Search results

The purpose of this study is to investigate the effect of digital platforms on the resilience of food entrepreneurs in non-Western countries before and after the COVID-19 pandemic.

This study adopts a qualitative approach using cross-case analysis based on interviews with 17 restaurants in Iran’s touristic Torqabeh region. The data were collected before (2018) and after (2023) the COVID-19 outbreak.

The findings indicate that social and economic factors, particularly procurement and technological aspects of the food value chain, exhibited high resilience, enabling food entrepreneurs to revitalize their functions post-pandemic. The results reveal that digital platforms and online food shopping within food entrepreneurship contributed to this resilience by enhancing food distribution and potentially broadening equitable food access.

The main limitations of this research are linked to the research method, which focuses on qualitative coding, and its geographic focus on Iran, which may limit the generalizability of the findings to other contexts. The key implications include providing additional insights into the resilience of food entrepreneurship, illustrating the varied effects of social, economic and legal factors on revitalizing food entrepreneurship and emphasizing the crucial role of digital platforms in advancing food entrepreneurship.

The study advances the understanding of resilience in food entrepreneurship, contributing to both theoretical and practical strategies in emerging market contexts and offering insights for future research.

Considering the construction industry’s vital role in economic development and social consequences, this study seeks to pinpoint critical barriers hindering Turkey’s sustainable construction (SC). Although several studies highlighted the barriers to SC worldwide, none identified the critical factors. By identifying and understanding these barriers, the research aims to comprehensively understand practices and formulate strategic recommendations to promote sustainable construction.

A systematic approach is adopted to achieve the research objectives. The study involves identifying potential barriers to SC with a systematic literature review. A questionnaire was organized and distributed via e-mail to architects, civil engineers, and contractors. The criticality of identified barriers was determined with normalized mean value analysis, and critical barrier factors (CBFs) to SC were isolated with exploratory factor analysis. Finally, the effect size of these factors was quantified through structural equation modeling.

This study identified 32 critical barriers hindering the SC in the Turkish construction industry among 49 barriers. Furthermore, this study revealed six CBFs to SC that are “inadequate supervision and control of SC,” “fear of transition to sustainable construction and disruptions in adoption,” “lack of educational opportunities,” “return on investment and financial bias,” “awareness and knowledge gap about SC,” and “lack of demand from stakeholders.” Among them, “awareness and knowledge gap about SC,” “fear of transition to SC,” “lack of educational opportunities,” “lack of demand from stakeholders,” and “inadequate supervision and control of SC” were determined as the very highly crucial CBFs hindering SC.

Although some studies have identified the barriers to SC qualitatively and quantitatively, studies have yet to provide insights into the critical barrier factors hindering SC. Therefore, this study comprehensively and quantitatively determines the relevant CBFs to SC using exploratory factor analysis and utilizes confirmatory factor analysis and structural equation modeling to present a structural model of how critical factors affect the SC.

Although the importance and variety of emotions have been emphasized in existing literature, studies on discrete emotions remain limited. This study aims to propose a method for more precise recognition and calculation of emotions in massive amounts of online data on attraction visitor experiences and behaviour, by using discrete emotion theory.

Using HowNet’s word similarity calculation technique, this study integrated multiple generic dictionaries, including the sentiment vocabulary ontology database of the Dalian University of Technology, the National Taiwan University Sentiment Dictionary and the Boson Dictionary. Word2vec algorithm filters emotion words unique to hospitality and tourism in 1,596,398 texts from Sogou News, Wikipedia and Ctrip reviews about attractions, and 1,765,691 reviews about attractions in China.

The discrete sentiment dictionary developed in this study outperformed the original dictionary in identifying and calculating emotions, with a total vocabulary extension of 12.07%, demonstrating its applicability to tourism.

The developed new dictionary can be used by researchers and managers alike to quickly and accurately evaluate products and services based on online visitor reviews.

To the best of the authors’ knowledge, this study is the first to construct a sentiment dictionary based on discrete emotion theory applicable to hospitality and tourism in the Chinese context. This study extended the applicability of affective psychology to hospitality and tourism using discrete emotion theory. Moreover, the study offers a methodological framework for developing a domain-specific sentiment dictionary, potentially applicable to other domains in hospitality.

