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This study investigates the pathways for adopting IoTs and BDA technologies to improve healthcare management.

The study relied on 445 healthcare professionals' perspectives to explore different causal pathways to IoTs and BDA adoption and usage for daily healthcare management. The Fussy-set Qualitative Comparative Analysis was adopted to explore the underlying pathways for healthcare management.

The empirical analysis revealed six different configural paths influencing the acceptance and use of IoTs and BDA for healthcare improvement. Two key user topologies from the six configural paths, digital literacy and ease of use and social influence and behavioural intentions, mostly affect the paths for using digital health technologies by healthcare physicians.

Despite this study's novel contributions, limitations include the fsQCA methodology, perceptual data and the context of the study. The fsQCA methodology is still evolving with different interpretations, although it reveals new insights and as such further studies are required to explain the configural paths of social phenomena. Additionally, future research should consider other constructs beyond the UTAUT and digital literacy to illustrate configural paths to healthcare technology acceptance and usage. Again, the views of healthcare professionals are perceptual data. Hence future research on operational data will support significant contributions towards pathways to accept and use emerging technologies for healthcare improvement. Lastly, this study is from a developing country perspective where emerging digital healthcare technology is still emerging to support healthcare management. Hence, more investigation from other cross-country analyses of configural paths for digital technology deployment in healthcare will enhance the conversation with IoTs and BDA for healthcare management.

Holistically, the acceptance and use of healthcare technologies and platforms is not solely on their capabilities, but a combination of distinct factors driven by users' perspectives. This offers healthcare administrators and institutions to essentially reflect on the distinct combinations of conditions favourable to health professionals who can use IoTs and BDA for healthcare improvement.

This study is among the few scholarly works to empirically investigate the configural paths to support healthcare improvement with emerging technologies. Using fsQCA is a unique contribution to existing information system literature for configural paths for healthcare improvement with emerging digital technologies.

This paper examines the evolution of massive open online courses (MOOCs) literature over the past 15 years and identifies its significant developments.

Utilizing main path analysis (MPA) on a dataset of 1,613 articles from the Web of Science (WoS) databases, the authors construct the main pathway in MOOC literature through a citation analysis. Pajek software is used to visualize the 34 influential articles identified in the field.

Three phases emerge in MOOC research: connectivism as a learning theory, facilitating education reform and breaking barriers to MOOCs adoption. Multiple-Global MPA highlights sub-themes including self-regulated learning (SRL), motivation, engagement, dropouts, student performance and the impact of COVID-19.

First, data limitations from the WoS core collection might not cover all research, but using reputable sources enhances data validity. Second, despite careful algorithm selection to enhance accuracy, there remains a limitation inherent in the nature of citations. Such biased citations may result in findings that do not fully align with scholars' perspectives.

The authors' findings contribute to the understanding of MOOCs literature development, enabling educators and researchers to grasp key trends and focus areas in the field. It can inform the design and implementation of MOOCs for more effective educational outcomes.

This study presents novel methodologies and important findings for advancing research and practice in MOOCs.

The purpose of this paper is to assess the validity and utility of two theoretical approaches to understanding the relationship between strategic orientations, innovation and firm performance and to examine the role of innovation in the relationship while avoiding circular arguments. The universalistic approach suggests that strategic orientations have independent and parallel effects on firms’ performance, and that innovation does not influence this relationship. The holistic approach proposes that strategic orientations in a complementary and interrelated view have both direct and indirect effects on firms’ performance through innovation.

A meta-analytic path analysis applying two-stage structural equation modeling (TSSEM) was conducted on data from 132 primary studies and 33,063 observations.

The holistic approach was demonstrated to be superior due to its more explanatory power in linking more complex relationships through simultaneous direct and indirect effects and its capacity for including the interrelatedness and complementarity of strategic orientations. It was found that innovation has a full mediating role in the relationship between entrepreneurial orientation (EO) and firm performance, and a partial mediating role in the relationship between market orientation (MO) and learning orientation (LO) and firm performance.

The study used observed variables instead of latent variables for meta-analytic path analysis, which may reduce some sources of endogeneity. However, causal inference is not possible due to the nature of meta-analysis. The scope of the final sample was limited by some studies not reporting the estimates of correlations between constructs.

Managers can improve an organization's chances of success in the marketplace by adopting a holistic view of strategic orientations focusing on customer satisfaction, learning from the external environment and pursuing new market opportunities. Furthermore, an organization can gain a competitive advantage through innovation by creating products and services that are different from what is currently available in the market. To be successful, an organization must not only create innovative products and services but also market them effectively to consumers.

