillsets, in essence, describe an individual. In this research context, we focus on specific capabilities, i.e., dynamic and impro- visational capabilities. Hence, we follow Wang and Ahmed [17] and define dynamic capabilities as ‘…the firm’s behavioral orientation constantly to integrate, reconfigure, renew and recreate its resources and capabilities and, most importantly, upgrade and reconstruct its core capabilities in response to the changing environment to attain and sustain competitive advantage.’ A crucial unique dynamic capability is the firm’s adap- tive capability (next to, for instance, absorptive capacity and innovation capability) which is considered the ability of a firm to reconfigure resources, coordinate processes, and effectively address changes in the business environment [4, 13, 18-20]. This research focuses on a specific dimension of adaptive capability, namely the management system adaptive capability [13]. Hence, this particular capability is essen- tial for firms as it encourages employees and managers to challenge outmoded practices across the organization and allows a firm to respond to changes in the market ade- quately [13, 21]. The extant literature distinguishes between dynamic capabilities and improvisational ones [14]. Hence, improvisational capabilities denote repetitively engaging in improvi- sational actions without formal planning by building innovative products and solutions to enhance operational and competitive benefits [3, 22]. Improvisational capabilities operate as a “third hand,” according to Pavlou and El Sawy [14], next to ‘planned’ dynamic and operational (zero-order) capabilities—that drive the present business op- erations—as a driver of change, adaption, and innovation during tumultuous times [23]. 4 2.2 Hypotheses development We adopt a complementarity and ambidexterity perspective, claiming that the sim- ultaneous execution of two seemingly opposing capabilities, i.e., improvisational and dynamic capabilities, complement each other to collectively achieve business model innovation during crises. This idea resonates well with Mintzberg’s ‘intended’ versus ‘emerging’ strategy [24]. Consistent with prior research on innovativeness that shows that dynamic capabili- ties drive the use the new technological innovations and enable business process inno- vations [25-28], we now argue that management system adaptability, as a dynamic ca- pability, drives the firm’s business model innovation. It does so by actively reducing inefficient coordination and control mechanisms across the organization and encourag- ing communication and information flow among the organization’s teams and employ- ees [13, 21]. In this regard, organizations that have managerial systems that are flexible and adaptive can better challenge outmoded traditions and practices, respond faster to shifts in the market and harness the organization-wide skills and competencies neces- sary to innovate [13, 21, 25]. Moreover, management systems adaptability is especially crucial during sudden disruptions, like the COVID-19 pandemic, as firms must react and adapt to changing customer demands and behaviors, seize business and technolog- ical opportunities and embrace new service innovations [20, 25, 26, 29]. Based on the above, we define the following: Hypothesis 1 (H1): Management systems adaptability positively impacts business model innovation during COVID-19. The nature of improvisational capabilities is different from dynamic capabilities. First, they are ‘emergent’ and enable firms to take action spontaneously rather than based on rigorous planning. Pavlou and El Sawy [14] summarize this well as they argue that these capabilities help firms to “….spontaneously reconfigure existing resources to build new operational capabilities to address urgent, unpredictable, and novel envi- ronmental situations.” Second, under conditions of high uncertainty where there is no time for organizational resource planning, these capabilities are crucial as they offer firms the reflexive instincts and needed improvisational activities to adapt and respond to the problems using the resources available [22, 30, 31]. Hence, improvisational ca- pabilities provide firms with the necessary skillset to adapt the firm’s products, propo- sitions and services, delivery channels, and technological platforms and support the way customer transactions are done [3, 4, 14, 22]. Management systems adaptability and improvisational capabilities collectively al- low organizations to foresee trends, developments clearly, and market disruption, deeply understand market dynamics, and adjust