This study demonstrates how artificial intelligence (AI) shapes the strategic planning process in volatile, uncertain, complex and ambiguous (VUCA) business environments. Having adopted various domains of the Cynefin framework, the research explores AI's transformative potential and provide insights regarding how organisations can harness AI-driven solutions for strategic planning.

This conceptual paper theorises the role of AI in strategic planning process in a VUCA world by integrating extant knowledge across multiple literature streams. The “model paper” approach was adopted to provide a theoretical framework predicting relationships among considered concepts.

The paper highlights potential application of the Cynefin framework to manage complexities in strategic decision-making process, the transformative impact of AI at different stages of strategic planning, the required strategic planning characteristics within VUCA to be supported by AI and the attendant challenges posed by AI integration in the uncertain business landscape.

This study pioneers a theoretical exploration of AI's role in strategic planning within the VUCA business landscape, guided by the Cynefin framework. Thus, it enriches scholarly discourse and expands knowledge frontiers.

The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI).

Leveraging a novel methodological approach, the study curates a corpus of 364 documents from Scopus spanning 2022 to 2024. Using the term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects the thematic essence of the ethical discourse in generative AI across diverse domains, including education, healthcare, businesses and scientific research.

The results reveal a diverse range of ethical concerns across various sectors impacted by generative AI. In academia, the primary focus is on issues of authenticity and intellectual property, highlighting the challenges of AI-generated content in maintaining academic integrity. In the healthcare sector, the emphasis shifts to the ethical implications of AI in medical decision-making and patient privacy, reflecting concerns about the reliability and security of AI-generated medical advice. The study also uncovers significant ethical discussions in educational and financial settings, demonstrating the broad impact of generative AI on societal and professional practices.

This study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM. It highlights the need for dynamic governance and continual monitoring of AI’s evolving ethical landscape, offering a model for future research and policymaking in diverse fields.

The study introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains.

Significant interest in Integrated Management Systems (IMS), as a key area within ISO-related Management System Standards (MSS) literature, has been evident from both academia and industry over the past three decades. This study aims to map the evolution and current state of IMS research and propose possible directions for future studies.

A comprehensive content and bibliometric analysis of 846 documents from the Scopus database across the period 1995 to 2023 was conducted. This included performance analysis to track publication trends and identify key contributors, and content analysis to specify dominant research methodologies and the MSS most commonly integrated. Furthermore, science mapping techniques—such as co-authorship networks, keyword co-occurrence analysis, and bibliographic coupling—were utilized to outline the collaborative networks and the conceptual and intellectual structure of the field.

The study identifies three principal IMS research themes: the practical implementation of IMS, their role in promoting sustainability and social responsibility, and their impact on continuous performance improvement. It also highlights the field’s evolution and key research constituents—including influential works, prolific authors, leading academic institutions and countries, and top publishing journals. It further reveals that IMS research exhibits strong collaboration across authors and countries, and a rich methodological plurality, notably with a marked increase in empirical surveys in recent years. Additionally, it identifies the most frequently referenced MSS for integration, prominently featuring ISO 9001, ISO 14001, and ISO 45001/OHSAS 18001.

This study is original in its application of a dual analytical approach—bibliometric and content analysis—to provide a holistic overview of IMS research. It offers new insights into the integration of diverse MSS and proposes several promising paths for future research. Among the most prominent are standardizing IMS fundamental specifications, conducting more empirical research with advanced methods to evaluate the effects of MSS integration, providing practical support for organizations in IMS implementation through tailored methodologies and tools, and exploring the potential of Industry 4.0 and 5.0 technologies to advance IMS practices.

This study explores the concept of “workplace survivor syndrome” (WSS) using bibliometric analysis and literature review, thereby identifying the avenues for future research in business management.

The study adopted a methodological combination of bibliometric analysis and literature review. The methodological order is as follows: using the Scopus database and identifying 118 articles from ABDC listed journals, performance analysis by VOS viewer (citation analysis), science mapping analysis by Biblioshiny (visualisation and graphical presentation), and finally, the content analysis of the best 40 articles with a minimum of 50 citations and without any year restriction.

The bibliometric analysis reveals the most influential authors, articles, journals, countries, publication trends, impactful articles, and impactful authors of workplace survivor syndrome research, along with popular keywords used in this area. The content analysis identified three themes: emotional, cognitive, and behavioural syndrome. The content analysis reported the central mechanism adopted in 40 articles, including theories, methodologies, variables, sample size, etc. Additionally, the study explored the positive reviews on WSS.

