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Consequence prediction is an emerging topic in safety management concerning the severity outcome of accidents. In practical applications, it is usually implemented through supervised learning methods; however, the evaluation of classification results remains a challenge. The previous studies mostly adopted simplex evaluation based on empirical and quantitative assessment strategies. This paper aims to shed new light on the comprehensive evaluation and comparison of diverse classification methods through visualization, clustering and ranking techniques.

An empirical study is conducted using 9 state-of-the-art classification methods on a real-world data set of 653 construction accidents in China for predicting the consequence with respect to 39 carefully featured factors and accident type. The proposed comprehensive evaluation enriches the interpretation of classification results from different perspectives. Furthermore, the critical factors leading to severe construction accidents are identified by analyzing the coefficients of a logistic regression model.

This paper identifies the critical factors that significantly influence the consequence of construction accidents, which include accident type (particularly collapse), improper accident reporting and handling (E21), inadequate supervision engineers (O41), no special safety department (O11), delayed or low-quality drawings (T11), unqualified contractor (C21), schedule pressure (C11), multi-level subcontracting (C22), lacking safety examination (S22), improper operation of mechanical equipment (R11) and improper construction procedure arrangement (T21). The prediction models and findings of critical factors help make safety intervention measures in a targeted way and enhance the experience of safety professionals in the construction industry.

The empirical study using some well-known classification methods for forecasting the consequences of construction accidents provides some evidence for the comprehensive evaluation of multiple classifiers. These techniques can be used jointly with other evaluation approaches for a comprehensive understanding of the classification algorithms. Despite the limitation of specific methods used in the study, the presented methodology can be configured with other classification methods and performance metrics and even applied to other decision-making problems such as clustering.

This study sheds new light on the comprehensive comparison and evaluation of classification results through visualization, clustering and ranking techniques using an empirical study of consequence prediction of construction accidents. The relevance of construction accident type is discussed with the severity of accidents. The critical factors influencing the accident consequence are identified for the sake of taking prevention measures for risk reduction. The proposed method can be applied to other decision-making tasks where the evaluation is involved as an important component.

The study directs attention to the psychological conditions experienced and knowledge management practices leveraged by faculty in higher education institutes (HEIs) to cope with the shift to emergency remote teaching caused by the COVID-19 pandemic. By focusing attention on faculty experiences during this transition, this study aims to examine an under-investigated effect of the pandemic in the Indian context.

Interpretative phenomenological analysis is used to analyze the data gathered in two waves through 40 in-depth interviews with 20 faculty members based in India over a year. The data were analyzed deductively using Kahn’s framework of engagement and robust coding protocols.

Eight subthemes across three psychological conditions (meaningfulness, availability and safety) were developed to discourse faculty experiences and challenges with emergency remote teaching related to their learning, identity, leveraged resources and support received from their employing educational institutes. The findings also present the coping strategies and knowledge management-related practices that the faculty used to adjust to each discussed challenge.

The study uses a longitudinal design and phenomenology as the analytical method, which offers a significant methodological contribution to the extant literature. Further, the study’s use of Kahn’s model to examine the faculty members’ transitions to emergency remote teaching in India offers novel insights into the COVID-19 pandemic’s effect on educational institutes in an under-investigated context.

Drawing on organizational learning and dynamic capabilities literature, this study aims to explore how suppliers from traditional emerging economies (STEE) can acquire, assimilate and use new knowledge essential for the development of production and marketing capabilities. These capabilities then facilitate suppliers in climbing the value chain from B-to-B to B-to-C.

The study adopted a longitudinal and multiple case study design to examine the practices of suppliers operating in a traditional emerging economy setting. This study selected Pakistan textile industry as an empirical setting, which is a predominantly supplier market for global buyers. Data sources entail semi-structured interviews with top executives and senior-level managers in four case firms and secondary data obtained from diverse sources.

The study identified transitionary phases of capabilities development that are facilitated by boundary-spanning knowledge acquisition and transformation in a dynamic manner. These capabilities are essential for a supplier’s entry into downstream international markets (i.e. launching its own products/brands in the end consumers’ market).

The findings could help managers in STEEs to understand the strategic importance of supply chain ties in their learning and capabilities development. It also provides strategic insights on what, how and why involved parties do engage over an extended period of time. Moreover, the findings of this study could help other firms to know and adopt the right type of technology(s) and systems that can help them reduce the technological gap in producing and marketing market-winning products.