accordingly. Furthermore, they enable firms to use combinations of resources (people, processes, technology) for new busi- ness operations, allowing firms to quickly respond to market and business changes and drive business model innovation [3, 13, 14, 21]. Synthesizing from the above, we define the following hypothesis: 5 Hypothesis 2 (H2): Improvisational capabilities positively impact business model in- novation during COVID-19. Deducting from our theoretical framework, we argue that business model innovation is a crucial antecedent to achieving high levels of organizational performance. For ex- ample, business model innovation is a key enabler for organizations to efficiently de- liver excellent services, offerings, and value to their customers through new digital technologies and online channels [5, 6]. In addition, especially during times of high uncertainty, customers want mobility in product and service delivery and a seamless service experience that ultimately results in high customer satisfaction [32, 33]. Business model innovation allows firms to address down-turn events like COVID- 19 by changing their core propositions to customers and the firm’s underlying operat- ing model [3, 34]. Also, business model innovation drives an organization’s growth ambitions (e.g., high profitability, increased market share) by transforming the organi- zation’s (go-to) markets and segments and the way the business operations can be scaled and deliver high-quality personalized services [4, 6, 35, 36]. The organization can accelerate new revenue streams through business model innovation so that oppor- tunities can be capitalized cost-effectively [29-32, 37, 38]. In addition, research has shown that business model innovation will substantially impact the firm’s competitiveness and value-creation processes for the organization [8, 39]. Finally, we define: Hypothesis 3 (H3): Business model innovation positively impacts organizational per- formance during COVID-19. 3 Methods and composite-based analyses We used an online survey anonymously and conveniently distributed it among SMEs in Ghana through our professional and educational network. In addition, we targeted senior business practitioners, like chief executive officers, chief information officers, chief digital officers, and IT managers. The data collection process was performed be- tween 8th April and 20th May 2020. After removing incomplete and inadequate re- sponses, our sample size was 105 SMEs. We adopted previously empirically validated measures for all constructs of the framework. We adopted four items from [3] to measure the firm’s capability to engage in improvisational actions without formal planning, i.e., improvisational capabilities, and three items from [12, 13] to measure the firm’s management system adaptability. In addition, we used nine indicators from [3, 9] to measure business model innova- tion. Finally, we adopted five measures from [23, 40] to measure organizational perfor- mance during COVID-19 and evaluate performance from a broad and balanced per- spective. All measures are included in the Appendix. We used structural equation modeling (SEM) as the preferred analysis method to test the hypotheses. Specifically, we use composite-based SEM, a variance-based ap- proach that uses weighted linear combinations of measurable items as proxies for un- 6 derlying theoretical conceptualizations [41]. This approach to SEM handles both emer- gent variables (composite-formative measurements) and latent variables (reflective measurement and common factor model) [42]. Therefore, it is considered a full estima- tor to SEM used in various types of research, including exploratory research and rela- tionship assessment between different proxies, confirmatory research, and predictive research [43]. Furthermore, composite-based SEM works well with relatively small sample sizes, as in our work, has less strict requirements concerning multivariate nor- mality, and offers model specification and requirement flexibility [44, 45]. Finally, composite-based SEM allows scholars to extend existing theory by examining the model’s ability to predict instead of establishing model fit only, as in covariance-based SEM [45, 46]. We use ADANCO 2.3 as the analysis tool [41], and all constructs are operationalized as latent variables, thus representing (reflective) common factor models. Moreover, compared to other composite-based tools like SmartPLS or WarpPLS, ADANCO offers the possibility to assess the overall goodness of fit [42, 47]. 