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This paper examines the effect of ESG ratings and its dimensions (environmental, social and governance) on the financial performance of JSE-listed firms included in FTSE/JSE Responsible Investment Index.

The paper employs panel data covering 40 JSE-listed firms included in FTSE/JSE Responsible Investment Index between 2015 and 2019. The paper employs the two-stage least squares (2SLS) instrumental variable regression technique to estimate the effect of ESG ratings and its dimensions (environmental, social and governance) on both accounting- and market-based performance indicators.

The results of the two-stage least squares instrumental estimation analysis reveal that investment in ESG initiatives improves both accounting- and market-based indicators of financial performance. Of the ESG pillars, the paper finds environmental initiatives improves firms' financial bottom line and market performance, while a firm's social and governance practices are observed to have no effect on a firm's accounting and market performance measures.

The insights from this study proffers policy implications for firms' management, investors and regulatory authorities.

As far as the authors are concerned, this paper presents the first empirical analysis on the contribution of ESG ratings on financial performance in South Africa.

The purpose of this paper is to analyse data literacy in the new Digital Competence Framework for Citizens (DigComp 2.2). Mid-2022 the Joint Research Centre of the European Commission published a new version of the DigComp (EC, 2022). This new version focusses more on the datafication of society and emerging technologies, such as artificial intelligence. This paper analyses how DigComp 2.2 defines data literacy and how the framework looks at this from a societal lens.

This study critically examines DigComp 2.2, using the data literacy competence model developed by the Knowledge Centre for Digital and Media Literacy Flanders-Belgium. The examples of knowledge, skills and attitudes focussing on data literacy (n = 84) are coded and mapped onto the data literacy competence model, which differentiates between using data and understanding data.

Data literacy is well-covered in the framework, but there is a stronger emphasis on understanding data rather than using data, for example, collecting data is only coded once. Thematically, DigComp 2.2 primarily focusses on security and privacy (31 codes), with less attention given to the societal impact of data, such as environmental impact or data fairness.

Given the datafication of society, data literacy has become increasingly important. DigComp is widely used across different disciplines and now integrates data literacy as a required competence for citizens. It is, thus, relevant to analyse its views on data literacy and emerging technologies, as it will have a strong impact on education in Europe.

This paper reviews recent research on the expected economic effects of developing artificial intelligence (AI) through a survey of the latest publications, in particular papers and reports issued by academics, consulting companies and think tanks.

Our paper represents a point of view on AI and its impact on the global economy. It represents a descriptive analysis of the AI phenomenon.

AI represents a driver of productivity and economic growth. It can increase efficiency and significantly improve the decision-making process by analyzing large amounts of data, yet at the same time it creates equally serious risks of job market polarization, rising inequality, structural unemployment and the emergence of new undesirable industrial structures.

This paper presents itself as a building block for further research by introducing the two main factors in the production function (Cobb-Douglas): labor and capital. Indeed, Zeira (1998) and Aghion, Jones and Jones (2017) suggested that AI can stimulate growth by replacing labor, which is a limited resource, with capital, an unlimited resource, both for the production of goods, services and ideas.

Our study contributes to the previous literature and presents a descriptive analysis of the impact of AI on technological development, economic growth and employment.

This paper aims to consider the relationship between urban events and urban public space, asking whether cities have enough space for events and whether events have enough space in cities.

Policy analysis surrounding events and festivals in the Netherlands is used to understand the dynamics of urban events, supported by content analysis of policy documents. A vignette of event space struggles in Amsterdam illustrates the contradictions of the event/space relationship.

The research identifies a policy shift in the Netherlands towards urban events from expansive, festivalisation strategies to defensive, NIMBYist policies. It exposes contradictions between protecting space as a living resource and the exploitation of space for regenerative purposes. Three future scenarios for urban events are outlined: conflict and competition, growth and harmony and digitalisation and virtualisation.

Develops scenarios for the future relationship between events and urban space.

