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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.

Employee engagement (EE) continues to be one of the most difficult challenges for organizations today. Numerous factors have been linked to EE, according to studies. However, the necessary human resource management (HRM) strategies and systems for enhancing EE have not yet been developed. It is questionable if all employees inside the company require the same HRM strategies, to boost engagement as one size does not fit all. Therefore, it is necessary to create employee profiles based on factors associated with EE. This study aims to develop employee profiles based on engagement dimensions and outcomes. It seeks to comprehend the relationship between engagement level and factors such as age, years of service and employment grade.

Using latent profile analysis (LPA), we identified five EE profiles (highly engaged, engaged, moderately engaged, disengaged and highly disengaged). These five profiles were characterized by five EE dimensions (Culture Dimensions, Leadership Dimensions, People Process, Business alignment Dimension and Job Dimension) and EE outcomes (Say, Stay and Strive).

The study revealed that Engaged profiles exhibited low stay outcomes. The highest percentage of disengaged employees fall under 25 years of age with less than 5 years of experience and are at the entry level.

The study highlights the significance of the people processes dimensions in enhancing engagement. Profiles with low people process dimensions showed high disengagement. Person-centered LPA adds and complements variable-centered approach to develop a better understanding of EE and help organizations devise more personalized strategies. The study would be of interest to both academics and practitioners.

The novelty of this study lies in its attempt to model the employee profiles to comprehend the relationship between engagement levels using LPA.

Local food impacts tourist satisfaction and drives the choice of a tourist destination. However, it is not clear which attributes of local food products drive tourists’ preferences. More specifically, little is known about potential segments in the tourist market. Acknowledging consumers’ divergent inclinations, we explore tourist preferences toward local food attributes through a market segmentation approach.

This study uses the Best-Worst Scaling method to examine the preferences of 311 tourists for attributes of local food products that are known to affect their choices. We employ a Latent Class Analysis to identify market segments with distinct preferences.

Results indicate that tourists prioritize taste, quality, authenticity, healthiness, connection to local culture and tradition and environmental friendliness over price, appearance, enhancement of local economy, availability and packaging. We further identify three segments: Sensory Seekers, Cultural Experiencers, and Price Conscious. The largest segment, Sensory Seekers, is driven by taste and quality attributes, while Cultural Experiencers prioritize connection to local culture and authenticity. The Price Conscious segment places a greater emphasis on price than the other segments. Cultural Experiencers demonstrate the highest willingness to pay for local food products.

The positioning of local products should target Cultural Experiencers. Agri-firms and state agencies promoting a tourist destination internationally can also use the connection to local culture and authenticity in their marketing campaigns.

Our research contributes to food marketing literature by showing which local food attributes drive tourist preferences. Moreover, we uncover unobservable heterogeneous preferences among tourists.

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.

This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.

The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.

The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

Total risk score (TRS) is considered one of the main indicators for risk evaluation. Several studies attempted to employ different types of risk indices for the evaluation of cost overrun causes. Hence, this study aims at performing a comparative analysis to evaluate the efficiency of three different approaches for TRS calculation.

Thirty-eight unique causes of cost overrun in urban-related construction projects were identified and a survey was conducted among construction professionals in Iran. The TRS for each cost overrun cause is calculated using single-attribute (SA), double-attribute (DA), and multiple-attribute (MA) approaches, and eventually, causes were ranked. Furthermore, principal component analysis (PCA), logistic regression analysis (LRA), and K-means clustering are utilized to compare the differences in the generated TRS using different approaches.

The results revealed that the TRS generated through the MA approach demonstrated the highest efficiency in terms of generating correlation between causes and their identified latent constructs, prediction capability, and classification of the influential causes in the same group.

The originality of this study primarily stems from the adoption of statistical approaches in the evaluation of the recently introduced TRS calculation approach in comparison to traditional ones. Additionally, this study proposed a modified application of the relative importance index (RII) for risk prioritization. The results from this study are expected to fulfill the gap in previous literature toward exploring the most efficient TRS calculation approach for those researchers and practitioners who seek to utilize them as a measure to identify the influential cost overrun causes.

