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The purpose of this study is to examine how construction project managers’ conflict management styles (CMSs) affect project team resilience from the perspective of social identity theory.

This study adopted a cross-sectional survey design and collected paired data from 110 construction project managers and 474 employees in China. Based on the data collected, the authors tested the proposed hypotheses using hierarchical regression analysis.

The results show that a project manager’s cooperative CMS positively affects team resilience, and a project manager’s competitive and avoidant CMS negatively affects team resilience. Team followership plays a mediating role in this relationship. The team power distance moderates the effects of a project manager’s cooperative and avoidant CMSs on team followership.

This paper enriches the existing literature on conflict management in construction projects and have potential guiding significance and application value for team resilience management practices.

Developing appropriate conflict management strategies (CMSs) is important for a firm to achieve better relationships with its supply chain partners. However, the literature has rarely considered how firms may simultaneously adopt various CMSs to address interfirm conflicts. Accordingly, this study aims to identify manufacturers' CMS archetypes with their main suppliers based on interfirm interdependence structure, further examining the relationship between various CMS archetypes and exchange performance.

First, the authors theoretically propose a manufacturer's CMS archetypes configured with Rahim's five CMSs based on interfirm interdependence structure. Second, cluster analysis with data from 200 Chinese manufacturers is used to generate a manufacturer's actual CMS archetypes. Third, the authors analyze the relationship between interfirm interdependence structure and Rahim's five CMSs as well as that with the new configured CMS archetypes. Finally, the authors use Analysis of Variance (ANOVA) to compare exchange performance disparities among manufacturers using different CMS archetypes.

First, cluster analysis results show that three archetypes – cooperative, competitive and cooperative-competitive CMS – emerge during interfirm conflict management. Second, regression analysis shows how interfirm interdependence structure can affect manufacturers' choice of different CMSs and CMS archetypes. Third, ANOVA results indicate that when addressing interfirm conflicts, the strong cooperative, strong competitive and strong cooperative-competitive CMS archetypes can help manufacturers get good relationship satisfaction with main suppliers. Regarding supplier opportunism, while both strong cooperative and strong competitive CMS archetypes are effective at restraining opportunism, the cooperative-competitive CMS archetype may trigger higher levels of supplier opportunism.

This study enriches the interfirm relationship management literature and provides insights for manufacturers to better address interfirm conflicts.

Dominant quality standards are present in all industries. Implicit in their use is the assumption that once adopted, there is little or no reason to replace them. However, there is evidence that, under certain circumstances, such standards do get replaced. The reasons for this action are not well-understood, either as they pertain to the displacement decision or to the selection and adoption of the alternative standard. The purpose of this study is to identify and explore these two issues (displacement and replacement) by drawing on data from the American healthcare system. This study is viewed through the theoretical lens of legitimacy theory. In addition, the process is viewed from a temporal perspective. The resulting findings are used to better understand how this displacement process takes place and to identify directions for interesting and meaningful future research.

This is an explanatory study that draws on data gathered from quality managers in 89 hospitals that had adopted a new healthcare quality standard (of these, some fifty percent had displaced the dominant quality standard – the Joint Commission – with a different standard – DNV Healthcare.

The combined literature review and case study data provide insights into the displacement process. This is a process that evolves over time. Initially, the process is driven by the need to meet customer demands. However, over time, as the organizations try to integrate the guidelines contained within the standards into the organization, gaps in the quality standard emerge. It is these gaps that motivate the need to displace standards. The legitimacy perspective is highly effective at explaining this displacement process. In addition, the study uncovers some critical issues, namely the important role played by the individual auditors in the certification process and the importance of fit between the standard and the context in which it is deployed.

The data for the propositions in this case study were derived from interviews and survey data from 89 healthcare organizations. It would be interesting to examine similar relationships with other quality standards and industries.

Our findings provide new insights related to motivations to decouple from a dominant quality standard. Results provide a cautionary tale for standards that hold a dominant market share such that perceived legitimacy of such standards is not as stable as originally thought.

This study illuminates the fragile nature of the stability of dominant standards and emphasizes the linkages between legitimacy concerns and divestiture of such standards.

Emotional intelligence (EI) and conflict management (CM) are essential skills for construction managers towards achieving organisational effectiveness. It is believed that an individual’s EI level (EIL) is a predictor of the preferred CM styles (CMS). This study aims to explore the relationships between EIL, preferred CMS and demographic factors in the construction sector of the Dominican Republic (DR).

The EIL and CMS of a sample of civil engineers in managerial positions were evaluated using the emotional intelligence appraisal and Rahim Organizational Conflict Inventory-II self-assessment tools.

