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The empirical results of the pivotal relationship between big data analytics capability (BDAC) and firm innovation remain inconclusive, necessitating a comprehensive understanding of the mediator and moderator through which firms can realize the potential innovation benefits of BDAC. Invoking the indirect perspective of dynamic capability theory, we constructed a moderated mediation model in which organizational learning mediates the impact of BDAC on firm innovation; the mediation effect of organizational learning is contingent upon market orientation.

Our hypotheses were tested using hierarchical regression and bootstrapping methods with a sample of 227 large- and medium-sized manufacturing firms in China.

The results reveal that both exploratory and exploitative learning fully mediate the link between BDAC and firm innovation. The mediation effect of exploitative learning is positively contingent upon market orientation; however, market orientation does not positively moderate the mediation effect of exploratory learning.

Our moderated mediation model is one of the first to provide a fine-grained understanding of the process through which BDAC is transformed into firm innovation as well as the conditions under which this mediating mechanism may work effectively, thereby further elucidating the theoretical black box regarding the BDAC-firm innovation link and resolving existing debates in the literature regarding why BDAC does not always yield positive outcomes.

The purpose of this study is to explore the means by which exporters foster innovation via the learning-by-exporting effect and to appraise the moderating role of employee human capital in the export–innovation relationship.

Leveraging the linked-survey-secondary data from the Human Capital Corporate Panel (HCCP) spanning 2011–2017, with 890 observations from 228 Korean exporters, this study utilizes Generalized Least Squares (GLS) regression to empirically test the proposed hypotheses.

The results indicate that exporting significantly boosts a firm’s innovation performance by encouraging the generation of new concepts in products, services, technologies and/or production lines. Moreover, the presence of international talent and highly educated staff positively moderates the relationship between export intensity and innovation performance.

By integrating organizational learning and human capital theories, this study yields theoretical and managerial insights by elucidating the roles of exporting and human capital in advancing innovation performance, thereby guiding corporate export strategies and human resource policies.

The main objective of this paper is to analyze how leadership unity (LU) within universities affects the innovativeness of faculty, with a focus on the potential moderating role of strategic sensitivity (SS).

The conceptual model of this research shows that SS and LU of the university impact the faculty's innovativeness. Meantime, the moderating effect of SS is assessed. A 49-item questionnaire was administered to 350 respondents who were managers and faculties of the university. The hierarchical regression technique was used for analyzing data and testing hypotheses.

The findings support both a curvilinear relationship based on a concave upward pattern and a linear relationship between LU in the university and the innovativeness of faculty. In addition, the university's SS positively influences the faculty's innovativeness. SS negatively moderates the curvilinear relationship between LU and faculty's innovativeness, i.e. the U-shaped effect exists only when the level of SS is high.

The results of this study shed new light on the relationships between LU and SS with innovativeness in the higher education landscape. It underlines the importance of SS as a moderator in the relationship between LU and innovativeness. This study was conducted in a developing country under sanctions with an Eastern culture, Iran. Thus, it is recommended that the conceptual framework of this study be tested in different countries with cultural diversity to generalize its findings.

Administrators of universities need to recognize that creating unity and cohesion among managers of various levels of the university is crucial. They should also be aware that responses to external changes can lead to new opportunities for the university. Embracing transformation within the organizational strategies of the university will have a significant influence on competition, politics, and internal operations.

This research contributes to the academic discussions on the importance of LU and SS and also the moderation effect of SS in driving and promoting innovativeness in among faculties by providing empirical evidence. The results present valuable insights for scholars, practitioners and policymakers seeking to understand innovativeness among faculties in the higher education setting.

Our aim was to elucidate how leaders’ behaviors may impact innovation and organizational learning in a fast-changing, human-centric and sustainability responsive AI-driven Industry 5.0 environment.

An unsystematic narrative review of relevant literature was conducted focusing on the influence of leadership behaviors on innovation and learning in Industry 5.0 environment.

