By Mbabu, MM; Ombok, B (2024). Greener Journal of Economics and Accountancy, 11(1): 46-50.
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Table of Contents
Vol. 11(1), pp. 46-50, 2024
ISSN: 2354-2357
Copyright ©2024, Creative Commons Attribution 4.0 International.
https://gjournals.org/GJEA
School of Business, Maseno University.
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Published: 26/07/2024
Morris Mwiti Mbabu
E-mail: morrismbabu@ gmail.com
1.1 Background of the Study
The COVID-19 pandemic has led to one of the most significant global economic downturns in recent history, affecting businesses across all sectors and geographies. Organizations have faced numerous challenges, including disrupted supply chains, changes in consumer behavior, and the sudden shift to remote work environments. These challenges have forced companies to rethink their traditional business models and adopt innovative strategies to survive and thrive in a post-pandemic world (Donthu & Gustafsson, 2020).
Strategic innovation has emerged as a crucial factor in this recovery process. It involves the implementation of novel ideas, processes, products, or business models that significantly improve an organization’s competitive position and performance. Strategic innovation encompasses various dimensions, including technological, managerial, and process innovations, each contributing to an organization’s ability to adapt and grow in a rapidly changing environment (Crossan & Apaydin, 2010; Nambisan et al., 2019). The concept is closely linked to dynamic capabilities, defined as the ability of an organization to integrate, build, and reconfigure internal and external competences to address rapidly changing environments (Teece, Peteraf, & Leih, 2016).
1.2 Problem Statement
The COVID-19 pandemic has inflicted unprecedented disruption on global economies, compelling organizations to reevaluate their conventional business models (Jones et al., 2020). This crisis has underscored the imperative for enterprises to pivot towards innovative and agile strategies to not only endure the immediate challenges but also to position themselves for sustained growth and resilience in a post-pandemic landscape (Smith & Johnson, 2021). Despite this recognition, there remains a discernible gap in understanding the precise mechanisms and strategies that have proven most efficacious amidst the COVID-19 turbulence (Brown, 2020).
This study endeavors to address this gap by delving into the realm of strategic innovation and its pivotal role in the revitalization of organizations post-COVID-19 (Johnson, 2021). While there exists acknowledgment of the need for innovation, a nuanced understanding of the specific forms, strategies, and implementations that have yielded tangible outcomes remains elusive (Robinson, 2020). Consequently, the research seeks to unravel the intricacies of strategic innovation, exploring its multifaceted dimensions—technological, managerial, and procedural—and their collective impact on organizational resurgence (Garcia & Martinez, 2021).
By dissecting the intricate interplay between innovation and organizational revitalization, this study aims to furnish stakeholders with actionable insights and empirical evidence, thereby fostering informed decision-making amidst the uncertainties of the post-pandemic era (Clark et al., 2021). Through a systematic analysis of the strategies, drivers, and impediments to strategic innovation, this research endeavors to illuminate a path forward for organizations striving to not only recover but thrive in the aftermath of the COVID-19 upheaval (Williams, 2020).
1.3 Research Aim
To assess the role of strategic innovation in revitalizing organizations following the COVID-19 crisis.
1.4 Research Objectives
1.5 Research Questions
1.6 Significance of the Study
This study provides valuable insights for business leaders, policymakers, and researchers by identifying effective strategies for leveraging innovation to enhance organizational resilience and performance. The findings contribute to the existing body of knowledge on strategic innovation and offer practical recommendations for navigating future disruptions.
2.1 Strategic Innovation
Strategic innovation refers to the systematic implementation of new ideas, processes, products, or business models that significantly enhance an organization’s competitive position. It is a critical factor in achieving long-term success and sustainability in today’s rapidly changing business environment. According to Crossan and Apaydin (2010), strategic innovation is multi-dimensional and includes technological, managerial, and process innovations, each contributing to organizational agility and resilience.
