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Fuzzy-set qualitative comparative analysis (fsQCA) is explored as a transformative tool rooted in complexity theory, shedding light on uncertainties shaping real-world decisions in tourism, with a focus on its application in the hospitality domain.

This study systematically evaluates fsQCA’s application in hospitality and tourism research, employing bibliometric analysis to scrutinize the published literature since its induction in 2011. The research seeks to understand the evolving usage by qualitatively reviewing impactful studies based on total citations.

The study reveals the ascendancy of fsQCA as a predominant approach in hospitality and tourism studies, particularly in illuminating decision-making paradigms in key sectors like destination and hotel selections and entrepreneurial orientations. However, an absence of fsQCA applications in gastronomy and wine tourism is identified, signaling uncharted territories for future inquiry.

Theoretical implications include paradigm shifts to complexity theory, configural analysis and asymmetric algorithms. Practical implications involve improved decision-making and tailored marketing, benefiting industry practitioners. Limitations include potential academic bias, while future research suggests exploring sub-sectors, sustainability and emerging technologies.

This study identifies gaps in the fsQCA application and pioneers its examination within the hospitality domain, offering a unique perspective on understanding intricate relationships and configurations among variables. The study emphasizes the efficacy of asymmetric methodologies in elucidating behavioral nuances in hospitality and tourism, providing a foundation for future inquiries to expand horizons and unravel the nuanced applications of fsQCA in this research domain.

Social media platforms are highly visible platforms, so politicians try to maximize their benefits from their use, especially during election campaigns. On the other side, people express their views and sentiments toward politicians and political issues on social media, thus enabling them to observe their online political behavior. Therefore, this study aims to investigate user reactions on social media during the 2016 US presidential campaign to decide which candidate invoked stronger emotions on social media.

For testing the proposed hypotheses regarding emotional reactions to social media content during the 2016 presidential campaign, regression analysis was used to analyze a data set that consists of Trump’s 996 posts and Clinton’s 1,253 posts on Facebook. The proposed regression models are based on viral (likes, shares, comments) and emotional Facebook reactions (Angry, Haha, Sad, Surprise, Wow) as well as Russell’s valence, arousal, dominance (VAD) circumplex model for valence, arousal and dominance.

The results of regression analysis indicate how Facebook users felt about both presidential candidates. For Clinton’s page, both positive and negative content are equally liked, while Trump’s followers prefer funny and positive emotions. For both candidates, positive and negative content influences the number of comments. Trump’s followers mostly share positive content and the content that makes them angry, while Clinton’s followers share any content that does not make them angry. Based on VAD analysis, less dominant content, with high arousal and more positive emotions, is more liked on Trump’s page, where valence is a significant predictor for commenting and sharing. More positive content is more liked on Clinton’s page, where both positive and negative emotions with low arousal are correlated to commenting and sharing of posts.

Building on an empirical data set from Facebook, this study shows how differently the presidential candidates communicated on social media during the 2016 election campaign. According to the findings, Trump used a hard campaign strategy, while Clinton used a soft strategy.

Present study aimed to integrate Geographic Information Systems (GIS) and Building Information Modeling (BIM) in conjunction with multicriteria decision-making (MCDM) to enhance infrastructure investment planning.

This analysis combines GIS databases with BIM simulations for a novel highway project. Around 150 potential alternatives were simulated, narrowed to 25 more effective routes and 3 options underwent in-depth analysis using PROMETHEE method for decision-making, based on environmental, cost and safety criteria, allowing for comprehensive cross-perspective comparisons.

A comprehensive framework proposed was validated through a case study. Demonstrating its adaptability with customizable parameters. It aids decision-making, cost estimation, environmental impact analysis and outcome prediction. Considering these critical factors, this study holds the potential to advance new techniques for assessment and planning railways, power lines, gas and water.

The study acknowledges limitations in GIS data quality, particularly in underdeveloped areas or regions with limited technology access. It also overlooks other pertinent variables, like social, economic, political and cultural issues. Thus, conclusions from these simulations may not entirely represent reality or diverse potential scenarios.

The proposed method automates decision-making, reducing subjectivity, aids in selecting effective alternatives and considers environmental criteria to mitigate negative impacts. Additionally, it minimizes costs and risks while demonstrating adaptability for assessing diverse infrastructures.

