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This study aims to investigate the effect of servant leadership on employee organizational identification and career satisfaction through the mediating lens of leader-member-exchange (LMX). Furthermore, this study also examines whether perceived organizational support (POS) strengthens the positive effect of servant leadership on LMX and subsequently, on employee organizational identification and career satisfaction.

Survey data were collected from 314 respondents working in hotels in United States of America (USA). Structural equation modeling (SEM), hierarchical moderation analysis and bootstrapping were used to test the study hypotheses.

Servant leadership was found to positively influence employee organizational identification and career satisfaction. Further, analysis revealed that LMX partially mediated the positive relationship between servant leadership and employee career satisfaction and fully mediated the positive relationship between servant leadership and organizational identification. However, although POS moderated the indirect relationship between servant leadership and employee’ career satisfaction, it did not moderate the indirect relationships between servant leadership and organizational identification.

This study provides insight into the nexus of servant leadership and organizational support in hospitality industry to foster the employee organizational identification and career satisfaction which are extremely needed for competitive advantage in hotel industry.

This study addresses recent calls for future researchers to investigate the important of servant leadership in the hospitality industry.

The purpose of this paper is to provide a systematic review of literature on corporate engagement with start-ups (CEWS) by identifying the modes, contexts, antecedents, barriers and outcomes. As an emerging field, CEWS presently has no such review available which will help in building consensus within the field and shape future research directions.

The study followed a two-phased systematic review of literature. Three research databases (i.e. Web of Science, ScienceDirect and SCOPUS) were accessed to gather and conduct the review. Of the total 379 papers retrieved, 63 total relevant papers were studied and analysed. The exhaustive review of literature helped to uncover the contexts, perspectives, antecedents, outcomes and barriers reported across the different modes of CEWS.

The study highlighted the five prominent modes of CEWS favoured by large corporations and start-ups. It found that the large corporations and start-ups associate with one another on the basis of complementarities of activities, resources and motives to pursue their strategic orientations. The engagements also face barriers on the ground, such as incompatibility of goals, power imbalances, cultural differences and weak engagement plans. Most important contexts seen were the high-technology industries in the developed economies like the USA and Europe. It also found that ecosystem creation, accessing innovation and corporate strategy have been preferred as the most productive modes of CEWS in the literature.

This review provides practitioners with a detailed list of the modes and drivers of CEWS. Subsequently, the barriers that need to be managed to successfully execute a specific mode of engagement. This shall enable the practitioners in developing and adopting the best practices while engaging with the start-ups to better facilitate the outcomes of CEWS.

To the best of the authors’ knowledge, there is no systematic literature review available in the domain of CEWS – thus, this study makes an important methodological contribution to the field. By consolidating the fragmented yet growing knowledge on CEWS, the study presents a detailed understanding of what drives and obstructs the engagement between large corporations and start-ups.

In developing countries like Tanzania, gems and jewellery industry mainly consists of disintegrated and unstable micro and small workshops which operate in a way that misalign value addition processes. This study is aimed to bridge gap by focussing on exploitation of industrial clusters in social normalisation and economic resilience to developing countries. The world economic shocks has been not only individually experienced but also globally shared while disrupted lives across all countries and communities and negatively affected global socio-economic growth.

Furthermore, the explorative design was adopted in this study in order to explore needs of respondents, and with the aim to direct the study towards a descriptive design. The sample frame consists of participants in gems and jewellery activities in Tanzania whereby sample was drawn from Dar es Salaam and Arusha. Semi-structured interview was used to collect quantitative data to establish evidence of Tanzanians’ SSJs linked to global value chains (GVCs).

Results revealed the benefits of exploitation of artisanal industrial clusters to Tanzanians’ SSJs when linked to global value chains (GVCs). Findings of the study demonstrate the importance of artisanal industrial clusters in facilitating Tanzanians’ SSJs to access GVCs. Further, insufficient education, trust and social protection directly affects inclusive GVCs, inferring that the impact of artisanal industrial clusters on inclusive GVCs in social normalisation and economic resilience.