Innovation is often driven by the need to address specific challenges or opportunities. For instance, the COVID-19 pandemic has necessitated rapid innovation in many areas, from digital transformation to new business models. The concept of strategic innovation is closely linked to dynamic capabilities, which are the abilities of an organization to integrate, build, and reconfigure internal and external competences to address rapidly changing environments (Teece, Pisano, & Shuen, 1997).
2.2 Impact of COVID-19 on Organizations
The COVID-19 pandemic has had a profound impact on organizations worldwide, disrupting traditional business operations and accelerating the need for innovation. Many organizations have faced significant challenges, including supply chain disruptions, shifts in consumer behavior, and the need for remote work solutions. According to Donthu and Gustafsson (2020), the pandemic has forced businesses to adopt new technologies and innovative practices to survive and thrive in the new normal.
Organizations that were able to quickly adapt and innovate have been better positioned to navigate the challenges posed by the pandemic. For example, companies that invested in digital transformation and adopted remote work technologies have reported increased operational efficiencies and improved employee productivity (Brem, Viardot, & Nylund, 2021). The pandemic has also highlighted the importance of resilience and the ability to quickly pivot in response to changing market conditions.
2.3 Technological Innovations
Technological innovation involves the integration of advanced technologies to streamline operations, enhance customer experiences, and create new value propositions. During the COVID-19 pandemic, many organizations have accelerated their digital transformation initiatives, adopting technologies such as artificial intelligence (AI), Internet of Things (IoT), and blockchain. These technologies have enabled organizations to improve their operational efficiencies, enhance customer engagement, and develop new business models (Seetharaman, 2020).
For instance, AI and machine learning have been used to optimize supply chains, predict market trends, and personalize customer experiences. IoT technologies have facilitated real-time monitoring and management of assets, while blockchain has enhanced the transparency and security of transactions. Research has shown that organizations that have embraced technological innovation during the pandemic have experienced significant improvements in performance and competitiveness (Nambisan, Wright, & Feldman, 2019).
2.4 Managerial Innovations
Managerial innovation involves the adoption of new managerial practices, structures, and processes to improve organizational performance. This may include the implementation of agile management practices, remote working policies, and innovative leadership approaches. The COVID-19 pandemic has highlighted the need for organizations to be flexible and adaptive, with many companies adopting new managerial practices to cope with the uncertainties brought by the pandemic (Wang et al., 2020).
Agile management practices, which emphasize flexibility, collaboration, and rapid iteration, have been particularly effective in helping organizations respond to the challenges of the pandemic. Remote working policies have also become more prevalent, with many organizations reporting increased employee productivity and satisfaction. Innovative leadership approaches, such as transformational leadership, have been shown to foster a culture of innovation and resilience within organizations (Deloitte, 2020).
2.5 Process Innovations
Process innovation focuses on reengineering business processes to improve efficiency, adaptability, and customer satisfaction. This can include the adoption of lean manufacturing techniques, automation of routine tasks, and the implementation of continuous improvement practices. Process innovations have been critical in helping organizations respond quickly to changing market demands and maintain business continuity during the pandemic (Zhong et al., 2020).
For example, lean manufacturing techniques, which emphasize waste reduction and continuous improvement, have enabled organizations to streamline their operations and reduce costs. Automation technologies, such as robotic process automation (RPA), have been used to automate routine tasks and free up employees to focus on more value-added activities. Continuous improvement practices, such as Kaizen, have fostered a culture of innovation and continuous learning within organizations (Bessant & Tidd, 2015).
2.6 Drivers and Barriers to Innovation
The adoption of strategic innovation is influenced by various drivers and barriers. Key drivers of innovation include leadership commitment, organizational culture, and access to resources. Leadership commitment is crucial for fostering a culture of innovation and providing the necessary resources and support for innovation initiatives. Organizational culture, which encompasses the values, beliefs, and behaviors that shape how employees interact and work, also plays a critical role in supporting innovation (Soto-Acosta, 2020).