This study advances the recent academic discussion that focusses on learning by supplying and the value-chain movement of suppliers (i.e. B-to-C) from their B-to-B engagements. The findings identified the vital and beneficial role of long-term relationships with global value chain partners in learning and capabilities development that led to value creation in the traditional emerging economy.

Human–robot collaboration (HRC) is an emerging research field for the construction industry along with construction robot adoption, but its implementation remains limited in construction sites. This paper aims to identify critical risk factors and their interactions of HRC implementation during engineering project construction.

Literature research, expert interviews, a questionnaire survey and a social network analysis (SNA) method were used. First, literature research and expert interviews were employed to identify risk factors of HRC implementation and preliminarily understand factor interactions. Second, a questionnaire survey was conducted to determine the degree of interactions between risk factors. Third, based on the data collected from the questionnaire survey, SNA metrics were used to find critical risk factors and critical interactions.

The critical risk factors consist of robot technology reliability, robot-perceived level, conflict between designers and users of construction robots, organisational culture, organisational strength, project cost requirements, changeability of project construction, project quality requirements and project safety requirements. The interactions between risk factors are strong and complex. Robot technology risk factors were relatively fundamental risk factors, and project risk factors had a direct influence on the risk of HRC implementation. The implementation cost of HRC was not identified as a critical risk factor. Individual risk factors could be mitigated by improving technical and organisational factors.

This paper contributes to the body of knowledge in the field of both HRC behaviours and its risk management in construction project management. Identifying the critical risk factors and their interactions of HRC implementation in the construction industry and introducing social network theory to the research on critical risk factors are the innovations of this paper. The findings and proposed suggestions could help construction professionals to better understand the HRC risk factors and to manage the risk of HRC implementation more effectively.

This study examines the impact of the Association to Advance Collegiate Schools of Business (AACSB) accreditation on education quality. We discern the prospective influences of AACSB, focusing on shifts in teaching methods and content and assessment procedures.

Using a case study approach, in-depth interviews are conducted with a Japanese-accredited business school’s faculty members to understand their perceptions of the school’s education-quality issues. The data were thematically analyzed.

Respondents acknowledged that AACSB accreditation has positively influenced teaching, encouraging active learning and the case method. However, they also indicated that accreditation had a restrictive effect on assessment activities, pushing toward compliance rather than genuine learning evaluation. This dichotomy suggests a need for balancing standard adherence with the flexibility to maintain educational depth and assessment integrity.

Convenience sampling may introduce self-selection bias. Furthermore, the qualitative case study approach does not allow for statistical generalization. However, when combined with existing literature, the findings can be analytically generalized and transferred to other contexts.

We provide insights regarding AACSB accreditation’s impact on business education, encompassing shifts in teaching methods and content and faculty perceptions of assessment. This study enhances the scholarly understanding of business school accreditation and offers guidance to accredited or accreditation-seeking academic institutions.

This paper explores the current state of Robo-advisory services in India. This paper further highlights the problems experienced by the service providers in disseminating the innovative business model among the Indians.

The study adopts a qualitative approach to investigate the industry experts by conducting semi-structured interviews. The data collected were transcripted and further analyzed using the content analysis technique. Finally, the authors utilized categorization and coding techniques to frame broad study themes.

The study findings reveal that the three pillars of Robo-advisory are ease and convenience, the time factor and transparency in operations. Robo-advisory services are still at a nascent stage in India. Furthermore, keeping the sentiments of Indians in mind, FinTech companies could combine automated Robo-advisory with a human touch of a wealth manager for optimal advisory services.

Since the present study is qualitative, the authors cannot generalize the study results. Future research can focus on empirically proving the constructs of the study using quantitative methods.

Robo-advisors have a well-established market in developed nations but are still nascent in developing countries like India. The current focus of service providers and regulatory authorities must be to increase awareness among investors by educating the investors and building trust.

The present study is the first to qualitatively synthesize the challenges faced by the FinTech service providers in the Indian market.

This study aims to answer the question whether investment funds managers exhibit behavioural biases in their investment decisions. Furthermore, it investigates if fund managers, as a group of institutional investors, make decisions in response to central bank’s communication as well as other information in relation to various behavioural inclinations.