Study findings reveals shortcomings in existing regulatory framework of linking Tanzanians’ SSJs to artisanal industrial clusters, for improvements to better support the inclusiveness in GVCs. Findings of this research invite interventions on institutional capabilities and entrepreneurial competencies to enhance the capabilities of small-scale jewellers (SSJs). Like other studies, this study involved cross-sectional data, limit targeted study population as representative of SSJs in industrial clusters and GVCs in economic crises at limited time.

The study findings makes important practical contributions to the Tanzania’s SSJs by examining mediating role of artisanal industrial clusters hence informing policymakers of mining sector how to improve accessibility on GVCs by focus on offering great institutional capabilities and entrepreneurial competencies. These findings will help SSJs and policy makers to get better understanding of the relationships in exploitation of artisanal industrial clusters when accessing GVCs. Therefore, they can make better decisions on implementing artisanal industrial clusters as well as management accessing GVCs, so that SSJs will attain the best possible performance.

This emphasises the importance of community empowerment in the GVCs process through artisanal industrial clusters. Study findings indicate the influence of industrial relations to social dynamics which are previously inadequately addressed and scantly researched. In actual fact study propose initiatives that ensure local communities benefit socially from the integration of SSJs into GVCs through artisanal industrial clusters. Findings suggest local communities that take into account inter-sectionality of artisanal industrial clusters and inclusive GVCs, by considering how factors like education, trust and social protection status intersect to influence the social inclusiveness of SSJs.

There is limited evidence of linking Tanzanians’ SSJs to GVCs in social normalisation and economic resilience and few researchers have explored this topic. This article leverages exploitation of industrial clusters in normalisation and economic resilience to developing countries such as Tanzania as way of improving shared prosperity, sustainability, inclusive growth, cohesion, value chain upgrading and financial inclusion to SSJs.

The purpose of this study is to provide information that reflects the true state of affairs and to develop a conceptual model that examines the moderating effect of uncertainty on relative brand trust–love.

Extant empirical studies, conceptual literature and related theories were identified and integrated to develop the conceptual model.

Findings of the study suggest that relative brand trust–love arises because of different components of uncertainty. Relative brand trust–love exhibited by the customer is moderated by uncertainty at the individual level, brand level and technology level. These uncertainties represent the situation that considers if relative brand trust–love can be enhanced. A few propositions have been suggested for such situations to enhance relative brand trust–love.

This paper proposes a conceptual model and also makes research propositions that need to be validated and confirmed empirically. It adds value to theoretical advancements in strengthening the customer–brand relationship. Furthermore, results suggest that relative brand trust–love can be improved over time if these components can be supported by increasing information at different levels. The conclusions will be informative to practitioners and brand managers in strengthening a brand into the market more effectively. Moreover, it can transform the brand into a global brand and help build sustainable long-term relationships. Findings can be useful and interesting information for the researchers who are interested in finding strategic ways for effective decision-making related to brand trust and brand love.

The conceptual model, and the propositions related to different levels of uncertainty, and its effect on relative brand trust–love, is a new approach in enhancing relative brand trust–love and hence helpful in strengthening the quality of customer–brand relationships.

The global food industry is faced with the dilemma of finding a balance between food wastage and food shortage. Approximately one-third of food produced globally goes to waste, while about 800 million people suffer from undernourishment. Given this context, the purpose of this study is to investigate the unresolved challenges related to enhancing transparency associated with products of high perishability and low shelf life.

The authors conducted 25 interviews with global agri-food supply chains (AFSCs) experts to ask what impedes the progress of the current technologies, such as blockchain, to enable transparency and traceability (T&T) in AFSCs.

The findings indicate barriers at the individual, firm and supply chain levels. Based on these barriers, the authors propose an interconnected framework to explain technologically-driven T&T and guide on barrier removal from AFSCs. The authors conclude that by applying technology (i.e. blockchain) the authors can resolve the tension of supporting T&T in AFSCs. This can enable the efficient and transparent tracking of goods, reduction of food waste and loss, as well as promotion of the use of recyclable packaging and further sustainable practices and materials, all of which are aligned with a range of UN Sustainable Development Goals (2, 8, 10 and 12). Moreover, the authors see that some factors are interrelated. Based on these factors, the authors build an interconnected framework to guide on barrier removal from AFSCs. Managers in AFSC would find the findings especially relevant.

