International Journal of Innovation and Technology Management

This study embarks on a comprehensive bibliometric analysis to elucidate the intricate relationship between knowledge management capabilities and perceived performance within information technology small and medium-sized enterprises (IT SMEs). Utilizing data from the Web of Science and Scopus databases, this research meticulously examines occurrences, word frequencies, and clustering analyses to identify pivotal thematic areas influencing IT SMEs. By employing advanced tools such as RStudio software and the Biblioshiny application, the study dissects average citations per year, presenting a dynamic view of research impact and evolving significance in the domain. Authorship dynamics are explored through Lotka’s law, and geographic contributions are analyzed, highlighting the global and collaborative essence of knowledge management research. Factorial analysis further reveals layered dimensions of research trends, offering guidance for researchers and practitioners in crafting nuanced strategies. The collective insights underscore the paramount role of knowledge management in enhancing IT SMEs’ performance and competitive edge, marking a significant stride toward understanding and implementing effective knowledge management strategies in the rapidly evolving IT landscape.

This study investigates how incubators provide feasible solutions to increase the opportunities for exploratory innovation (EI) of small business enterprises (SBEs). Participation in cross-industry collaborations (CICs) bridged by incubators provides the foundation for the view that SBEs access cross-industry knowledge (CIK) to increase their potential for EI. Hypotheses are tested using CICs of 489 SBEs of Taiwanese university-based incubators. Results show that accessing CIK has a significant and positive influence on the EI of SBEs. Business assistance, technological assistance, and coaching service also strengthen the benefits of CIK for EI. Business assistance and coaching service allow SBEs to increase EI regardless of access to CIK. However, this positive effect cannot be achieved through technological assistance alone but rather in combination with access to CIK.

This study uses an extensive sample of company data referring to seven Latin American countries to analyze the potential influence of simultaneity and heterogeneity in determining decisions to commit to R&D and innovation outputs. First, the analysis focuses on the decisions about spending on in-house and external research. Second, the outcomes of three different innovations: process, product, and organizational. Choices were considered simultaneously by employing a multivariate probit estimation model. In so doing, the enquiry considered the possible systematic interdependencies among the decisions. The results indicate that the two R&D decisions are interdependent. However, a different picture emerged in relation to innovation outputs, in which the correlations coefficients were small with unexpected signs. This may indicate a structural weakness of the technological production system in Latin America, wherein the synergies, complementarities, and complex relationships usually involved in the process of innovation are not fully developed.

Background: In an era where technological advancements are reshaping the global workforce, understanding the dynamics of technology adoption within the public sector becomes paramount. This study examines how employees in the Ghanaian public sector respond to technology readiness (TR) in terms of their career adaptability (CA) and innovative work behavior (IWB). The uniqueness of this research lies in its empirical investigation of how TR’s motivational and inhibitory dimensions influence CA and, subsequently, IWB, within a context marked by rapid digital transformation. This study is essential due to the key role of Ghana’s public sector employees in national development, especially with ongoing digitization. It fills a research gap by assessing how these employees’ psychological readiness for technology affects their careers and innovation.

Method: The study used a cross-sectional survey design and collected data from 484 public sector employees and their supervisors. Partial least squares structural equation modeling (PLS-SEM) was used to test the hypotheses. Results: Findings reveal that optimism, insecurity, and discomfort — components of TR — exert a significant influence on CA, which in turn fosters IWB. Interestingly, innovativeness did not predict CA, suggesting a nuanced interplay between TR dimensions and adaptability. Implications: By elucidating the factors that drive CA and IWB in response to TR, this study offers valuable insights for policymakers and organizational leaders seeking to enhance employee adaptability and innovation amidst technological changes. It underscores the importance of nurturing an optimistic and secure technological environment to cultivate a resilient and forward-thinking public sector workforce. Originality/Value: This study pioneers the exploration of TR’s impact on CA and IWB, utilizing the career construction model of adaptation as a theoretical lens. The findings provide actionable strategies for bolstering the public sector’s technological and innovative capacity, informed by the empirical evidence from the surveyed sample.

Banks are increasingly motivated to align their business operations with the United Nations Sustainable Development Goals (SDGs) as this can help them attract investors and build a more loyal customer base in the long run. This paper explores whether major UK banks (HSBC Holdings Plc, Lloyds Banking Group Plc, Barclays Plc, Standard Chartered Plc, and NatWest Group Plc) are increasing their involvement in green banking by transitioning away from fossil fuels. The research gathered expert and policymaker views on decarbonization (reducing carbon footprint) at the Scotland Policy Conference UK in 2022 to support its findings. The results reveal slow progress toward a green banking regime, as the banks continued underwriting fossil fuel IPOs and bond issuances, and funding carbon-intensive industries. Their portfolios primarily consist of fossil fuel projects, with implications for insufficient effort toward meeting UN SDGs related to global climate commitments. The study proposes a policy recommendation: governments should introduce regulations requiring banks to develop a minimum percentage of financial products and services designed to support SDG-related activities, fostering a low-carbon economy.

The research attempts to determine the inclination toward embracing e-commerce, focusing on the satisfaction of low- and middle-income consumers with online services and their ability to navigate a secure online platform. We examine our hypothesis with survey technique. The questionnaire was adapted from prior studies. The research was carried out through empirical methods, utilizing a convenience sample of 290 participants from various locations in Barishal, Chattogram, Dhaka, Khulna, Rajshahi, Rangpur, Mymensingh, and Sylhet, from Bangladesh. SMARTPLS was used for analysis of the gathered data. The findings demonstrated that the desire to utilize e-commerce is strongly and positively predicted by perceived risk, social influence, and facilitating aspects. This study addresses the gap by identifying and understanding these three aspects together, beyond contextual barriers, that influence e-commerce adoption among low- and middle-income consumers in Bangladesh. The study contributes to the understanding of e-commerce adoption in emerging markets by providing a comprehensive, theoretically grounded model that identifies key institutional and contextual elements influencing online shopping behavior. The insights gained offer valuable implications for policymakers and e-commerce platforms aiming to enhance user engagement in online shopping. However, the lack of thorough study that addresses problems outside contextual imperatives limits our knowledge of what motivates e-commerce in Bangladesh. This study examines a comprehensive, theoretically developed model that find the pertinent institutional and contextual elements that might lead to the uptake of online shopping in underdeveloped countries.

Countries have traditionally relied on various innovation measures to plan national-wise strategies and analysis, which hinders their ability to take proactive actions beforehand. This study introduces an advanced model for predicting national innovativeness using machine learning techniques. Utilizing a comprehensive dataset that includes 1,410 observation points and 20 variables from 141 countries spanning from 2011 to 2020, the data were sourced from the World Bank Open Data, Global Entrepreneurship Monitor (GEM) and Global Innovation Index (GII). The proposed model employs machine learning algorithms such as regression tree, random forest, support vector machine and extreme gradient boosting (XGBoost), and benchmarks their performance against the traditional linear regression analysis method. The findings reveal that all five machine learning models significantly outperform the traditional linear regression model in terms of correlation, root mean square error (RMSE) and mean absolute error (MAE), with XGBoost demonstrating superior performance. The XGBoost analysis highlights university and industry research collaboration, knowledge workers and logistics performance as the most critical variables influencing national innovativeness. This research not only presents a robust predictive model leveraging machine learning but also contributes to theoretical advancements by uncovering previously overlooked variables, offering new insights and practical implications for enhancing national innovation strategies.