A comprehensive study was conducted based on a questionnaire, which is composed of three main parts exploring: (1) general information about the funds under the management of the surveyed group of fund managers, (2) factors that influence the investment process with an emphasis on the National Bank of Poland communication and (3) behavioural inclinations of the surveyed group. Cronbach’s alpha statistic was applied for measuring the reliability of the survey questionnaire and then chi-squared test was used to investigate the relationships between the answers provided in the survey.

The central bank’s communication matters for investors, but its impact on their decisions appears to be only moderate. Interest rates were found to be the most important announcements for investment fund managers. The stock market was the most popular market segment where the investments were made. The ultra-short time horizon played no, or only small, role in the surveyed fund managers’ decisions as most of them invested in a longer horizon covering 1 to 5 years. Moreover, most respondents declared that they considered in their decisions the information about market expectations published in the media. Finally, majority of the fund managers manifested limited rationality and were subject to behavioural biases, but the decisions and behavioural inclinations were independent and, in most cases, they did not influence each other.

The results reported in this study can be used in practice to better understand and to improve the fund managers’ decision-making processes.

Apart from the commonly tested behavioural biases in the group of institutional investors in the existing literature, such as loss aversion, disposition effect or overconfidence, this paper also focuses on the less intensively analysed behavioural inclinations, i.e. framing, illusion of the control, representativeness, sunk cost effect and fast thinking. The originality of this study further lies in the way the research was conducted through interviews with fund managers, who were found to be subject to behavioural biases, although those behavioural inclinations did not influence their investment decisions. This finding indicates that professionalism and collectivism in the group of institutional investors protect them from irrationality.

After each of the COVID-19-induced lockdowns, an unprecedented surge in leisure travel was observed, resulting in tourists flocking to places of tourist interest. This phenomenon was termed revenge travel in popular literature. The purpose of this study is to explore the phenomenon of revenge travel in detail through an academic lens. It examines the psychological and emotional motivations for revenge travel while studying the differences in travel behaviour pre- and post-pandemic.

The study applies an interpretive phenomenological approach to explore post-pandemic travel behaviour. Data were collected via personal in-depth semi-structured interviews. Thematic analysis of the transcripts was conducted to arrive at discussion themes.

Drawing on the established theories, the findings of the study indicate that lockdown fatigue coupled with mortality salience induced individuals towards leisure travel. This in turn led to mood alleviation and compensation for the deprivation undergone.

The study reveals important insights into post-pandemic preferences of travel destinations (off-beat locations near urban clusters), accommodation options (more travellers choosing home stays and stand-alone properties) and vacation itineraries (a gradual shift towards slow tourism with more focus on immersive experiences). Further, there are indications that hotels may develop “workcations” and “staycations” as a new line of offering.

The study adds to the small body of knowledge on revenge travel. It adopts a phenomenological approach, thereby capturing the “lived experiences” of the participants and providing an in-depth look into the psychological and emotional motivations of revenge travel that have not been explored previously. The study provides insights into the travellers' psychology post a period of withdrawal and restraint.

This research aims to explore the research data ethics governance framework and collaborative network to optimize research data ethics governance practices, to balance the relationship between data development and utilization, open sharing, data security and to reduce the ethical risks that may arise from data sharing and utilization.

This study explores the framework and collaborative network of research data ethics policies by using the UK as an example. 78 policies from the UK government, university, research institution, funding agency, publisher, database, library and third-party organization are obtained. Adopting grounded theory (GT) and social network analysis (SNA), Nvivo12 is used to analyze these samples and summarize the research data ethics governance framework. Ucinet and Netdraw are used to reveal collaborative networks in policy.

Results indicate that the framework covers governance context, subject and measure. The content of governance context contains context description and data ethics issues analysis. Governance subject consists of defining subjects and facilitating their collaboration. Governance measure includes governance guidance and ethics governance initiatives in the data lifecycle. The collaborative network indicates that research institution plays a central role in ethics governance. The core of the governance content are ethics governance initiatives, governance guidance and governance context description.

This research provides new insights for policy analysis by combining GT and SNA methods. Research data ethics and its governance are conceptualized to complete data governance and research ethics theory.

A research data ethics governance framework and collaborative network are revealed, and actionable guidance for addressing essential aspects of research data ethics and multiple subjects to confer their functions in collaborative governance is provided.

This study analyzes policy text using qualitative and quantitative methods, ensuring fine-grained content profiling and improving policy research. A typical research data ethics governance framework is revealed. Various stakeholders' roles and priorities in collaborative governance are explored. These contribute to improving governance policies and governance levels in both theory and practice.