Abstract

Several studies have explored firm performance in the post-Covid-19 pandemic era. However, there is not much research to find reports divulging the complex relationship dynamics between business intelligence, organizational and network learning, customer value anticipation, and creative economy-based small-medium enterprises (SMEs) performance in developing countries. This study aims to uncover the complexity of those relationships. The quantitative data were collected from 313 creative economy-based SMEs in East Java, Indonesia. Using PLS-SEM, this study disclosed that business intelligence practices could not directly impact SMEs’ performance. Business intelligence will be crucial to SMEs’ performance with the support of organizational learning as a mediator. The finding also confirmed the presence of serial mediation of organizational learning and innovation in the relationship between business intelligence and SMEs’ performance. However, the role of network learning and innovation is also important, considering their relatively large direct impact on SMEs’ performance. The theoretical implications of this research broke the boundaries of strategic management theory in resource-based view and knowledge-based view in the latest era, where creative economy-based SMEs have been able to mobilize resources to carry out business intelligence to realize innovation and high performance. Further research is suggested to explore the role of business intelligence in promoting specific performance areas, such as marketing performance, financial performance, and human resource management. In addition, it is advisable to choose more specific research subjects, including those in the culinary subsector, and pay attention to other areas, e.g., the demographics of respondents in the model as a control variable.

Introduction

The performance of small-medium enterprises (SMEs) in the post-pandemic era of Covid-19 has been in the spotlight of many scientists, e.g. Ref. [1] highlighted the declining performance of 273 SMEs at the company level operating in commercial, manufacturing, service, or construction industries in certain geographical areas in Finland due to a competitive mindset contaminated by high-performance growth aspirations [2], examined the impact of identity-based entrepreneurial passion on the performance of 193 established SMEs in Ghana [3], identified the mediating role of green product innovation between green entrepreneurial orientation, green transformational leadership, and the performance of 384 manufacturing SMEs operating in Amman, Jordan [4], discussed the export performance of 329 SMEs in Vietnam and how they are developing their internal skills to face external challenges arising from the digitalization process and the COVID-19 crisis [5], analyzed the role of learning in an organization while measuring and managing the sustainable performance of 710 manufacturing sector SMEs operating in Laos [6], examined the impact of international resources on the sustainable performance of 380 Pakistani SMEs through the mediating role of green entrepreneurial orientation [7], tested the influence of organizational creativity and open innovation on the performance of 206 SMEs spread across several regions in Indonesia., and [8] explored how 247 Vietnamese SMEs can leverage information technology (IT) to overcome crises, exploit innovative opportunities, adapt to changing market conditions, and drive new competitive initiatives. This is due to the prominent role of creative economy-based SMEs as the drivers of economic growth in Indonesia and other countries [9]. East Java is no exception; one of the regions contributing 14.92% of Indonesia’s national gross domestic product (GDP) [10]. This province has much potential in the creative economy sector, which is supported by 113 startups [11,12] to face the turbulent business environment as a result of accelerating digital transformation [13] during the Covid-19 pandemic. The number of creative economy-based SMEs will continue to grow, even from the activities of homemakers who are familiar with digital literacy and the ease of doing business today [14].

Conclusion

This research has attempted to investigate the link between business intelligence, organizational learning, network learning, customer value anticipation (CVA), innovation, and creative economy-based SMEs performance in East Java of Indonesia. The findings indicate that business intelligence does not directly impact SMEs’ performance because the data generated from business intelligence practices needs to be integrated into knowledge that must be learned through organizational learning. If this is offset by CVA, it will give birth to innovations that ultimately impact SMEs’ performance. Hence, even though the impact of serial mediation network learning and innovation presence on the relationship between business intelligence and performance is not supported by field data in this study, network learning still need attention to improve SMEs’ performance. The theoretical implications of this research break the boundaries of strategic management theory resource-based view (RBV) and knowledge-based view (KBV) in the latest era, where creative economy-based SMEs in developing countries have been able to mobilize resources to carry out business intelligence, even though in a simple way, to realize Innovation and high performance.

The limitation of this research is that it has yet to explore the causes of the insignificant effect of network learning on innovation. Thus, future research can conduct qualitative studies to obtain more in-depth information regarding the relationship between network learning with Innovation. In addition, this research still uses economy creative-based SMEs as samples, while future research is recommended to involve specific samples, for example, in the culinary sector only, the sector with the highest number in East Java. Researchers can also pay attention to other provinces, such as West Java, which has the largest creative economy exports in Indonesia. Further scholars are also expected to explore the role of business intelligence in more specific performance areas, such as marketing performance, financial performance, and human resource management, and include the demographics of respondents in the model as a control variable. The data distribution in PLS-SEM analysis is only possible to assess statistical significance using the Bootstrapping technique.