4 Empirical results and interpretation Before we tested the core hypotheses of this work, we assessed the model fit. Table 1 outlines the key results of this assessment. Using ADANCO, model fit is assessed through three related metrics [42]. The first one is SRMR (Standardized root mean squared residual). This metric describes how the empirical correlation matrix differs from a model-implied correlation matrix. In addition, the software provides bootstrap- based outcomes for HI95 and HI99 percentiles. So, if the SRMS goes beyond these two values, it is improbable that the model is true. The other two metrics, dULS (unweighted least squares discrepancy) and dG (geodesic discrepancy), are other metrics to unfold the extent to which the empirical correlation matrix differs from the model-implied variant. As can be seen from the Table beneath, all obtained values are below HI99-values, suggesting an appropriate model fit. The hypotheses can now be tested. Table 2. Goodness of fit assessment Goodness of model fit (saturated model) Value HI95 HI99 Conclusion SRMR 0.0688 0.0631 0.0690 Supported dULS 1.0946 0.9196 1.1007 Supported dG 0.8037 0.8573 0.9965 Supported Figure 1 shows the outcomes of the structural model assessment using ADANCO. This Figure includes the software’s obtained beta coefficients (i.e., estimates from the regression analyses), t-values (i.e., the coefficient divided by the associated standard error and also showing the importance of the construct in the model), and coefficient of determination (R2), that shows the amount of variation in the respective outcomes (de- pendent variables). 7 As the Figure shows, all hypotheses can be accepted as the beta coefficients were significant. We also controlled these outcomes by including “size” and “industry” in the model. These variables had no significant impact on the outcomes (size: β = -0.004 t = 0.353, p = 0.93) and “industry” (β = −0.056, t = 0.204, p = 0.58). Fig. 1. Results of the structural models. 5 Discussion It goes without saying that under tumultuous times, firms must reframe how they posi- tion their offerings in the marketplace and how they interact with customers. This work tried to unfold whether or not two strategic capabilities collectively drive the firm’s business model innovation that serves as the foundation for achieving high levels of organizational performance under COVID-19. Outcomes of this work using a compo- site-based approach showcased that both improvisational capabilities and management systems adaptability are critical drivers of business model innovation. We also showed that business model innovation positively influences organizational performance under COVID-19. These outcomes have several theoretical and practical implications, which will be discussed next. Building upon the foundations of the DCV, we argued that two strategic capabilities could profoundly impact organizational success. This study proposed that planned (management systems adaptability) and improvisational capabilities positively impact business model innovation. We found evidence for the study’s main proposition and thereby extended work by [14] by showing the collective impact that planned and im- provisational capabilities can have on business model innovation. However, based on this work’s outcomes, it is evident that improvisational capabilities have a more sub- stantial impact than management systems adaptability. It could be that the planned dy- namic capabilities’ effect is weaker under highly tumultuous conditions like the current COVID-19 pandemic, whereas the effect of improvisational capabilities is more sub- stantial, as argued by Pavlou and Sawy [14]. With these outcomes, we also adhere to 8 the call by Teece [4] that scholars should investigate the contributions of dynamic ca- pabilities to business model innovation. We also add the body of knowledge on innovation and managing down-turn events [4, 6, 10, 21]. Specifically, this work now shows how business model innovation driven by strategic capabilities can drive high levels of organizational performance during a global pandemic like COVID-10. Hence, we stress that organizations capable of or- chestrating their organizational resources and addressing downturn events by making specific changes to their core value proposition will successfully achieve business model innovation and high levels of organizational performance. Next to theoretical contributions, we also outline some implications of this work for practice. Hence, we argue that organizations that excel in their business model innova- tion should embrace a strategic approach using an integrative process of project man- agement and dynamic resource allocation; and an organizational model that