This study is the first to meta-analytically assess the explanatory value of two theorized models linking strategic orientations, innovation and firm performance. It also clarifies the role of innovation in the relationship between strategic orientations and firm performance.

Small and medium enterprises (SMEs) significantly contribute to economic growth, development, exports and employment of the nations. To maintain competitiveness in today's market, SMEs must explore and identify enablers to enhance their digital transformation process. This paper aims to shed light on some essential enablers SMEs can use to implement digital resilience successfully.

The quantitative assessment and validation of the enablers have been done using powerful and novel techniques, namely, the Delphi method, “fuzzy interpretive structural modelling” (F-ISM) method and “cross-impact matrix multiplication applied to classification (MICMAC)” analysis. The F-ISM model is developed using the information drawn from digital transformation experts and practitioners involved in the digital transformation process for SMEs. Furthermore, the F-ISM model provides four paths to complete the pathway to digital resilience.

The F-ISM and MICMAC analysis revealed four ways to enhance the digital transformation process in SMEs. These enterprises can utilise these path assessments to become digitally resilient in the present dynamic scenario. To enhance digital resilience among SMEs, the study identified ten enablers. Among these, “management competencies” was the most crucial, followed by “knowledge management” and “monitoring and controlling”.

The present study is limited in that the data used to develop the models were collected from a small group of industry experts whose opinions may not exhibit the comprehensive views of the population.

The findings can help SMEs enhance the digital transformation process by taking up different pathways to integrate the various enablers of digital resilience depending on resource availability.

The results indicate the most critical and influential enablers for enhancing digital resilience among SMEs. This research can be valuable to academicians, industry practitioners and researchers for guiding their future work.

This research aims to reveal the working principles of the decision mechanism that affects the use of neural implant acceptance and to discuss the leading role of digital literacy in this mechanism. In addition, it aimed to examine the theoretical connections of the research model with the conservation of resources (COR) and technology acceptance model (TAM) theories in the discussion.

The authors collected data from 300 individuals in an organization operating in the health sector and analyzed the data in the Smart Partial Least Squares (PLS) 3.3.3. This way, the authors determined the relationships between the variables, the path coefficients and the significance levels.

The study has found that strong digital literacy skills are linked to positive emotions and attitudes. Additionally, maintaining a positive mindset can improve one's understanding of ethics. Ethical attitudes and positive emotions can also increase the likelihood of adopting neural implants. Therefore, it is crucial to consider both technical and ethical concerns and emotions when deciding whether to use neural implants.

The research results determined the links between the cognitive, emotional and ethical factors in the cyborgization process of the employees and gave original insights to the managers and employees.

1. Determination of antecedents that affect individuals' acceptance of neural implant use.

2. Application to 300 individuals working in a health organization.

3. Path analysis using the least squares method via Smart PLS 3.3.3

4. Significant path coefficients among digital literacy, positive emotions, attitude, ethical understanding and acceptance of neural implant use.

Determination of antecedents that affect individuals' acceptance of neural implant use.

Application to 300 individuals working in a health organization.

Path analysis using the least squares method via Smart PLS 3.3.3

Significant path coefficients among digital literacy, positive emotions, attitude, ethical understanding and acceptance of neural implant use.

The purpose of this research was to explore the stickiness of players' motivation in a virtual community and to explore the important factors for gamers.

In this research, motivation was the independent variable; the virtual community was the mediator; and stickiness was the dependent variable. An online questionnaire survey was conducted, with users of augmented reality (AR) as the research objects. Statistical analysis was carried out using SPSS and AMOS software to verify the research model and research hypotheses, to understand the relation between player motivation and stickiness and to determine whether there were any changes in the virtual community.

The authors found that the relation between players' motivation in AR-based games and the virtual community had a significant positive impact. Ingress had a significant positive impact on the virtual community and stickiness, and Pokémon had a significant positive impact too. The virtual community of the Ingress game played a completely mediating role in motivation and stickiness, but the virtual community in Pokémon did not have a mediating effect.

The novel approach adopted in this study enabled us to determine the causal relation between player motivation, the virtual community and stickiness, on the basis of the theoretical framework formulated, and the latter was used to construct a path analysis model diagram. The correlation between motivation and the virtual community, between the virtual community and stickiness, and the causal relation between all three was verified. The study results and conclusions may help companies understand how to use virtual communities in AR games to improve stickiness and motivate gamers to continue playing.

The existing literature on knowledge-sharing (KS) behavior in the organizational context demonstrates that there is diversity, if not divergence, in understanding KS. Thus, this paper aims to integrate social cognitive theory and social exchange theory to construct a research model for determining the incentive for knowledge sharing among individuals in organizations based on past empirical results.