Drawing on industrial network theory and signalling theory, the authors propose an interconnected framework for explaining barriers (challenges) and potential solutions (opportunities) to T&T in AFSCs. This framework is developed by examining the interconnections of barriers at micro, meso and macro levels and applying signalling theory to explain how solutions address these barriers. The specific contributions of this study are: the list of barriers that impede the implementation of technological solutions for T&T in AFSCs; and a three-stage framework that explains how to remove the barriers for T&T. The study is limited by the focus on blockchain, which calls for future research once the next decentralised technology becomes available.

This paper aims to assess the relationships between foreign direct investment (FDI) and domestic entrepreneurship (DE) with the moderating role of formal institutions (FI), logistics and information communication technology (ICT) capacities.

The study is based on unbalanced panel data of 53 countries from 2006 to 2020 at different stages of development, using a fuzzy-set qualitative comparative analysis.

The research results indicate that FDI directly affects the establishment of domestic entrepreneurship. Additionally, FDI firms via the buffer mechanism of FI, logistics and ICT development for DE. Through its adjustment to the quality of institutions, logistics and ICT infrastructure, GDP per capita determines the direction of FDI's impact on DE.

The study's findings grant empirical evidence and theoretical contributions to the relationship between FDI and domestic entrepreneurial development through the buffering mechanism of FI, logistics and the role of ICT.

This paper aims to analyze the socioeconomic impacts of infectious diseases based on uncertain behaviors of social and effective subsystems in the countries. The economic impacts of infectious diseases in comparison with predicted gross domestic product (GDP) in future years could be beneficial for this aim along with predicted social impacts of infectious diseases in countries.

The proposed uncertain SEIAR (susceptible, exposed, infectious, asymptomatic and removed) model evaluates the impacts of variables on different trends using scenario base analysis. This model considers different subsystems including healthcare systems, transportation, contacts and capacities of food and pharmaceutical networks for sensitivity analysis. Besides, an adaptive neuro-fuzzy inference system (ANFIS) is designed to predict the GDP of countries and determine the economic impacts of infectious diseases. These proposed models can predict the future socioeconomic trends of infectious diseases in each country based on the available information to guide the decisions of government planners and policymakers.

The proposed uncertain SEIAR model predicts social impacts according to uncertain parameters and different coefficients appropriate to the scenarios. It analyzes the sensitivity and the effects of various parameters. A case study is designed in this paper about COVID-19 in a country. Its results show that the effect of transportation on COVID-19 is most sensitive and the contacts have a significant effect on infection. Besides, the future annual costs of COVID-19 are evaluated in different situations. Private transportation, contact behaviors and public transportation have significant impacts on infection, especially in the determined case study, due to its circumstance. Therefore, it is necessary to consider changes in society using flexible behaviors and laws based on the latest status in facing the COVID-19 epidemic.

The proposed methods can be applied to conduct infectious diseases impacts analysis.

In this paper, a proposed uncertain SEIAR system dynamics model, related sensitivity analysis and ANFIS model are utilized to support different programs regarding policymaking and economic issues to face infectious diseases. The results could support the analysis of sensitivities, policies and economic activities.

• A new system dynamics model is proposed in this paper based on an uncertain SEIAR model (Susceptible, Exposed, Infectious, Asymptomatic, and Removed) to model population behaviors;

• Different subsystems including healthcare systems, transportation, contacts, and capacities of food and pharmaceutical networks are defined in the proposed system dynamics model to find related sensitivities;

• Different scenarios are analyzed using the proposed system dynamics model to predict the effects of policies and related costs. The results guide lawmakers and governments' actions for future years;

• An adaptive neuro-fuzzy inference system (ANFIS) is designed to estimate the gross domestic product (GDP) in future years and analyze effects of COVID-19 based on them;

• A real case study is considered to evaluate the performances of the proposed models.