We found that leadership behaviors that align with Industry 5.0 demands and values must emphasize collaboration, empathy, and continuous learning. The translation of leaders’ actions into desired outcomes requires a psychologically safe work environment, ensuing team cohesion, empowering team members, promoting a learning culture, engendering trust, and vision and goal alignment.

Being aware of leadership qualities required in Industry 5.0 environment, characterized by machine–human collaboration, sustainable innovations, and continuous learning, enables organizations to focus their recruitment efforts on leaders’ characteristics that align with this environment. It also helps them design suitable leaders’ training and development programs. This study requires further expansion and empirical testing to validate the proposed model.

There is a plethora of studies on leadership in various contexts; however, there is very little research on the type of leadership that maybe effective in the fast changing and AI-driven Industry 5.0 environment. The findings of this paper shed light on such a leadership.

For over a decade, fintech has challenged traditional business models and processes in the financial services industry. The ongoing disruption has necessitated the digital transformation of financial institutions (FIs) to remain an integral part of the financial system. This paradigm shift is not merely a technological update. Still, it signifies a cultural and operational rebirth, compelling FIs to embrace innovation, adaptability, and a customer-centric approach in the digital era. Independent of the business model, FIs must become digitally ambidextrous, offer tailored and dynamic customer experiences, support financial inclusion, and promote an environmental, social, and governance (ESG) agenda while leveraging data and remaining compliant. From digitalization to fintegration, the financial services industry's future is deemed to be an exciting and productive one.

Business Model Innovation is increasingly created by an ecosystem of related companies. This paper aims to investigate the transition of a manufacturing ecosystem toward electric vehicles from a business model perspective.

The authors investigate an automotive manufacturing ecosystem that is in transition toward electric and electrified vehicles, conducting semi-structured interviews with 46 informants from 27 ecosystem members.

The results reveal that the actions of several ecosystem members are driven by regulations relating to emissions. Novel requirements regarding components and complementary offers necessitate the entry of actors from other industries and the formation of new ecosystem members. While the newly emerged ecosystem has roots in an established ecosystem, it relies on new value offers. Further, the findings highlight the importance of ecosystem governance, while the necessary degree of change in the members' business models depends on their roles and positions in the ecosystem. Therefore, upstream suppliers of components must perform business model adaptation, whereas downstream providers must perform more complex business model innovation.

The paper is among the first to investigate an entire manufacturing ecosystem and analyze its transition toward electric vehicles and the implications for business model innovation.

Health technologies are advancing rapidly and becoming more expensive, posing a challenge for financing healthcare systems. Health technology assessment (HTA) improves the efficiency of resource allocation by facilitating evidence-informed decisions on the value of health technologies. Our study aims to create a customized HTA roadmap for Oman based on a gap analysis between the current and future status of HTA implementation.

We surveyed participants of an advanced HTA training program to assess the current state of HTA implementation in Oman and explore long-term goals. A list of draft recommendations was developed in areas with room for improvement. The list was then validated for its feasibility in a round table discussion with senior health policy experts to conclude on specific actions for HTA implementation.

Survey results aligned well with expert discussions. The round table discussion concluded with a phasic action plan for HTA implementation. In the short term (1–2 years), efforts will focus on building capacity through training programs. For medium-term actions (3–5 years), plans include expanding the HTA unit and introducing multiple cost-effectiveness thresholds while from 6–10 years, publishing of HTA recommendations, critical appraisal reports, and timelines is recommended.

Although the HTA system in Oman is still in its early stages, strong initiatives are being taken for its advancement. This structured approach ensures a comprehensive integration of HTA into the healthcare system, enhancing decision-making and promoting a sustainable, evidence-based system addressing the population’s needs.

The purpose of this paper is to investigate the factors influencing the development of social media marketing strategy in an international context. We specifically look at the potential drivers and barriers throughout the social media marketing strategy development process and how cultural differences shape social media marketing strategy decision-making among firms in international markets.