Accordingly, the methodology adopted in this study is the meta-analytic structural equation modeling based on the data gathered from 78 studies (80 samples, n = 29,318).

The most significant predictors of KSB were organizational support and social interaction ties, whereby KS intention and attitude were most optimally predicted by organizational commitment, knowledge self-efficacy, social interaction ties, organizational expectancy and reciprocal benefit. This study carried out a moderation analysis to look into potential causes of inconsistent results.

This meta-analysis shows the most influencing factors that trigger KSB in organizations. Moreover, this study clarifies the possible reasons for the inconsistent findings of the previous studies. Thus, it contributes to the KS literature.

There is still an ongoing debate on the value relevance of capital structure and its determinants. Recently the issue has been explored in family firms after being explored in mature firms. This paper investigates the role of institutional investors and the firm's innovation activity in influencing the firm's decision and ability to acquire debt capital.

A large sample of 700 privately-held family firms in Italy from 2010 to 2019. Two analysis techniques are used: panel analysis and path analysis. The value of debt and the debt ratio are used as leverage measures. The value of patent (as a proxy for innovation) and institutional investor are the explanatory variables.

The results show that institutional investors have no relationship with financial leverage measures except when controlling for an interaction variable (Institutional investors × Lombardy region). The patent value is positively correlated with debt; however, the ratio patent-to-asset is negatively related to financial leverage indicating higher risk exposure. The nonlinearity test demonstrates a turning point when the relationship between patent value and debt inverts.

Firms should monitor their innovation activity since excessive innovation increases risk exposure and affects financing opportunities and value. The involvement of institutional investors does not always enhance value.

Existing literature focuses separately on family firm innovations and financial leverage as outcome variables, emphasizing the role of institutional investors in both fields by adopting agency theory and socioemotional wealth framework. In this study, the authors go further by merging both relationships, investigating the dynamics of the institutional-family firm innovation relationship in influencing the firm's capital structure. The authors contribute to the ongoing debate by providing original findings on capital structure, governance and innovation, supported by rigorous methods to enhance family firms' decision-making.

This paper aims to help understand how adopting risk allocation criteria impacts the delivery of public–private partnership (PPP) mass housing in Nigeria with the view of promoting the adoption of PPP housing scheme in Nigeria.

The research design adopts the census sampling approach by using well-structured questionnaires distributed to stakeholders involved in PPP-procured mass housing projects, i.e. consultants, in-house professionals, contractors and the organized private sector, registered with PPP departments in the Federal Capital Territory Development Authority, Abuja, Nigeria. Sixty-three risk factors, nine risk allocation criteria and nine project delivery indices were submitted for the respondents to rank on a Likert scale of 7. Two hypotheses were formulated to test whether the risk allocation criteria impacted PPP mass housing delivery or otherwise. The study adopts partial least square-structural equation modeling to model the effect of risk on risk allocation criteria on project delivery indices and risk severity.

The finding shows that project risk allocation criteria have less effect on project delivery indices than on risk severity. The study concludes that risk allocation principles do not directly affect the delivery of PPP-procured mass housing projects. This is evident by the path coefficient of 0.724 values, which is not statistically significant at a 5% alpha protection value. The study concludes that allocating critical risk factors influences the performance of PPP-procured mass housing projects, as the path coefficient of 0.360 is also not significantly far from 0 and at a 5% alpha protection value.

The study is one of the recent studies conducted in PPP-procured mass housing projects in Nigeria owing to the novelty of procurement option in the sector. It highlights the risk factors that can jeopardize the PPP-procured mass housing project objectives. The study is of immense value to PPP actors in the sector by providing the necessary information required to formulate risk response methods to minimize the impact of the risk factors in PPP mass housing projects.

This study aims to evaluate the usage of partial least squares structural equation modeling (PLS-SEM) in journals related to logistics and supply chain management (LSCM).

Based on a structured literature review approach, the authors reviewed 401 articles in the field of LSCM applying PLS-SEM published in 15 major journals between 2014 and 2022. The analysis focused on reasons for using PLS-SEM, measurement model and structural model evaluation criteria, advanced analysis techniques and reporting practices.

LSCM researchers sometimes did not clarify the reasons for using PLS-SEM, such as sample size, complex models and non-normal distributions. Additionally, most articles exhibit limited use of measurement models and structural model evaluation techniques, leading to inappropriate use of assessment criteria. Furthermore, progress in the practical implementation of advanced analysis techniques is slow, and there is a need for improved transparency in reporting analysis algorithms.

This study contributes to the field of LSCM by providing clear criteria and steps for using PLS-SEM, enriching the understanding and advancement of research methodologies in this field.