A new system dynamics model is proposed in this paper based on an uncertain SEIAR model (Susceptible, Exposed, Infectious, Asymptomatic, and Removed) to model population behaviors;

Different subsystems including healthcare systems, transportation, contacts, and capacities of food and pharmaceutical networks are defined in the proposed system dynamics model to find related sensitivities;

Different scenarios are analyzed using the proposed system dynamics model to predict the effects of policies and related costs. The results guide lawmakers and governments' actions for future years;

An adaptive neuro-fuzzy inference system (ANFIS) is designed to estimate the gross domestic product (GDP) in future years and analyze effects of COVID-19 based on them;

A real case study is considered to evaluate the performances of the proposed models.

This study aims to focus on sustainable supplier selection in a construction company considering a new multi-criteria decision-making (MCDM) method based on dominance-based rough set analysis. The inclusion of sustainability concept in industrial supply chains has started gaining momentum due to increased environmental protection awareness and social obligations. The selection of sustainable suppliers marks the first step toward accomplishing this objective. The problem of selecting the right suppliers fulfilling the sustainable requirements is a major MCDM problem since various conflicting factors are underplay in the selection process. The decision-makers are often confronted with inconsistent situations forcing them to make imprecise and vague decisions.

This paper presents a new method based on dominance-based rough sets for the selection of right suppliers based on sustainable performance criteria relying on the triple bottom line approach. The method applied has its distinct advantages by providing more transparency in dealing with the preference information provided by the decision-makers and is thus found to be more intuitive and appealing as a performance measurement tool.

The technique is easy to apply using “jrank” software package and devises results in the form of decision rules and ranking that further assist the decision-makers in making an informed decision that increases credibility in the decision-making process.

The novelty of this study of its kind is that uses the dominance-based rough set approach for a sustainable supplier selection process.

The purpose of this study is to identify the relationship between artificial intelligence (AI) and fake news detection. It also intended to explore the negative effects of fake news on society and to find out trending techniques for fake news detection.

“Preferred Reporting Items for the Systematic Review and Meta-Analysis” were applied as a research methodology for conducting the study. Twenty-five peer-reviewed, most relevant core studies were included to carry out a systematic literature review.

Findings illustrated that AI has a strong positive relationship with the detection of fake news. The study displayed that fake news caused emotional problems, threats to important institutions of the state and a bad impact on culture. Results of the study also revealed that big data analytics, fact-checking websites, automatic detection tools and digital literacy proved fruitful in identifying fake news.

The study offers theoretical implications for the researchers to further explore the area of AI in relation to fake news detection. It also provides managerial implications for educationists, IT experts and policymakers. This study is an important benchmark to control the generation and dissemination of fake news on social media platforms.

Farmer producer organisations (FPOs) are considered as a strategy to improve the livelihoods of small farmers through economies of scale by providing collective strength to farmers for improved access to production technology, value-addition services, high-quality inputs and marketing services for improving their incomes. This study investigates the impact of FPO membership on organic farming household's income in Northeast India.

This study uses field survey data collected from all four districts of Sikkim. Primary data were obtained from a survey of 560 organic farming households, 280 of which are FPO members and the rest 280 are non-members. Propensity score matching (PSM) is used to estimate the impact of FPO membership on net returns, return on investment (ROI) and profit margin.

Results show that the FPO members had, on average, Rs. 7,254–8,133 higher annual net returns, 4.6–4.8% higher ROI and 8–8.4% higher profit margin than the non-members. The findings confirm that FPO membership has a positive and significant impact on net returns, return on investment and profit margin. Also, heterogeneity analysis indicates that FPO membership has larger positive impact on relatively bigger farmers and female-headed households.

As the study was based on a cross-sectional survey, the findings may be subjected to some limitations.

This study is based on a novel data set, collected specifically to examine the economic impact of FPO membership on organic farming in India.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2023-0451