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Authenticity has been studied from a variety of disciplinary perspectives, leading to a rich but confused literature. This study, a review, aims to compare the psychology and sociology/tourism definitions of authenticity to clarify the concept. From a psychological perspective, authenticity is a mental appraisal of an object or experience as valued leading to feelings and summative judgements (such as satisfaction or perceived value). In objective authenticity, a person values the object due to belief in an expert’s opinion, constructive authenticity relies on socially constructed values, while existential authenticity is based on one’s self-identity. The resultant achievement of a valued goal, such as seeing a valued object, leads to feelings of pleasure. Sociological definitions are similar but based on different theoretical antecedent causes of constructed and existential authenticity. The paper further discusses the use of theory in tourism and the project to develop tourism as a discipline. This project is considered unlikely to be successful and in turn, as argued, it is more useful to apply theory from other disciplines in a multidisciplinary manner. The results emphasise that it is necessary for tourism researchers to understand the origins and development of the concepts they use and their various definitions.

To explore value vs worth in the context of compulsory acquisition.

Analysis of statutory environment within the context of valuation theory.

Value and worth could be reconciled by redefining special value in Act.

Public policy amendment.

Public policy amendment.

Facilitate just compensation.

Topical issue in New South Wales, where massive compulsory acquisition programme underway to facilitate infrastructure development.

This paper aims to analyse how financial literacy (FL) is conceptualised and operationalised in the Indian context.

A systematic literature review (SLR) was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) protocol. Thirty-six articles published between 2010 and 2020 were considered for analysis. The FL conceptualisation was examined based on knowledge, ability, skill, attitude and confidence elements. The FL operationalisation was analysed using the modified version of the Organisation for Economic Co-operation and Development’s (OECD) Programme for International Student Assessment (PISA) 2012 model for organising the domain for an assessment framework.

The findings indicate that, despite offering operationalisation details of the FL, 13 out of 36 studies did not include a conceptual definition of FL. Of the 23 studies that mentioned a conceptual definition, 87% are primarily focused on the “knowledge” element and only 39% have combined knowledge, ability/skill and attitude elements in defining FL. As in the developed countries, the Indian studies also preferred investment/saving-related contents in their FL measures. The volume of content focusing on the financial landscape is meagre amongst the FL measures used in India and developed countries. The survey instruments of most studies have been designed in the individuals’ context but have failed to measure the extent to which individuals apply the knowledge in performing their day-to-day financial transactions. Further, it was found that 20 out of 36 studies did not convert the FL level of their target groups into a single indicator or operational value.

To the best of our knowledge, this is the first study that explores the FL’s assessment practices in India. Further, this study offers new insights by comparing the contents of FL measures used in Indian studies with those used in developed countries.

The purpose of this paper is to examine the nature of cyberbullying and corresponding strategies being used under the New United Arab Emirates (UAE) Cybercrimes Law.

The analysis begins with a review of the nature of cyberbullying, focusing primarily on key concepts, underlying risk factors, forms of cyberbullying and adverse effects. Background information about the nature of cyberbullying will then lay the foundation for the subsequent sections of the analysis, which will focus on preventive strategies and legislative measures. The second section of the analysis will entail a review of the legislative framework for cyberbullying in the UAE. The goal here is to examine how the UAE is responding to the emerging threat of cyberbullying in its jurisdiction. The next section will then shift gears to interventions and strategies being implemented at the global level. A global perspective is central to comparing practices in the UAE to international standards and regulations.

Findings from the analysis have shown that the UAE has the most robust and comprehensive cyberbullying laws internationally. Nonetheless, the New Cybercrimes Law is ambiguous, and it is not expressively specific to cyberbullying. The law does not have a clear definition of cyberbullying, as well as the scope of its application to specific cases involving students. A comparative analysis across jurisdictions has revealed that most countries neither have specific cyberbullying laws nor explicitly define the phenomenon in existing laws. Thus, cyberbullying is a gray area in the UAE national law, requiring a clear definition and scope of application. The courts will establish case law that will finally address the current definitional challenges and extend of applying the New Cybercrimes Law.

The analysis concludes with the application of international best standards and practices to the UAE context, focusing specifically on how to strengthen laws and procedures in the UAE.

Research on employee non-/compliance to information security policies suffers from inconsistent results and there is an ongoing discussion about the dominating survey research methodology and its potential effect on these results. This study aims to add to this discussion by investigating discrepancies between what the authors claim to measure (theoretical properties of variables) and what they actually measure (respondents’ interpretations of the operationalized variables). This study asks: How well do respondents’ interpretations of variables correspond to their theoretical definitions? What are the characteristics of any discrepancies between variable definitions and respondent interpretations?

This study is based on in-depth interviews with 17 respondents from the Swedish public sector to understand how they interpret questionnaire measurement items operationalizing the variables Perceived Severity from Protection Motivation Theory and Attitude from Theory of Planned Behavior.

The authors found that respondents’ interpretations in many cases differ substantially from the theoretical definitions. Overall, the authors found four principal ways in which respondents interpreted measurement items – referred to as property contextualization, extension, alteration and oscillation – each implying more or less (dis)alignment with the intended theoretical properties of the two variables examined.