By integrating GIS and BIM data to support a MCDM workflow, this study proposes to fill the existing research gap in decision-making prioritization and mitigate subjective biases.

The Sustainable Development Goals (SDGs) framework emerged as a guidepost for the transition to sustainable development. To achieve this transition, companies are encouraged to integrate these goals into their business strategies, processes and corporate reporting cycle. The purpose of this paper is to review and critique the corporate SDGs reporting literature, develop insights into the state of this research field and identify a future research agenda.

Using a structured literature review (SLR) methodology, the paper reviews 65 empirical papers published in this field to identify how the current research is developing, offers a critique and identifies future research avenues to advance this field.

Corporate SDGs reporting is developing as a research area of great importance. The findings reveal that current SDGs reporting literature lacks theorisation, overly focusses on publicly listed companies and succinctly describes organisations’ engagement with the SDGs as superficial. Surprisingly, regions such as North America, the UK and other emerging economies have received less attention from scholars. Further, only a few authors have specialised in this field, and there currently exists low levels of international collaborations among authors as well as practitioners.

The paper provides a novel contribution to the emerging field of corporate SDGs reporting. The key theoretical implications from this study’s SLR include the need for more interventionist research. Although there is an increasing number of accounting scholars developing research within this field, the prevailing research is concentrated on corporate SDGs engagement, drivers of SDGs reporting and scope of SDGs reporting. Furthermore, the scientific discourse remains largely under-theorised with positivist framings primarily focussed on the “what” questions. Thus, a modification to the current approaches and research methods is necessary to advance this field further.

The study provides practitioners with valuable insights into the current state of corporate reporting on the SDGs. To achieve more substantive engagement and reporting, a deeper understanding of the factors that influence corporate behaviour and disclosure practices is necessary. In particular, the study identifies new opportunities for practitioners to enhance the value relevance of corporate SDGs reporting.

The paper offers a comprehensive structured review of the empirical papers published on corporate SDGs reporting. It contributes to deepening this nascent research field by identifying five distinct areas where accounting and business scholars may focus to advance the field further and contribute to achieving the SDGs agenda.

The topic of artificial intelligence (AI) has been expanding rapidly in recent years, gaining the attention of academics and practitioners. This study provides a structured literature review (SLR) on AI and management decisions (MDs) by analysing the scientific output and defining new research topics.

The study uses a rigorous methodological approach to summarise the state of the art of the past literature. The authors used Scopus as the database for data collection and utilised the Bibliometrix R package. In total, 204 peer-reviewed English articles were collected and analysed.

The results showed that literature in this field is emerging. Studies are focused on using AI as forecasting and classification for management decision-making, AI as a tool to improve knowledge management in organisations and extract information. The cluster analysis revealed the presence of five thematic clusters of studies on the topic.

The study’s originality lies in providing a new perspective on AI for MDs. In particular, the analysis reveals a new classification of research streams and provides fruitful research questions to continue research on the topic.

Super-tall towers have surfaced as a pragmatic remedy to meet the escalating requisites for both residential and commercial areas and to stimulate economic growth in the Middle East. In this unique regional context, optimizing spatial usage stands as a paramount consideration in the architectural design of skyscrapers. Despite the proliferation of super-tall towers, there exists a conspicuous dearth of comprehensive research pertaining to space efficiency in Middle Eastern skyscrapers. This study endeavors to bridge this substantial gap in the literature.

The research methodology utilized in this paper adopts a case study approach to accumulate data regarding super-tall towers in the Middle East, with a specific focus on investigating space efficiency. A total of 27 super-tall tower cases from the Middle East were encompassed within the analytical framework.

Key findings can be succinctly summarized as follows: (1) average space efficiency was 75.5%, with values fluctuating between a minimum of 63% and a maximum of 84%; (2) average ratio of the core area to the gross floor area (GFA) registered 21.3%, encompassing a spectrum ranging from 11% to 36%; (3) predominantly, Middle Eastern skyscrapers exhibited a prismatic architectural form coupled with a central core typology. This architectural configuration mostly catered to residential and mixed-use functions; (4) the combination of concrete and outrigger frame systems was the most frequently utilized; (5) as the height of the tower increased, space efficiency tended to experience a gradual decline and (6) no significant discernible disparities were detected in the impact of diverse load-bearing systems and architectural forms on space efficiency.