Provides an analysis of the recursive spatial implications of the growth of the events sector for cities and the growth of cities for events.

Research on technology acceptance of online entertainment with age, gender and cultural factors as moderator, is rarely conducted. Previous research predominantly focused on age or gender as moderator, neglecting the influence of cultural factors. Therefore, this study aims to investigate acceptance of online entertainment technology, incorporating age, gender and cultural factors as moderator.

Data were collected through a survey comprising 1,121 individuals aged 14–24 years from three cities in Indonesia. The proposed theoretical model examined the causal effect of acceptance and moderating effects due to individual gender, age, power distance, individualism, feminism and uncertainty avoidance (AU). Subsequently, structural equation modeling was used to evaluate the theoretical model, and the results confirmed several findings from previous research.

The findings confirmed the positive direct impact of habit and price value (PV) on behavioral intention and hedonic motivation, as well as social influence on habit. The recent findings derived from the moderating effect analysis showed that age, individualism and feminism played a moderating role in the effects on individual intention due to habit. Additionally, gender and AU moderated the effects on individual habits due to hedonic motivation.

This research contributes to the limited knowledge of technology acceptance of online entertainment, and also integrates the causal effects of individual intention due to habit, PV, hedonic motivation and social influence, considering the moderating role of culture, age and gender. Consequently, the investigation provides valuable insights into the literature by presenting evidence of age, gender and cultural differences in acceptance. Furthermore, it offers practical guidance to online entertainment application developers on designing applications to satisfy consumers of different ages, genders and cultures.

This study aims to explore the holistic context of organisational staff retention in small, medium and large organisations. It also aims to identify the factors affecting the retention of organisations of different sizes.

The study implements an empirical test of a model created during previous research with the participation of 511 employees. The responses to the online questionnaire and the modelling were analysed using the partial least squares structural equation modelling method. The models were tested for internal consistency reliability, convergent and discriminant validity, multicollinearity and model fit.

Two models were tested by organisation size, which revealed a total of 62 significant correlations between the latent variables tested. Identical correlations were present in both models in 22 cases. After testing the hypotheses, critical variables (nature of work, normative commitment, benefits, co-workers and organisational commitment) were identified that determine employees’ organisational commitment and intention to leave, regardless of the size of the organisation.

As a result of this research, the models developed are suitable for identifying differences in organisational staffing levels, but there is as yet no empirical evidence on the use of the scales for homogeneous groups of employees.

The results show that employees’ normative commitment and organisational commitment are critical factors for retention. Of the satisfaction factors examined, the nature of work, benefits and co-workers have a significant impact on retention in organisations, so organisational retention measures should focus on improving satisfaction regarding these factors.

The readers of the journal would appreciate the work, which highlights the significance of employee psychology and retention for organisational success.

The study is based on primary data and, to the best of the authors’ knowledge, is one of the few studies that take a holistic approach to organisational staff retention in the context of the moderating effect of organisational size. This study contributes to a comprehensive understanding of the phenomenon of employee retention and in contrast to previous research, examines the combined effect of several factors.

This research finds out to what extent companies’ names are influenced by the place’s attributes versus the official branding policies of a place and, consequently, whether and how local companies “buy into” the place’s strategies put forth by official bodies.

This paper is both conceptual and empirical, as a mixed quantitative and interpretive approach is used. The companies’ names of a tourist industry (the tour and guiding companies) in three destinations are compared and pitched against the branding of these cities. The companies' names are classified into categories to ascertain whether they reflect (or diverge from) the official strategies. Finally, a conceptual model is developed to explain the findings: the strategic naming model (SNM).

Our main finding is that the overall business features of a place being stronger determinants to the naming strategies than tourist destination branding initiatives. The intrinsic features of a place seem thus to be “above” destination branding policies. The researched features account for different naming strategies, such as highly original names, flat names or non-strategic names.