There was a strong positive correlation between the rated EIL and the scores of collaboration and compromise styles, i.e. participants with higher EIL have stronger fit within the collaboration and compromise styles of managing conflicts. For participants with lower EIL, collaboration and compromise styles were also top preferences, but with no statistical significance. Significant relationships existed between gender, collaboration and compromise styles and between work experience and collaboration style. No significant relationship between demographics and EIL.

The construction industry needs innovative construction managers whose CMS and EIL are compatible with the culture and overall organisational objectives.

To the best of the authors’ knowledge, this is the first study appraising the EI and CMS of civil engineers working in DR construction industry.

The purpose of this article is to conduct a bibliometric analysis of the cellular manufacturing system (CMS) literature published from 1982–2021 to identify key issues and trends for the future.

A six-stage methodology is used to conduct a literature review, which includes: (1) article collection; (2) inclusion/exclusion criteria; (3) reviewing the articles; (4) analyzing the articles; (5) framework development; and (6) future research directions. A total of 936 CMS-specific articles are reviewed. This paper made use of three software tools: the R package, VOSviewer and SciMAT.

According to the findings, the majority of CM researchers focused on cell formation and design. The USA, Iran and India are the top three leading publishers. Additionally, the gap and future direction of CM are discussed.

To the best of the authors' knowledge, the current study is the first attempt to investigate CMS evaluation through bibliometric and thematic analysis and provides a decisional framework as well as steps for CMS adoption. For individuals who are interested in understanding more about CMS and its evolution, this paper offers a starting point.

This study addresses the production optimization of a cellular manufacturing system (CMS) in magnetic production enterprises. Magnetic products and raw materials are more critical to transport than general products because the attraction or repulsion between magnetic poles can easily cause traffic jams. This study needs to address a method to promote the scheduling efficiency of the problem.

To address this problem, this study formulated a mixed-integer linear programming (MILP) model to describe the problem and proposed an auction and negotiation-based approach with a local search to solve it. Auction- and negotiation-based approaches can obtain feasible and high-quality solutions. A local search operator was proposed to optimize the feasible solutions using an improved conjunctive graph model.

Verification tests were performed on a series of numerical examples. The results demonstrated that the proposed auction and negotiation-based approach with a local search operator is better than existing solution methods for the problem identified. Statistical analysis of the experiment results using the Statistical Package for the Social Sciences (SPSS) software demonstrated that the proposed approach is efficient, stable and suitable for solving large-scale numerical instances.

An improved auction and negotiation-based approach was proposed; The conjunctive graph model was also improved to describe the problem of CMS with traffic jam constraint and build the local search operator; The authors’ proposed approach can get better solution than the existing algorithms by testing benchmark instances and real-world instances from enterprises.

The purpose of this paper is to investigate how three core elements of critical management studies (CMSs), “de-naturalisation”, “reflexivity” and “(non)-performative intent”, can help expand the current debate in the supply chain management (SCM) field.

The authors used a systematic literature review to select 103 articles published in 12 high-ranking journals in the SCM field based on the Academic Journal Guide of the Chartered Association of Business Schools.

The findings of this study suggest that SCM studies can be narrowed down into four major CMSs themes: “power”, “ethics and environmental issues”, “diversity” and “working conditions”, but even these themes are still under-discussed and undertheorized in SCM. The literature the authors reviewed is more concerned with explaining these phenomena than questioning them and proposing new agendas. This paper, therefore, will discuss how these three core elements of CMS can help transform the “hidden” issues of SCM, which it will do by illustrating it in the context of buyer–supplier relationships and lean manufacturing.

This research will encourage SCM scholars who are interested in conducting more critical studies and teaching the harmful effects of global supply chains.

This paper highlights that a combination of SCM and CMS approaches is important when we decide to adopt a more critical “constructive” view of supply chain challenges and engage practical and critical views, respectively, to generate knowledge that not only increases (corporate) performance but also highlights social needs and values.

The purpose of this study was to perform sentiment analysis and analyze the growth and popularity of Drupal, Joomla and WordPress on YouTube over a four-year period. This included identifying the most liked and commented videos for each content management system (CMS), ranking the CMSs based on the number of positive comments they received, and using natural language processing techniques to identify the top ten most frequently appearing words in videos about the CMSs.

The data for assessing the features of the videos of Drupal, WordPress and Joomla was extracted using Webometric Analyst version 4.4. with the help of the YouTube application programming interface key for videos on the selected CMSs uploaded from 2019 to 2022. The extraction of comments and sentiment analysis for the relevant videos was done using Mozdeh.

