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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 study aims to address the noted gap in comprehensive overviews detailing the developmental trajectory of Islamic finance (IF) as an interdisciplinary academic field.

The study introduces a unique approach using the combined methodologies of co-word analysis and main path analysis (MPA) by examining a broad collection of IF research articles.

The investigation identifies dominant themes and foundational works that have influenced the IF discipline. The data reveals prominent areas such as Shariah governance, financial resilience, ethical dimensions and customer-centric frameworks. The MPA offers detailed insights, narrating a journey from the foundational principles of IF to its current challenges and opportunities. This journey covers harmonizing religious beliefs with contemporary financial models, changes in regulatory landscapes and the continuous effort to align with broader socioeconomic aspirations. Emerging areas of interest include using new technologies in IF, standardizing global Islamic banking and assessing its socioeconomic effects on broader populations.

This study represents a pioneering effort to map out and deepen the understanding of the IF field, highlighting its dynamic evolution and suggesting potential avenues for future academic exploration.

This study explores the career trajectories of Chief Executive Officers (CEOs) to uncover unique characteristics that contribute to their success. By utilizing web scraping and machine learning techniques, over two thousand CEO profiles from LinkedIn are analyzed to understand patterns in their career paths. This study offers an alternative approach compared to the predominantly qualitative research methods employed in previous research.

This study proposes a framework for analyzing CEO career patterns. Job titles and company information are encoded using the Standard Occupational Classification (SOC) scheme. The study employs the Needleman-Wunsch optimal matching algorithm and an agglomerative approach to construct distance matrices and cluster CEO career paths.

This study gathered data on the career transition processes of graduates from several renowned public and private universities in the United States via LinkedIn. Employing machine learning techniques, the analysis revealed diverse career trajectories. The findings offer career guidance for individuals from various academic backgrounds aspiring to become CEOs.

The building of a career sequence that takes into account the number of years requires integers. Numbers that are not integers have been rounded up to facilitate the optimal matching process but this approach prevents a perfectly accurate representation of time worked.

This study makes an original contribution to the field of career pattern analysis by disclosing the distinct career path groups of CEOs using the rich LinkedIn online dataset. Note that our CEO profiles are not restricted in any industry or specific career paths followed to becoming CEOs. In light of the fact that individuals who hold CEO positions are usually perceived by society as successful, we are interested in finding the characteristics behind their success and whether either the title held or the company they remain at show patterns in making them who they are today.

As a matter of fact, nearly all CEOs had previous experience working for a non-Fortune organization before joining a Fortune company. Of those who have worked for Fortune firms, the number of CEOs with experience in Fortune 500 forms exceeded those with experience in Fortune 1,000 firms.

The purpose of this study is to investigate the influence of design for lean six sigma (DFLSS) on operational functions of the innovation management model by appraising an innovation management assessment framework.

An empirical approach for evaluating causal relationships among various constructs in the model phases that identify optimum pathways in achieving commercial success was adopted. A quantitative analysis of survey data were collected from large, medium and small organiations, including incubators in ANZ (Australia, New Zealand) and TMSV (Thailand, Malaysia, Sri Lanka and Vietnam).

The structural equation modelling recursive path analysis results of the model provide empirical evidence and pathways through the various constructs considered in the model. All these pathways lead to delivering optimum commercialization success (CS). Furthermore, DFLSS is confirmed as an enabler and has direct one-to-one and indirect influence on all the operational function constructs of the model including commercial success.

This study had a relatively small sample size of completed responses obtained from the population and a constrained ability to compare commercialization success (CS) between the two regions in the dataset. Future studies could be conducted on a global scale to increase responses.

The research findings enabled the development of important and practical guidelines for managers and innovation practitioners engaged in planning and management of innovation.

This research offers a holistic approach for integrating DFLSS with stage gate phases of innovation management assessment framework, supported by empirical evidence, to aid organizations in effectively managing the innovation process and achieving greater success in commercialization.

This paper aims to identify key factors influencing the development of advanced manufacturing clusters and propose governance pathways for their digital innovation ecosystems.

A quantitative analysis of the Tai-Xin Integrated Economic Zone in China is conducted using data collected through a questionnaire survey. An evaluation index for the development level of advanced manufacturing clusters is constructed, and a structural equation model is used to identify key influencing factors and governance pathways.

This paper reveals that factors such as industrial foundation, technological innovation capability, social institution environment and government policies have a significant positive impact on the development of digital innovation ecosystem in advanced manufacturing clusters. It constructs a governance model for the digital innovation ecosystem and proposes three major pathways: integration of heterogeneous innovation resources, enhancement of digital capabilities, and fostering digital collaborative governance. The crucial role of digital technology in improving data processing efficiency, optimizing resource allocation and promoting collaboration among entities is emphasized. These pathways can optimize resource allocation, boosting the competitiveness and innovation capacity of clusters.

By incorporating advanced manufacturing clusters into the digital innovation ecosystem framework, this paper enriches theoretical research on both fronts. It offers specific governance pathways and policy recommendations, providing valuable references and guidance for promoting the digital transformation and ecosystem construction of manufacturing clusters.

The aim of this study is to provide an empirical investigation of the agricultural cluster’s economic, social and environmental values. By doing so, the authors aim to offer policymakers and decision-makers a strategic approach that promotes competitiveness and economic development through shared value.

The authors used AMOS software and applied structural equation modeling to achieve the study’s objectives. The study used this approach with path analysis through the Analysis of Moment Structures software.