establishes the right capabilities, metrics, incentives, decision rights, and responsibilities. Also, decision-makers should develop dynamic capabilities to proactively identify, manage, and evaluate a wide range of new businesses, projects, opportunities, and risks. Moreover, these capabilities enable the organization to challenge the status quo and leave outmoded traditions and practices. Also, firms should actively engage in impro- visational activities and allow a firm to respond quickly to changes in the market. Hence, business model innovation and its subsequent impact on performance must start with and be guided by a clear set of choices that connect to the overall firm and even department and business unit strategies. This study has several limitations that future work could address. First, we currently only collected data from Ghana, possibly inhibiting the generalization of these out- comes to developed countries, even though we expect that the outcomes will be gener- alizable to other emerging countries. Second, the theoretical model only included dy- namic and improvisational capabilities as an antecedent of business model innovation. Apart from the discriminant validity tests, showing the distinctiveness of each con- struct, Pavlou and El Sawy [14] argue that planned and dynamic capabilities can have different relationships with their antecedents (e.g., IT system support or flexibility), their outcomes (consequences), and possible moderating constructs like the technolog- ical or market turbulence. Future work could investigate these particular relationships. Third, we only focused on a particular dimension of adaptive capability: the man- agement system adaptability [13]. The DCV embraces various capabilities, including sense and respond capabilities, knowledge processes, digital dynamic capability, and strategic flexibility [13, 20, 21, 48-51]. Thus, future work can also unravel the contri- butions of organizational capabilities to business model innovation. References 1. Crittenden, V.L., & Crittenden, W.F.: Building a capable organization: The eight levers of strategy implementation. Business Horizons 51(4), 301-309 (2008). 2. Bock, A.J., Opsahl, T., George, G., & Gann, D.M.: The effects of culture and structure on strategic flexibility during business model innovation. Journal of management studies 49(2), 279-305 (2012). 9 3. Guo, H., Su, Z., & Ahlstrom, D.: Business model innovation: The effects of exploratory orientation, opportunity recognition, and entrepreneurial bricolage in an emerging economy. Asia Pacific Journal of Management 33(2), 533-549 (2016). 4. Teece, D.J.: Business models and dynamic capabilities. Long range planning 51(1), 40-49 (2018). 5. Chesbrough, H.: Business model innovation: it's not just about technology anymore. Strategy & Leadership 35(6), 12 - 17 (2007). 6. Christensen, C.M., Bartman, T., & Bever, D.v.: The hard truth about business model innovation. MIT Sloan Management Review 58(1), 31-40 (2016). 7. Lindgardt, Z., Reeves, M., Stalk, G., & Deimler, M.S.: Business model innovation. When the Game Gets Tough, Change the Game, The Boston Consulting Group, Boston, MA 118 (2009). 8. Anwar, M.: Business model innovation and SMEs performance—does competitive advantage mediate? International Journal of Innovation Management 22(07), 1850057 (2018). 9. Zott, C., & Amit, R.: Business model design and the performance of entrepreneurial firms. Organization Science 18(2), 181-199 (2007). 10. Chesbrough, H.: Business model innovation: opportunities and barriers. Long range planning 43(2-3), 354-363 (2010). 11. Rachinger, M., Rauter, R., Müller, C., Vorraber, W., & Schirgi, E.: Digitalization and its influence on business model innovation. Journal of Manufacturing Technology Management 30(8), 1143-1160 (2018). 12. Gibson, C.B., & Birkinshaw, J.: The antecedents, consequences, and mediating role of organizational ambidexterity. Academy of Management Journal 47(2), 209-226 (2004). 13. Akgün, A.E., Keskin, H., & Byrne, J.: Antecedents and contingent effects of organizational adaptive capability on firm product innovativeness. Journal of Product Innovation Management 29171-189 (2012). 14. Pavlou, P.A., & El Sawy, O.A.: The “third hand”: IT-enabled competitive advantage in turbulence through improvisational capabilities. Information Systems Research 21(3), 443- 471 (2010). 15. Ray, G., Barney, J.B., & Muhanna, W.A.: Capabilities, business processes, and competitive advantage: choosing the dependent variable in empirical tests of the resource‐based view. Strategic Management Journal 25(1), 23-37 (2004). 