This study scrutinized 371, 234 and 313 videos of WordPress, Joomla and Drupal on YouTube. The findings reveal that there is a chronological growth of videos of the three CMSs in four years and till the present time, WordPress has the highest number of videos followed by Drupal and then Joomla. Regarding the ranking of highly liked videos, WordPress again wins the list with the highest number of likes in its videos followed by Drupal and then Joomla. For analyzing sentiments of the total comments extracted 123,409 for WordPress, 1,790 for Joomla and 1,783 for Drupal, respectively, WordPress receives the highest average positive comments followed by Drupal then Joomla. In top word frequency, the word “thank” highly occurs and viewers are asking for more tutorial videos.

To the best of the authors’ knowledge, this study is the first attempt for analyzing the sentiments of WordPress, Drupal and Joomla using Mozdeh software within the concerning period.

User feedback inferred from the user's search-time behavior could improve the learning to rank (L2R) algorithms. Click models (CMs) present probabilistic frameworks for describing and predicting the user's clicks during search sessions. Most of these CMs are based on common assumptions such as Attractiveness, Examination and User Satisfaction. CMs usually consider the Attractiveness and Examination as pre- and post-estimators of the actual relevance. They also assume that User Satisfaction is a function of the actual relevance. This paper extends the authors' previous work by building a reinforcement learning (RL) model to predict the relevance. The Attractiveness, Examination and User Satisfaction are estimated using a limited number of the features of the utilized benchmark data set and then they are incorporated in the construction of an RL agent. The proposed RL model learns to predict the relevance label of documents with respect to a given query more effectively than the baseline RL models for those data sets.

In this paper, User Satisfaction is used as an indication of the relevance level of a query to a document. User Satisfaction itself is estimated through Attractiveness and Examination, and in turn, Attractiveness and Examination are calculated by the random forest algorithm. In this process, only a small subset of top information retrieval (IR) features are used, which are selected based on their mean average precision and normalized discounted cumulative gain values. Based on the authors' observations, the multiplication of the Attractiveness and Examination values of a given query–document pair closely approximates the User Satisfaction and hence the relevance level. Besides, an RL model is designed in such a way that the current state of the RL agent is determined by discretization of the estimated Attractiveness and Examination values. In this way, each query–document pair would be mapped into a specific state based on its Attractiveness and Examination values. Then, based on the reward function, the RL agent would try to choose an action (relevance label) which maximizes the received reward in its current state. Using temporal difference (TD) learning algorithms, such as Q-learning and SARSA, the learning agent gradually learns to identify an appropriate relevance label in each state. The reward that is used in the RL agent is proportional to the difference between the User Satisfaction and the selected action.

Experimental results on MSLR-WEB10K and WCL2R benchmark data sets demonstrate that the proposed algorithm, named as SeaRank, outperforms baseline algorithms. Improvement is more noticeable in top-ranked results, which usually receive more attention from users.

This research provides a mapping from IR features to the CM features and thereafter utilizes these newly generated features to build an RL model. This RL model is proposed with the definition of the states, actions and reward function. By applying TD learning algorithms, such as the Q-learning and SARSA, within several learning episodes, the RL agent would be able to learn how to choose the most appropriate relevance label for a given pair of query–document.

This study aims to investigate whether the contract manufacturer (CM) should take the first-mover advantage in the end-product without supplying core components to the original equipment manufacturer (OEM) immediately, or should fully squeeze the benefit of the learning effect through an amplified production quantity by letting the OEM enter the end-product market early.

The authors propose a two-period model for a supply chain consisting of a CM and an OEM where the CM has four alternative entry strategies concerning it competition to the OEM in the end-product market. For each strategy, the authors derive the equilibrium solutions of the two firms using a backward approach. Comparison leads to the CM’s final choices among the four strategies.

For both CM and OEM, the monopoly and the first-entry strategies will be dominated by either the post-entry or the simultaneous-entry strategy, and thus, their preferred strategy is chosen from the latter two. Regarding the two firms choices between the post- and simultaneous-entry strategy, the CM prefers the post-entry strategy when the OEMs brand premium is at a moderate level, whereas the OEM prefers the post-entry strategy when its brand premium is low, and the learning effect can amplify the interval for the CMs adopting the post-entry strategy as well as changes the interval for the OEMs preference related to the two strategies.

This paper is the first one to explore the optimal strategy for a CM to maximize its profit in a co-opetitive supply chain situation with a CM and an OEM. The authors believe that our paper contributes to both literature and the market.