The qualitative method used proved vital to better understand respondents’ interpretations which, in turn, is key for improving self-reporting measurement instruments. To the best of the authors’ knowledge, this study is a first step toward understanding how precise and uniform definitions of variables’ theoretical properties can be operationalized into effective measurement items.

The purpose of the paper is to afford a comprehensive conceptualization and operationalization of the construct of talent management through an inclusive exploration of conceptual clarifications for existing confusions while developing a complete measuring instrument.

The archival method was adopted together with a systematic review based on Khan et al.’s (2003) five steps of systematic literature review. The systematic review has encircled published research articles between 1982 and 2023 in the human resource management (HRM) arena. A total of 130 articles were initially scrutinized, and 106 were systematically reviewed to conceptualize, operationalize and explore clarifications for confusions and instrument development for talent management.

This study explored conceptual clarifications for existing confusions towards talent management while recognizing definitions that come under the main philosophical schools for the underlying concept of talent. A novel practical definition has been established for talent management while recognizing dimensions, and then certain elements. A comprehensive instrument has been developed to measure talent management.

This study is limited to instrument development in measuring talent management; nevertheless, there is an enormous scope for using the instrument to empirically measure talent management through organizational and employees perspectives linked to diverse global contexts in future studies.

The developed comprehensive instrument is a vibrant contribution to future investigations related to empirically measuring talent management associated with organizational and employee perspectives related to diverse global contexts in winning “war for talent.” This study endows a significant input to the whole frame of HRM knowledge as it resolves existing conceptual ambiguities towards talent management while defining and operationalizing it.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

In demand-driven markets, customer value, sometimes called perceived use value or consumer surplus, is defined by the customer rather than the firm. The value a firm can appropriate, its profits, is driven by the customer’s willingness to pay for the value they receive, adjusted by costs. This paper introduces a conceptual framework that helps understand value creation and appropriation in demand-driven markets and shows how to influence them through strategic decision-making.

This paper uses an axiomatic approach combined with an extended analytical formulation of the jobs-to-be-done framework to contextualise demand-driven markets. It mathematically derives implications for managerial decision-making concerning selecting customer segments, optimising customer value creation and maximising firm value appropriation in a competitive environment.

Rooting strategic decision-making in the jobs-to-be-done framework allows distinguishing between what customers want to achieve (goal), what product attributes need to be satisfied (opportunity space/constraints) and what value creation criteria related to features are important (utility function). This paper shows that starting from a job-to-be-done, the problem of identifying which customer segments to serve, what product to offer and what price to charge, can be formulated as an optimisation problem that simultaneously (rather than sequentially) solves for the three decision variables, customer segments, product features and price, by maximising the value that a firm can appropriate, subject to maximising customer value creation and constrained by the competitive environment.

Applying the derived results to simultaneously deciding which customer segments to target, what product features to offer and what price to charge, given a set of competing products, allows managers to increase their chances of winning the competitive game.

This paper shows that starting from a job-to-be-done and simultaneously focusing on customers, product features, price and competitors enhances firm profitability. Strategic decision-making is formulated as an optimisation problem based on an axiomatic approach contextualising demand-driven markets.

We attempt to construct a grey three-way conflict analysis model with constraints to deal with correlated conflict problems with uncertain information.

In order to address these correlated conflict problems with uncertain information, considering the interactive influence and mutual restraints among agents and portraying their attitudes toward the conflict issues, we utilize grey numbers and three-way decisions to propose a grey three-way conflict analysis model with constraints. Firstly, based on the collected information, we introduced grey theory, calculated the degree of conflict between agents and then analyzed the conflict alliance based on the three-way decision theory. Finally, we designed a feedback mechanism to identify key agents and key conflict issues. A case verifies the effectiveness and practicability of the proposed model.

The results show that the proposed model can portray their attitudes toward conflict issues and effectively extract conflict-related information.

By employing this approach, we can provide the answers to Deja’s fundamental questions regarding Pawlak’s conflict analysis: “what are the underlying causes of conflict?” and “how can a viable consensus strategy be identified?”

The paper aims to provide a critical review of how variations in the conceptualization and contextualization of hate crime across US cities might impact how their individual law enforcement agencies collect hate crime data. Media reports and political discourses present hate crime as a prevalent problem in the USA. However, this representation of hate crime in the public sphere is not reflected in the relatively low national numbers of hate crimes published annually by the Federal Bureau of Investigation.

Drawing primarily on the national hate crime data for the period 2008–2018, this author conducted a secondary research study of the concept, context, extent and law enforcement collection of hate crime data in five cities in the USA.

This paper is a product of some of the findings of the study, which include the definition of hate crime at the federal, state and city levels and the contextualization of hate crimes at these levels. The findings show inconsistencies in how the five cities and associated law enforcement agencies conceptualize hate crime and in how they collect and report hate crime data at local and national levels.

Through its analysis of how five US cities and the associated law enforcement agencies interpret and respond to hate crime data collection, with recommendations of best practices for hate crime data collection by law enforcement agencies, the paper contributes to the academic and nonacademic debate on hate crime.