16. Ismail, A.I., Rose, R.C., Uli, J., & Abdullah, H.: The relationship between organisational resources, capabilities, systems and competitive advantage. Asian Academy of Management Journal 17(1), 151-173 (2012). 17. Wang, C.L., & Ahmed, P.K.: Dynamic capabilities: A review and research agenda. International journal of management reviews 9(1), 31-51 (2007). 18. Zhou, K.Z., & Li, C.B.: How strategic orientations influence the building of dynamic capability in emerging economies. Journal of Business Research 63(3), 224-231 (2010). 19. Van de Wetering, R.: The impact of artificial intelligence ambidexterity and strategic flexibility on operational ambidexterity. In: Proceedings of the Pacific Asia Conference on Information Systems (PACIS) 2022, Taipei/Sydney Virtual Conference (2022). 10 20. Van de Wetering, R.: Understanding the Impact of Enterprise Architecture Driven Dynamic Capabilities on Agility: A Variance and fsQCA Study. Pacific Asia Journal of the Association for Information Systems 13(4), 32-68 (2021). 21. Tuominen, M., Rajala, A., & Möller, K.: How does adaptability drive firm innovativeness? Journal of Business Research 57(5), 495-506 (2004). 22. Senyard, J., Baker, T., & Davidsson, P.: Entrepreneurial bricolage: Towards systematic empirical testing. Frontiers of Entrepreneurship Research 29(5), 1-14 (2009). 23. Van de Wetering, R.: Enterprise Architecture Resources, Dynamic Capabilities, and their Pathways to Operational Value. In: Proceedings of the Fortieth International Conference on Information Systems (ICIS), AIS (2019). 24. Mintzberg, H.: The strategy concept I: Five Ps for strategy. California management review 30(1), 11-24 (1987). 25. Van de Wetering, R., Hendrickx, T., Brinkkemper, S., & Kurnia, S.: The Impact of EA- Driven Dynamic Capabilities, Innovativeness, and Structure on Organizational Benefits: A Variance and fsQCA Perspective. Sustainability 13(10), 5414 (2021). 26. Van de Wetering, R., & Besuyen, M.: How IT-Enabled Dynamic Capabilities Add Value to the Development of Innovation Capabilities, In: D.B.A. Mehdi Khosrow-Pour (eds.) Encyclopedia of Organizational Knowledge, Administration, and Technology, pp. 999- 1016. IGI Global: Hershey, PA, USA (2021). 27. Teece, D., & Leih, S.: Uncertainty, innovation, and dynamic capabilities: An introduction. California management review 58(4), 5-12 (2016). 28. Schoemaker, P.J., Heaton, S., & Teece, D.: Innovation, dynamic capabilities, and leadership. California management review 61(1), 15-42 (2018). 29. Jiang, Y., & Stylos, N.: Triggers of consumers’ enhanced digital engagement and the role of digital technologies in transforming the retail ecosystem during COVID-19 pandemic. Technological Forecasting and Social Change 172 1-19 (2021). 30. e Cunha, M.P., Gomes, E., Mellahi, K., Miner, A.S., & Rego, A.: Strategic agility through improvisational capabilities: Implications for a paradox-sensitive HRM. Human Resource Management Review 30(1), 100695 (2020). 31. Baker, T., Miner, A.S., & Eesley, D.T.: Improvising firms: Bricolage, account giving and improvisational competencies in the founding process. Research Policy 32(2), 255-276 (2003). 32. Keiningham, T., Aksoy, L., Bruce, H.L., Cadet, F., Clennell, N., Hodgkinson, I.R., & Kearney, T.: Customer experience driven business model innovation. Journal of Business Research 116 431-440 (2020). 33. Clauss, T., Kesting, T., & Naskrent, J.: A rolling stone gathers no moss: the effect of customers' perceived business model innovativeness on customer value co‐creation behavior and customer satisfaction in the service sector. R&D Management 49(2), 180-203 (2019). 34. Clauss, T., Breier, M., Kraus, S., Durst, S., & Mahto, R.V.: Temporary business model innovation–SMEs’ innovation response to the Covid‐19 crisis. R&D Management 52(2), 294-312 (2022). 35. Johnson, M.W., & Lafley, A.G.: Seizing the white space. In: Business Model Innovation for Growth and Renewal. Harvard Business School Press, Boston, MA (2010) 11 36. Amit, R., & Zott, C.: Business model innovation: Creating value in times of change. IESE Business School of Navarra, Barcelona. IESE Working Paper, No. WP-870 (2010). 37. Pohle, G., & Chapman, M.: IBM's global CEO report 2006: business model innovation matters. Strategy & leadership (2006). 38. Ucaktürk, A., Bekmezci, M., & Ucaktürk, T.: Prevailing during the periods of economical crisis and recession through business model innovation. Procedia-Social and Behavioral Sciences 2489-100 (2011). 39. Amit, R., & Zott, C.: Creating value through business model innovation. In: Strategy in changing markets: new business models. MIT Sloan Management Review 53310 36-44 (2012). 40. Chen, J.-S., & Tsou, H.