The study considered only the articles from the ABDC journal quality list for the review.

The article is persuasively the first research to provide the intellectual structure and comprehensive bibliometric analysis of workplace survivor syndrome.

The objective of this paper is to describe the evolution of the available subject heading list, i.e. the CC Soggettario (Carabinieri Corps Soggettario), towards a thesaurus, that is CCThes (Carabinieri Corps Thesaurus), to support subject indexing and retrieval of the documentary heritage held by the Historical Office of the General Command of the Carabinieri Corps. This work follows the need to implement a controlled vocabulary compliant with the state-of-the-art standards.

The methodology implements the practice of reengineering available vocabularies, following standardised guidelines for thesaurus development. The conversion process includes the balance maintenance of what has been achieved in the CC Soggettario and the enrichment of the semantic structure in the thesaurus by using both deductive and inductive methods.

The main result of this study is a thesaurus compliant with ISO 29964-1:2011 recommendations, which improves information retrieval performances and interoperability with other vocabularies and applications. It generally has a mono-hierarchical structure with the possibility of admitting, as an exception, the poly-hierarchy for a few concepts. An introductive user guide has been created as a complementary tool to the CCThes.

This is an applied study which deals with Knowledge Organisation System (KOS) reengineering and outlines this process using a pragmatic approach. The paper strength lies in providing the description of performed activities and conveying a set of resources to approach KOS reengineering practice. The study is also relevant for the preservation and diffusion of a part of the social memory and identity of Italy.

A methodological approach is required that complements studies based on surveys, providing a perspective with greater truthfulness and coverage. The study aims to develop a methodology to validate psychological/managerial constructs using data from Google Trends, taking as a case study a critical thinking (CT) scale in organizational domains previously supported by survey data.

The developed methodology consists of eight stages, in which the following is integrated: (1) Internet search interest data (19 Spanish-speaking countries); (2) deductive research processes (e.g. theoretical model, linguistic manifestations, fieldwork, data matrix, analysis statistical, reporting); (3) psychometric properties (e.g. construct validity, criterion validity, reliability) and (4) objective data to examine criterion validity (e.g. unemployment rate).

The application of the methodology produces evidence that supports the reliability (Cronbach’s alpha, Guttman’s λ₄), construct validity (intra-correlations and correlations with reference variables: “entrepreneurship,” “critical thinking,” “soccer,” “beer,” “pornography”) and criterion validity (prediction of unemployment rate) of the CT scale.

The methodology makes it possible to support or invalidate the quality of construct measurement scales by planning, capturing and processing data available on the internet.

This manuscript is useful for research in business management (and related areas), which is intensive in the use of psychological/managerial constructs.

The methodology uses a new type of evidence; it is noninvasive, usually more truthful than responses to surveys, and has greater coverage of people participating indirectly in the study.

This paper aims to explain and demonstrate how the Repertory Grid Technique (RGT) and Honey’s Content Analysis (HCA) can make new contributions to the field of Operations and Supply Chain Management (OSCM). The proposition involves integrating these complementary analyses to fortify the rigor of qualitative research and establish robust data analysis protocols to identify the main attributes of interviewees regarding a phenomenon while understanding in their perspective how these attributes impact the desired analysis outcome.

This study uses examples with rich empirical data from 40 interviewees across two organizations. The examples use a protocol that allows the grouping of meanings from different knowledgeable individuals and capturing relevant constructs related to an outcome.

The combination of RGT and HCA permits researchers to effectively identify and analyze the constructs individuals and groups utilize to comprehend the subject matter under investigation. Consequently, these techniques present a structured means to conduct grounded theory investigations and interpretive research, thereby enabling the iterative development of the preliminary conceptual models necessary for OSCM field advancement.

We present two examples in which the protocol is applied to the field of OSCM. These examples illustrate that the techniques provide valuable opportunities for OSCM research, particularly for addressing the limitations related to sample size. Ultimately, RGT and HCA complement quantitative methodologies by uncovering nuanced variations and micro-foundations within firm- and network-level phenomena, offering insights essential for advancing our understanding of OSCM dynamics in specific contexts.