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The purpose of this paper is to explore the doer effect of service failure (SF), good prior experience (GPE) and recovery on overall customer satisfaction and repurchase intentions for multi-agents in tourism service supply chain (TSSC). It specifically focuses on internal and external failure and recovery.

The study employs a 2×2×3 between-subjects experimental design with 12 diverse scenarios. It aims to examine the main effects of GPE and the interaction effects of SF and recovery on overall customer satisfaction and repurchase intentions.

The main findings show that consumers do not show favorable behavioral outcomes when they have GPE with an affiliated party. Results of the experiments demonstrate that for hotels, there is no interaction effect between failure and recovery regarding overall customer satisfaction and repurchase intentions; however, for travel agencies, an interaction effect has been found. This indicates that an internal failure (by travel agency) should be recovered internally to increase the behavioral outcomes for travel agency. However, if there is an external failure (by hotel) then the essential thing is providing a recovery.

Although the service literature covers failure and recovery in diverse contexts, these concepts are rarely studied from a multi-agent perspective in the service supply chain literature. In such a chain, a failure by a different party may remain unresolved, and this may create a positive effect on another party, if they provide recovery for the failure. This means that the doer of the failure and/or recovery (the party responsible from the failure and/or recovery) may have an impact on behavioral outcomes. However, previous literature has neglected to focus on the important issue of which entity/party performs the failure and/or recovery, and the effect on behavioral outcomes. By focusing on a principal-agent relationship in a TSSC, the study aims to address this research gap.

This paper aims to examine how organizational practices such as climate for conflict management (CCM) and high involvement work practices (HIWPs) reduce the negative consequences of workplace bullying (WPB) on work-related depression (WRD).

Data were collected from 468 full-time employees working in the financial sector in Türkiye by applying a student-recruited sampling strategy. The aforesaid relationships were assessed using SPSS AMOS 29.

The results indicated that WPB leads to WRD; however, this effect is alleviated by employee perceptions of strong CCM and the administration of HIWPs in workplace settings.

Collecting data from a single source poses the risks of self-report data bias; however, in the future, data may be collected from multiple sources to lessen this potential threat. The study was a cross-sectional study, which makes it hard to make casual inferences; longitudinal data would be more beneficial to establish casual associations.

Business owners and managers can draw from the study results to create a work environment perceived by employees to be fair when dealing with conflicts and the negative vices of bullying in workplaces. Also, organizations may administer practices that empower employees’ confidence and competence to deal with negative persecution in organizations.

Few studies, if any, have focused on examining the moderating effect of CCM and HIWPs in the association between WPB and WRD. Drawing upon conservation of resources theory, the study stands out as it tests the moderating effect of CCM and HIWPs in the connection between WPB and WRD. The findings contribute to the few available studies tackling organizational factors relevant to alleviating the negative consequences of WPB in organizations.

This paper aims to use text mining methods to explore the similarities and differences between countries’ national digital transformation (DT) and Industry 4.0 (I4.0) policies. The study examines the applicability of text mining as an alternative for comprehensive clustering of national I4.0 and DT strategies, encouraging policy researchers toward data science that can offer rapid policy analysis and benchmarking.

With an exploratory research approach, topic modeling, principal component analysis and unsupervised machine learning algorithms (k-means and hierarchical clustering) are used for clustering national I4.0 and DT strategies. This paper uses a corpus of policy documents and related scientific publications from several countries and integrate their science and technology performance. The paper also presents the positioning of Türkiye’s I4.0 and DT national policy as a case from a developing country context.

Text mining provides meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, aligned with their geographic, economic and political circumstances. Findings also shed light on the DT strategic landscape and the key themes spanning various policy dimensions. Drawing from the Turkish case, political options are discussed in the context of developing (follower) countries’ I4.0 and DT.

The paper reveals meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, reflecting political proximities aligned with their geographic, economic and political circumstances. This can help policymakers to comparatively understand national DT and I4.0 policies and use this knowledge to reflect collaborative and competitive measures to their policies.

This paper provides a unique combined methodology for text mining-based policy analysis in the DT context, which has not been adopted. In an era where computational social science and machine learning have gained importance and adaptability to political and social science fields, and in the technology and innovation management discipline, clustering applications showed similar and different policy patterns in a timely and unbiased manner.

There is a large number of perishable foodstuffs produced, stored, distributed and delivered daily around the world. Almost all except for root vegetables are sensitive products to temperature. Thus, adopting uninterrupted and appropriate logistics activities with predetermined range of temperature from production site until end-user is critical for ensuring required quality and safety. If a mistake is made during either transport or storage, it not only becomes risky for human health, but also generates huge food waste for the environment and negative economic impact for food providers. Therefore, this study aims to identify all potential factors affecting the cold chain performance in the food industry and to design a framework that includes these factors. This framework is also a roadmap for managers, food providers and logistics parties for sustainable cold chain management.

Considering, tangible and intangible potential criteria, the ultimate goal of this study is to identify potential criteria affecting cold food chain performance and propose a conceptual framework including 12 main criteria. Next, the importance order of each criterion and the causal relationships between them are determined. In this study, this relationship among criteria is analyzed by using fuzzy Decision-making Trial and Evaluation Laboratory (DEMATEL) approach because of its ability to solve complex problems by ensuring causal relationship among factors, additionally to determine importance order. Finally, suggestions for administrative implications are presented.

Fuzzy DEMATEL was used to explain the causal link and importance order among identified drivers. The analysis shows that five criteria (C1, C3, C8, C9 and C12) belong to cause (influential) groups and remaining seven criteria belong to effect (influenced) groups. The highest influential criterion is staff (C8) and is followed by technical issues (C9) as the second most influential factor. Additionally, top three most important factors are traceability (C7), staff (C8) and cold transportation (C5). According to the numerical results of fuzzy DEMATEL implementations, suggestions for managerial implementations are presented.

The main contribution of the study is to propose meaningful suggestions for managerial implications about sustainable cold chain in food industry for businesses and to examine causal relations between criteria and to rank criteria in descending importance order.

To the best of the authors’ knowledge, this is the first study that focuses on determining the potential criteria affecting cold supply chain performance both theoretically and empirically in the sustainability environment. What are the enablers that affect the cold food supply chain stages is the research question of this study.