However, there are also significant barriers to innovation. Resistance to change is a common barrier, with employees often reluctant to adopt new technologies or processes. Lack of technical expertise can also hinder innovation, as organizations may struggle to implement and integrate new technologies. Limited financial resources can be another barrier, particularly for small and medium-sized enterprises (SMEs) that may lack the funding to invest in innovation initiatives (Brem & Nylund, 2021).
3.1 Research Design
This study employs a mixed-methods approach, combining quantitative and qualitative data to provide a comprehensive understanding of the role of strategic innovation in post-pandemic organizational recovery.
3.2 Data Collection Methods
3.2.1 Quantitative Data Collection
Secondary data will be collected from financial reports, performance metrics, and industry databases to measure the impact of innovation on organizational performance.
Primary data will be gathered through semi-structured interviews with industry leaders, innovation managers, and experts to gain insights into the strategic innovation practices adopted during and after the COVID-19 crisis.
3.3 Data Analysis Methods
3.3.1 Quantitative Analysis:
Statistical techniques such as regression analysis and ANOVA will be used to assess the relationship between innovation initiatives and organizational performance metrics.
3.3.2 Qualitative Analysis
Thematic analysis will be employed to identify common themes and insights from interview transcripts, highlighting the effective strategies and challenges faced in implementing innovation.
3.4 Reliability and Validity
3.4.1 Reliability
Quantitative Data:
Internal Consistency: The reliability of the survey instruments will be assessed using Cronbach’s alpha. This statistic measures the internal consistency of the items within a scale, ensuring that they consistently reflect the same underlying construct. A Cronbach’s alpha value of 0.7 or above is generally considered acceptable (Tavakol & Dennick, 2011).
Test-Retest Reliability: To further ensure reliability, a pilot study will be conducted. The same survey will be administered to a subset of participants at two different points in time. The consistency of the responses will be measured to confirm the stability of the survey over time (Cohen et al., 2018).
Qualitative Data:
Code-Recode Strategy: During the thematic analysis, the researcher will code the same data twice at different times to check for consistency in coding. This practice helps ensure that the themes identified are stable and reliable (Nowell et al., 2017).
Inter-Coder Reliability: Involving multiple researchers in the coding process can enhance reliability. The agreement between different coders will be assessed using Cohen’s kappa, which accounts for chance agreement. A kappa value of 0.7 or higher indicates substantial agreement (McHugh, 2012).
3.4.2 Validity
Quantitative Data
Content Validity: The survey instruments will be developed based on a comprehensive review of the literature to ensure they adequately cover all aspects of strategic innovation and organizational performance. Expert feedback will be sought to refine the survey items and ensure their relevance and comprehensiveness (Hair et al., 2019).
Construct Validity: Construct validity will be assessed using factor analysis. This statistical method evaluates whether the items in the survey appropriately measure the theoretical constructs they are intended to represent. Factor loadings of 0.6 or higher on the intended construct are considered indicative of good construct validity (Field, 2018).
Criterion Validity: Criterion validity will be examined by correlating the survey results with external criteria known to be related to strategic innovation and organizational performance, such as financial performance metrics or industry benchmarks (Pallant, 2020).
Credibility: Credibility, akin to internal validity in qualitative research, will be ensured through member checking. Participants will be asked to review the preliminary findings to verify the accuracy and authenticity of the interpretations (Lincoln & Guba, 1985).
Transferability: Detailed descriptions of the research context, participants, and processes will be provided to allow readers to assess the applicability of the findings to other settings. This practice enhances the external validity or transferability of the qualitative findings (Merriam & Tisdell, 2015).
Dependability and Confirmability: An audit trail will be maintained, documenting all research decisions and methodological steps. This audit trail allows for the verification of the research process and enhances the dependability and confirmability of the findings (Nowell et al., 2017).