-T.: Performance effects of IT capability, service process innovation, and the mediating role of customer service. Journal of Engineering and Technology Management 29(1), 71-94 (2012). 41. Dijkstra, T.K., & Henseler, J.: Linear indices in nonlinear structural equation models: best fitting proper indices and other composites. Quality & Quantity 45(6), 1505-1518 (2011). 42. Henseler, J.: Composite-based structural equation modeling: analyzing latent and emergent variables. 2020: Guilford Publications. 43. Henseler, J.: Partial least squares path modeling: Quo vadis? Quality & Quantity 52(1), 1- 8 (2018). 44. Hair Jr, J.F., Sarstedt, M., Ringle, C.M., & Gudergan, S.P.: Advanced issues in partial least squares structural equation modeling. 2017: SAGE Publications. 45. Petter, S., & Hadavi, Y.: With great power comes great responsibility: The use of partial least squares in information systems research. ACM SIGMIS Database: The DATABASE for Advances in Information Systems 52(SI), 10-23 (2021). 46. Hair, J.F., Risher, J.J., Sarstedt, M., & Ringle, C.M.: When to use and how to report the results of PLS-SEM. European Business Review 31(1) 2-24 (2019). 47. Tsao, W.-C., Hsieh, M.-T., & Lin, T.M.: Intensifying online loyalty! The power of website quality and the perceived value of consumer/seller relationship. Industrial Management & Data Systems 116(9) 1987-2010 (2016). 48. Van de Wetering, R.: Achieving digital-driven patient agility in the era of big data. In: Dennehy, D., Griva, A., Pouloudi, N., Dwivedi, Y.K., Pappas, I., Mäntymäki, M. (eds) Responsible AI and Analytics for an Ethical and Inclusive Digitized Society. I3E 2021. Lecture Notes in Computer Science, vol 12896. Springer, Cham. https://doi.org/10.1007/978-3-030-85447-8_8 (2021). 49. Teece, D.J., Pisano, G., & Shuen, A.: Dynamic capabilities and strategic management. Strategic Management Journal 18(7), 509-533 (1997). 50. Zhou, K.Z., & Wu, F.: Technological capability, strategic flexibility, and product innovation. Strategic Management Journal 31(5), 547-561 (2010). 51. Van de Wetering, R., & Versendaal, J.: Information Technology Ambidexterity, Digital Dynamic Capability, and Knowledge Processes as Enablers of Patient Agility: Empirical Study. JMIRx Med 2(4), e32336 (2021). doi: 10.2196/32336 12 Appendix A: Measurement items Improvisational capabilities (7-point Likert Scale, 1 = Strongly disagree, 7 = Strongly agree) To what extent do you agree with the following statements? IMP1: We apply combinations of business and IT resources at hand to pursue new strategic initiatives such as entering a new market IMP2: We apply combinations of resources at hand for new business operations IMP3: We apply combinations of resources at hand for expansion IMP4: We apply combinations of resources at hand to create new products or services Management system adaptability (7-point Likert Scale, 1 = Strongly disagree, 7 = Strongly agree) To what extent do you agree with the following statements? MSA1: The management systems in this organization encourage people to challenge out- moded traditions/practices/sacred cows MSA2: The management systems in this organization are flexible enough to allow us to re- spond quickly to the current changes in our markets MSA3: The management systems in this organization evolve rapidly in response to shifts in our business priorities Business model innovation (7-point Likert Scale, , 1 = Strongly disagree, 7 = Strongly agree) Please indicate your firm’s capabilities relative to competition for each of the following: BMI1: Our business model offers new combinations of products, services, and information BMI2: Our business model attracts a lot of new customers BMI3: Our business model attracts a lot of new suppliers and partners BMI4: Our business model bonds participants together in novel ways BMI5: Our business model links participants to transactions in novel ways BMI6: We frequently introduce new ideas and innovations into our business model BMI7: We frequently introduce new operational processes, routines, and norms into our busi- ness model BMI8: We are pioneers of the business model BMI9: Overall, our business model is novel Organizational performance during COVID-19 (7-point Likert Scale, , 1 = Strongly disagree, 7 = Strongly agree) For the past few weeks, our company, relatively to our main competitors in the same industry (for non-competing governmental agencies, you could also read competitors as ‘other minis- tries or departments’), has been able to maintain or increase: OP1: Customer satisfaction OP2: Business brand and image OP3: Customer loyalty OP4: Market share OP5: Profitability