Despite the proliferation of super-tall towers, there exists a conspicuous dearth of comprehensive research pertaining to space efficiency in Middle Eastern skyscrapers. This study endeavors to bridge this substantial gap in the literature.

This study aims to examine the mediating role of audit seasonality on the association between audit fees and audit quality in Nigerian deposit money banks.

The sample comprises 14 banks with annual financial statements between 2008 and 2020. The modified Baron and Kenny’s (1986) causal mediation model by Iacobucci et al. (2007) through the use of bootstrapped partial least square structural equation modelling and Sobel’s (1986) z-test is adopted to achieve this study’s objective.

The results of the causal mediation analysis show evidence of a fully mediating role of audit seasonality in the association between audit fees and audit quality in the Nigerian banking industry.

This study extends the body of knowledge by demonstrating how audit fees influence audit quality through audit seasonality as a mediator in line with the job demands-and resources and conservation of resources theories. Regulatory authorities should be wary of policies that will further increase the workload of already burdened personnel of audit firms as the uniform fiscal year-end of 31 December introduced in the Nigerian banking system has unintended consequences on audit fees and audit quality.

To the best of the author’s knowledge, this is one of the first studies to provide evidence on the indirect association between audit fees and audit quality.

This study seeks to explicate how institutional disruptions impact multinational corporation (MNC) subsidiary control choices. It uses institutional theory to understand the influence of formal and informal institutions across countries on the type of control system employed in an MNC manufacturing subsidiary.

This study’s sample is based on a unique dataset from five trustworthy sources. We use multi-level models to account for the hierarchical nature of the sample of 1,630 multinational subsidiaries spread across 26 host countries by firms from 21 home countries.

The institutional distance between the host and the home country has a negative relationship with strategic control. In contrast, the home country’s power distance has a positive relationship with strategic control.

Study findings indicate the need to incorporate formal and informal institutional elements in the control system’s conceptual framing and design. This notion complements existing visualizations of optimizing MNC controls through extant articulations of minimizing governance costs through organizational design choices or strategic needs.

This study aims to explore the “psychological contract of safety” (PCS), a key factor in the safety climate (SC), which relies on the behavioral safety actions of workers at construction sites. While numerous factors have been identified in various sectors across different countries, there is a consensus among researchers that there is a dearth of common assessment factors specifically for the Indian construction industry (ICI). Therefore, this study undertakes a systematic review of existing literature to identify the factors that determine PCS in construction and to ascertain the relative importance index (RII) of these variables and their interrelationships using structural equation modelling (SEM).

A structured survey was conducted among 420 professionals in the ICI to collect data. This data was then analyzed using descriptive and inferential statistical methods to derive results.

The findings of the study indicate that PCS factors have a significant impact on the construction industry (CI). The inferential analysis ranks “Safety System” as the top factor with the highest RII value. The chi-square results highlight two key SC factors that enhance and regulate an organization’s safety performance. The SEM results reveal that SC factors contribute to the improvement of PCS and influence worker safety behavior.

The outcomes of this study will be beneficial for stakeholders aiming to improve safety at construction sites and enhance safety performance by fulfilling the mutual safety obligations of employers and employees and by improving safety norms, procedures and policy-making. This paper also provides a theoretical framework for scholars to reassess the results in various contexts.

Online employee reviews have emerged as a crucial information source for business managers to evaluate employee behavior and firm performance. The purpose of this paper is to test the relationship between employee personality traits, derived from online employee reviews and job satisfaction and turnover behavior at the individual level.

The authors apply text-mining techniques to extract personality traits from online employee reviews on Indeed.com based on the Big Five theory. They also apply a machine learning classification algorithm to demonstrate that incorporating personality traits can significantly enhance employee turnover prediction accuracy.

Personality traits such as agreeableness, conscientiousness and openness are positively associated with job satisfaction, while extraversion and neuroticism are negatively related to job satisfaction. Moreover, the impact of personality traits on overall job satisfaction is stronger for former employees than for current employees. Personality traits are significantly linked to employee turnover behavior, with a one-unit increase in the neuroticism score raising the probability of an employee becoming a former employee by 0.6%.

These findings have implications for firm managers looking to gain insights into employee online review behavior and improve firm performance. Online employee review websites are recommended to include the identified personality traits.

This study identifies employee personality traits from automated analysis of employee-generated data and verifies their relationship with employee satisfaction and employee turnover, providing new insights into the development of human resources in the era of big data.