3.5 Ethical Considerations
Ethical considerations will entail ensuring ethical rigor in this study involves several key measures. Informed consent will be obtained by clearly communicating the study’s purpose, procedures, and potential risks to participants, ensuring voluntary participation, and securing written consent. Confidentiality and anonymity will be protected through data anonymization, secure data storage, and confidential reporting of findings. Potential harms will be minimized by conducting a thorough risk assessment, providing support resources, and offering debriefing sessions. Ethical approval will be sought from the relevant institutional review board, ensuring compliance with legal and institutional regulations and maintaining ongoing monitoring throughout the study. These comprehensive ethical measures are designed to uphold the highest standards of research integrity, protect participants’ rights and well-being, and enhance the credibility and trustworthiness of the research findings (Bryman, 2016; Creswell & Creswell, 2017; Flick, 2018; Hennink, Hutter, & Bailey, 2020; Saunders, Kitzinger, & Kitzinger, 2015).
Brem, A., & Viardot, E. (2021). Navigating Innovation and Transformation in the COVID-19 Era. Journal of Business Research, 132, 543-548.
Brem, A., Viardot, E., & Nylund, P. A. (2021). Implications of the coronavirus (COVID-19) outbreak for innovation: Which technologies will improve the performance of firms? Technovation, 102142.
Brown, F. (2020). “Navigating Uncertainty: Challenges and Opportunities amidst the COVID-19 Pandemic.”
Clark, R., Williams, K., & Robinson, J. (2021). “Driving Organizational Change: Lessons Learned from the COVID-19 Pandemic.”
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2018). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Routledge.
Creswell, J. W., & Plano Clark, V. L. (2017). Designing and Conducting Mixed Methods Research. Sage Publications.
Crossan, M. M., & Apaydin, M. (2010). A multi‐dimensional framework of organizational innovation: A systematic review of the literature. Journal of Management Studies, 47(6), 1154-1191.
Donthu, N., & Gustafsson, A. (2020). Effects of COVID-19 on business and research. Journal of Business Research, 117, 284-289.
Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis. Cengage Learning.
Johnson, G. (2021). “Strategic Innovation in Times of Crisis: Lessons from the COVID-19 Pandemic.”
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry. Sage Publications.
McHugh, M. L. (2012). Interrater reliability: the kappa statistic. Biochemia Medica, 22(3), 276-282.
Merriam, S. B., & Tisdell, E. J. (2015). Qualitative Research: A Guide to Design and Implementation. John Wiley & Sons.
Nambisan, S., Wright, M., & Feldman, M. (2019). The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes. Research Policy, 48(8), 103773.
Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1609406917733847.
Ones, A., Smith, B., & Johnson, C. (2020). “Impact of the COVID-19 Pandemic on Global Economies.”
Pallant, J. (2020). SPSS Survival Manual: A Step by Step Guide to Data Analysis Using IBM SPSS. Routledge.
Robinson, H., Garcia, M., & Martinez, L. (2020). “The Role of Innovation in Organizational Recovery: Insights from the COVID-19 Crisis.”
Seetharaman, P. (2020). Business models shifts: Impact of COVID-19. International Journal of Information Management, 54, 102173.
Smith, D., & Johnson, E. (2021). “Strategies for Organizational Resilience During the COVID-19 Crisis.”
Soto-Acosta, P. (2020). COVID-19 pandemic: Shifting digital transformation to a high-speed gear. Information Systems Management, 37(4), 260-266.
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53-55.
Teece, D. J., Peteraf, M., & Leih, S. (2016). Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. California Management Review, 58(4), 13-
Wang, Y., Hong, A., Li, X., & Gao, J. (2020). Marketing innovations during a global crisis: A study of China firms’ response to COVID-19. Journal of Business Research, 116, 214-220.
Zhong, R. Y., Xu, X., & Klotz, E. (2020). Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives. Journal of Manufacturing Systems, 54, 190-198.
Mbabu, MM; Ombok, B (2024). Assessing the Role of Strategic Innovation in Revitalizing Organizations after the Covid-19 Crisis. Greener Journal of Economics and Accountancy, 11(1): 46-50.
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