The study is conducted with an inductive research approach involving in-depth interviews with 32 firms from Finland, China and Brazil. Using inductive data analysis, we identify both internal and external factors that drive and hinder the development of firms’ social media marketing strategies. Moreover, we explore the essential elements in social media marketing strategy development based on the key practices observed among these firms, which enables us to conduct a comparative analysis of how cultural values influence the development of social media marketing strategies.

Our findings underscore the importance of both internal (i.e. resources and capabilities) and external (i.e. market-level and country-level) factors that influence the development of social media marketing strategy. Our analysis also unveiled four key practices throughout the social media marketing strategy development process: social selling, content marketing, risk management and relationship management. Additionally, we identified three distinct mindsets regarding firms’ social media selling objectives across companies in the three countries.

The comparative approach provides novel insight into firms' international social media marketing strategy. Our proposed conceptual model shows the development process of social media marketing strategy in the international context. The research propositions highlight the role of cultural values and open up new avenues for future research.

Total productive maintenance consists of strategies and procedures that aim to guarantee the entire functioning of machines in a production process so that production is not interrupted and no loss of quality in the final product occurs. Planned maintenance is one of the eight pillars of total productive maintenance, a set of tools considered essential to ensure equipment reliability and availability, reduce unplanned stoppage and increase productivity. This study aims to analyze the influence of statistical reliability on the performance of such a pillar.

In this study, we utilized a multi-method approach to rigorously examine the impact of statistical reliability on the planned maintenance pillar within total productive maintenance. Our methodology combined a detailed statistical analysis of maintenance data with advanced reliability modeling, specifically employing Weibull distribution to analyze failure patterns. Additionally, we integrated qualitative insights gathered through semi-structured interviews with the maintenance team, enhancing the depth of our analysis. The case study, conducted in a fertilizer granulation plant, focused on a critical failure in the granulator pillow block bearing, providing a comprehensive perspective on the practical application of statistical reliability within total productive maintenance; and not presupposing statistical reliability is the solution over more effective methods for the case.

Our findings reveal that the integration of statistical reliability within the planned maintenance pillar significantly enhances predictive maintenance capabilities, leading to more accurate forecasts of equipment failure modes. The Weibull analysis of the granulator pillow block bearing indicated a mean time between failures of 191.3 days, providing support for optimizing maintenance schedules. Moreover, the qualitative insights from the maintenance team highlighted the operational benefits of our approach, such as improved resource allocation and the need for specialized training. These results demonstrate the practical impact of statistical reliability in preventing unplanned downtimes and informing strategic decisions in maintenance planning, thereby emphasizing the importance of your work in the field.

In terms of the originality and practicality of this study, we emphasize the significant findings that underscore the positive influence of using statistical reliability in conjunction with the planned maintenance pillar. This approach can be instrumental in designing and enhancing component preventive maintenance plans. Furthermore, it can effectively manage equipment failure modes and monitor their useful life, providing valuable insights for professionals in total productive maintenance.

Based on the theoretical predictions of media equation theory and the computers-are-social-actors (CASA) perspective, this study aims to examine the effects of performance error type (i.e. logical, semantic or syntactic), task type and personality presentation (i.e. dominant/submissive and/or friendly/unfriendly) on users’ level of trust in their personal digital assistant (PDA), Siri.

An experimental study of human–PDA interactions was performed with two types of tasks (social vs functional) randomly assigned to participants (N = 163). While interacting with Siri in 15 task inquiries, the participants recorded Siri’s answers for each inquiry and self-rated their trust in the PDA. The answers were coded and rated by the researchers for personality presentation and error type.

Logical errors were the most detrimental to user trust. Users’ trust of Siri was significantly higher after functional tasks compared to social tasks when the effects of general usage (e.g. proficiency, length and frequency of usage) were controlled for. The perception of a friendly personality from Siri had an opposite effect on social and functional tasks in the perceived reliability dimension of trust and increased intensity of the presented personality reduced perceived reliability in functional tasks.

The research findings contradict predictions from media equation theory and the CASA perspective while contributing to a theoretical refinement of machine errors and their impact on user trust.