Learning organization (LO) concept has received much attention in the last decades. The purpose of an LO is to proactively shape its future by fostering a culture of continuous learning among its members. This approach empowers the organization to adapt, evolve and innovate, aligning with the needs and aspirations of both internal and external stakeholders. As a result, the assessment of an organization’s LO level, whether strong or weak, becomes a matter of significance. This study aims to use Senge’s LO Questionnaire (SLOQ) in conjunction with latent profile analysis (LPA) to identify latent classes within the organization. This approach seeks to enhance the organization‘s learning potential and facilitate its evolution.

This research was a cross-sectional study conducted at Hamadan University of Medical Sciences, Iran. The statistical population consisted of 451 managers and employees across seven departments based at the university headquarters. The sample included 295 individuals, and data was collected using the SLOQ.

The LPA has shown an excellent accuracy (97%) in identifying SLOQ cut-off points (three classes of good, moderate and low). Most of the participants are considered as a moderate LO (55.9%), which implies the possibility of improving learning, especially enhancing “common vision” and “systemic thinking.” Also, individuals belonging to the high-class category were more likely to be male and hold associate degree than individuals in other categories.

This study depends on self-report, and different perceptions of questions can cause percept bias. Another limitation is about the nature of this research, which is cross-sectional, which may bring back causality among variables. The other is only three demographic variables have been considered, and it is necessary to pay attention to other influential variables in future studies.

The originality of this study lies in its use of the SLOQ in combination with LPA to identify latent classes in an organization to improve learning potentials.

This exploratory research aims to identify the effect of perceived stress level and self-efficacy on management quality and what practices and theories need to be enhanced to improve management quality under volatility business environments.

The study surveyed 291 working women, using the Perceived Stress Scale and the General Self-Efficacy Scale. Latent class analysis (LCA) for classifications of respondents, using categorical observed variables and MANCOVA, are applied to determine the relationship between stress and self-efficacy on the assigned classes.

The study suggests that in a highly volatile business environment, where stress is high, affecting management quality, managers as individuals fall into one of four classes that describe their techniques of coping with the stress, namely Uncommitted Experimenters, Try Anything, Intrinsically Motivated and Externally Motivated. Techniques of stress management classification are significantly related to the combined perceived stress and self-efficacy measures, with Externally Motivated respondents as the classification with a significant mean difference.

The main limitation of the study at hand refers to the sample size versus the number of potential factors of stress. This limitation highlights the need for further data gathering and research in this area, as stress is a critical factor of performance and often ignored in traditional management theories. Another limitation of this study is the lack of in-depth analysis of the use of meditation; its benefits and how to best use this practice in traditional work settings.

The outcome of the study could have significant implications for quality of management in business, private and social sectors by providing meditation as a tool for employees and stakeholders to handle stress in conflict zones.

Using stress management techniques might prove to be a low-cost tool for better quality management of human assets.

The authors study focuses on women in volatile economic turmoil, natural devastations, conflict areas and politically insecure environments. This socioeconomic segment was rarely scrutinized despite its direct effect on a large number of economies hosting a sizeable portion of the world’s population. Interesting potential results highlight the relationship between the respondents in the Intrinsically Motivated class and stress reduction for the benefit of management quality.

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 purpose of this research is to identify the most important attributes of metaverse influencers and examine their impact on customer engagement and social glue.

Three studies (one qualitative and two quantitative) were conducted to understand the phenomenon better. The qualitative study (Study 1) was conducted to identify the antecedents of the theoretical model, which was tested in Study 2 using the covariance-based structural equation modelling (CB-SEM) technique. Study 3 then divided the respondents based on the metaverse influencer attribute preferences.

Results of Study 1 revealed the six most influential attributes of metaverse influencers: physical attractiveness, social attractiveness, perceived credibility, metaverse-influencer fit, intimacy and attitude homophily. Further, Study 2 validated that attractiveness and perceived credibility enhance engagement. Also, the results revealed that intimacy, perceived credibility and homophily enhance social glue. Moreover, parasocial relationships mediate the association between intimacy, attitude homophily, perceived credibility and (engagement and social glue). The conditional indirect effect of physical attractiveness, social attractiveness and metaverse–influencer fit on (engagement and social glue) via parasocial relationships at different high and low levels of self-discrepancy was significant. Finally, Study 3 used latent class analysis to reveal different clusters of metaverse users.

This research enriches our understanding of metaverse influencers, contributing to the influencer marketing literature. It offers actionable insights for marketers by elucidating key influencer attributes, aiding in enhancing engagement and social glue.

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.