As the work is based on a convenience sample, it cannot claim strong representativity. The fact that each of the three data sets was processed by a different researcher might bring up personal biases.

This work is a call for a more intensive use of naming strategies to the companies’ advantages, as naming is found out to be strategy used to a very low degree. Thanks to this research, companies will understand the different naming possibilities and be able to apply them to their strategies by choosing names which express “uniqueness” or “belonging”. Practitioners will also be aware of whether they are communicating towards the industry or towards the market.

No works were found that empirically pursue our research goals. Therefore, this research might be considered as a novelty. The proposed SNM model explains and relates the most usual company naming techniques, which were unrelated up to date.

The purpose of this study is to provide insights and guidance for practitioners in terms of ensuring rigorous ethical and moral conduct in artificial intelligence (AI) hiring and implementation.

The research employed two experimental designs and one pilot study to investigate the ethical and moral implications of different levels of AI implementation in the hospitality industry, the intersection of self-congruency and ethical considerations when AI replaces human service providers and the impact of psychological distance associated with AI on individuals' ethical and moral considerations. These research methods included surveys and experimental manipulations to gather and analyze relevant data.

Findings provide valuable insights into the ethical and moral dimensions of AI implementation, the influence of self-congruency on ethical considerations and the role of psychological distance in individuals’ ethical evaluations. They contribute to the development of guidelines and practices for the responsible and ethical implementation of AI in various industries, including the hospitality sector.

The study highlights the importance of exercising rigorous ethical-moral AI hiring and implementation practices to ensure AI principles and enforcement operations in the restaurant industry. It provides practitioners with useful insights into how AI-robotization can improve ethical and moral standards.

The study contributes to the literature by providing insights into the ethical and moral implications of AI service robots in the hospitality industry. Additionally, the study explores the relationship between psychological distance and acceptance of AI-intervened service, which has not been extensively studied in the literature.

The operations and viability of microfinance institutions (MFIs), crucial for socioeconomic development and poverty reduction, heavily rely on the multilevel relationships among borrowers, loan officers and MFIs. This study examines the relationship between interpersonal and firm-level relationship quality (RQ) and their simultaneous impact on customer loyalty (CL) in microfinance. Additionally, it investigates the mediating effect of firm-level RQ between CL and interpersonal RQ.

In this study, correlational research methods were employed. Completed questionnaires were received from 498 MFI borrowers in Dar es Salaam and Mwanza cities. Regression techniques and structural equation modeling were utilized to analyze the data. Before hypothesis testing, the validity and reliability of the measurements were confirmed.

Interpersonal-level and firm-level RQs are significantly related. Interpersonal-level RQ and its dimensions are significantly linked to CL, whereas firm-level RQ and its dimensions are insignificantly related to CL, except for commitment. Interpersonal-level relationships have a stronger impact on CL than firm-level relationships. Among all the dimensions of RQ, commitment has the greatest influence on CL at both levels. Firm-level RQ negatively and insignificantly mediates the relation between interpersonal-level RQ and CL.

The study findings only apply to Tanzania's microfinance industry, because the interactions between and the relative effects of firm and interpersonal ties may vary across various contexts and cultures. Future research may consider replicating this study in other contexts and cultures to confirm these findings.

This study advances the understanding of how multilevel relationships affect CL within the microfinance industry. This insight will assist MFIs and policymakers in identifying alternative and more efficient relational strategies to enhance CL, a critical element for the sustainability of MFIs. In turn, the sustainability of MFIs in low-income countries like Tanzania holds paramount importance for stimulating socioeconomic development and, hence, achieving the goal of poverty eradication.

While previous studies on multilevel relationships concentrated on a single relational dimension (trust) and were conducted within the realms of retail, airline and industrial manufacturing, the current study employs the three most popular relational dimensions: trust, commitment and satisfaction, within the microfinance context. Additionally, this study investigates the mediation effect of firm-level RQ between interpersonal-level RQ and CL, a previously unexplored area in research.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.