The empirical results indicate that creating shared value (CSV) can enhance the agricultural sector’s competitiveness through clustering. Rather than enhancing competitiveness directly, CSV plays a crucial role in improving the relationship between clustering and competitiveness. The authors also examined Porter’s diamond of competitiveness and evaluated factors for creating a shared value strategy, such as factor conditions, demand conditions, related and supporting industries, strategy, structure, rivalry and the role of government.

This study focuses solely on the agricultural cluster in Qalqilya governorate and cannot be applied to other regions without additional research.

Ensuring that stakeholders in the agricultural sector are kept informed about the activities of the cluster and the benefits of their participation is crucial. Empirical findings and conclusions have demonstrated that a shared value strategy can enhance the competitiveness of this sector. To achieve this, institutions involved in developing the agricultural cluster must increase their efficiency and capacity. Consulting experts in this field and drawing on experiences from other countries can aid in achieving this goal. Additionally, enhancing farmers’ productivity should be a priority, and the Ministry of Agriculture can provide training and workshops to improve their skills and expertise.

This study suggests that Palestinian policymakers should establish effective partnerships between the government and the agricultural sector’s firms in Qalqilya to reinforce the cluster’s competitiveness. This strategy can stimulate competitiveness and promote economic and social development in Palestine.

Tourism is an essential industry in Indonesia, and understanding its inter-sectoral and inter-regional connections is critical for policy development. This study examines the economic impact of regional tourism in Indonesia and the connections between different tourism-related regions and industries.

This study uses a non-survey method to estimate the inter-regional input-output table (IRIOT) in 2019, backward and forward linkage to identify the role of tourism in the economy, and the structural path analysis (SPA) to identify the inter-sectoral and inter-regional flow of tourism effect. The benchmark IRIOT 2016 published by Badan Pusat Statistik (BPS) serves as the primary data source.

The findings indicate that tourism has a relatively high impact on the overall national economy and plays an essential role in nine provinces. However, this study uses four provinces to represent Indonesian tourism: Jakarta, Jawa Timur, Bali, and Kepulauan Riau. The SPA result captures that Kepulauan Riau Province has the highest tourism multiplier effect and Jawa Timur has the highest coverage value. Moreover, the manufacturing sector receives the most benefit from the tourism effect, followed by trade, construction, agriculture, transportation, and electricity-gas. From a spatial perspective, tourism connections are not solely based on geographical proximity. Instead, they are established through an intricate supply chain network of manufactured goods. This emphasizes the significance of considering supply chain dynamics when investigating inter-regional relationships in the tourism sector.

This research contributes to the literature by estimating the IRIOT in 2019, disaggregating tourism activities from related economic sectors, constructing tourism-extended IRIOT, and identifying the critical path of tourism effect in numerous provinces with different economic structures. This novel approach offers valuable insights into the full spectrum of tourism’s economic impact, which has not been previously explored in this depth. This study is useful for policymaking, investment insight, and disaster mitigation.

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.

Following counterarguments from previous studies, overwhelming evidence from meta-analyses shows that supply-chain integration (SCI) is not universally associated with improved performance. This study aims to clarify all the mechanisms purported to reach the combined effects of SCI dimensions.

In this study, the authors first conducted a literature review and identified the effects of different individual SCI dimensions and their interactions and mediation, or arcs of integration, as reported by past studies. Data from a survey of 348 firms were analyzed using multiple regressions, structural equation modeling and latent class analysis.

The results showed that the positive independent associations between SCI dimensions and operational performance change as they interact. Larger combinational effects were found in a second-order SCI construct and a mediation model. Smaller effects and fewer significant paths (and even negative signs) were also found. This shows that more SCI is not always better. Finally, configurational effects varied: the latent class analysis showed that a more inward (internal integration) gestalt performed better for some firms.

Some variables that could affect operations performance were missing from the models, and this paper did not specify the directions of relationships among the five performance dimensions. The authors are aware of cumulative models, but they are not within the scope of this paper. In addition, this study did not consider other sociocultural factors that may influence different dimensions of SCI.

This study shows SCI dimensions play different roles. Therefore, it is needed to theorize the distinct roles of internal and external integration and how they somehow compensate or substitute for each other. The results suggest that future studies need to reexamine the universal predictions especially from resource-based, resource-advantage and relational views. SCIs are not inimitable and rare resources for all firms. Furthermore, implementing SCI does not guarantee resource and relational advantages for all firms.

This paper aims to discuss recent criticism related to partial least squares structural equation modeling (PLS-SEM).

Using a combination of literature reviews, empirical examples, and simulation evidence, this research demonstrates that critical accounts of PLS-SEM paint an overly negative picture of PLS-SEM’s capabilities.

Criticisms of PLS-SEM often generalize from boundary conditions with little practical relevance to the method’s general performance, and disregard the metrics and analyses (e.g., Type I error assessment) that are important when assessing the method’s efficacy.

We believe the alleged “fallacies” and “untold facts” have already been addressed in prior research and that the discussion should shift toward constructive avenues by exploring future research areas that are relevant to PLS-SEM applications.

All statistical methods, including PLS-SEM, have strengths and weaknesses. Researchers need to consider established guidelines and recent advancements when using the method, especially given the fast pace of developments in the field.

This research addresses criticisms of PLS-SEM and offers researchers, reviewers, and journal editors a more constructive view of its capabilities.