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The concept of information security culture, which recently gained increased attention, aims to comprehensively grasp socio-cultural mechanisms that have an impact on organizational security. Different measurement instruments have been developed to measure and assess information security culture using survey-based tools. However, the content, breadth and face validity of these scales vary greatly. This study aims to identify and provide an overview of the scales that are used to measure information security culture and to evaluate the rigor of reported scale development and validation procedures.

Papers that introduce a new or adapt an existing scale of information security culture were systematically reviewed to evaluate scales of information security culture. A standard search strategy was applied to identify 19 relevant scales, which were evaluated based on the framework of 16 criteria pertaining to the rigor of reported operationalization and the reported validity and reliability of the identified scales.

The results show that the rigor with which scales of information security culture are validated varies greatly and that none of the scales meet all the evaluation criteria. Moreover, most of the studies provide somewhat limited evidence of the validation of scales, indicating room for further improvement. Particularly, critical issues seem to be the lack of evidence regarding discriminant and criterion validity and incomplete documentation of the operationalization process.

Researchers focusing on the human factor in information security need to reach a certain level of agreement on the essential elements of the concept of information security culture. Future studies need to build on existing scales, address their limitations and gain further evidence regarding the validity of scales of information security culture. Further research should also investigate the quality of definitions and make expert assessments of the content fit between concepts and items.

Organizations that aim to assess the level of information security culture among employees can use the results of this systematic review to support the selection of an adequate measurement scale. However, caution is needed for scales that provide limited evidence of validation.

This is the first study that offers a critical evaluation of existing scales of information security culture. The results have decision-making value for researchers who intend to conduct survey-based examinations of information security culture.

This paper aims to present the fundamental aspects for the development and validation (D&V) of attitudes’ measurement scale, as well as its practical aspects that are not deeply explored in books and manuals. These aspects are the results of a long experience of the authors and arduous learning with errors and mistakes.

The nature of this paper is methodological and can be very useful for an initial reading on the theme that it rests. This paper presents four D&V stages: literature review or interviews with experts; theoretical or face validation; semantic validation or validation with possible respondents; and statistical validation.

This is a methodological paper, and its main finding is the usefulness for researchers.

The main implication of this paper is to support researchers on the process of D&V of measurement scales.

Became a step-by-step guide to researchers on the D&V of measurement scales.

Support researchers on their data collection and analysis.

This is a practical guide, with tips from seasoned scholars to help researchers on the D&V of measurement scales.

This study aims to provide a measurement model, and the underlying constructs and items, for Logistics 4.0 in manufacturing companies. Industry 4.0 technology for logistics processes has been termed Logistics 4.0. Logistics 4.0 and its elements have seen varied conceptualizations in the literature. The literature has mainly focused on conceptual and theoretical studies, which supports the notion that Logistics 4.0 is a relatively young area of research. Refinement of constructs and building consensus perspectives and definitions is necessary for practical and theoretical advances in this area.

Based on a detailed literature review and practitioner focus group interviews, items of Logistics 4.0 for manufacturing enterprises were further validated by using a large-scale survey with practicing experts from organizations located in Central Europe, the Northeastern United States of America and Northern Thailand. Exploratory and confirmatory factor analyses were used to define a measurement model for Logistics 4.0.

Based on 239 responses the exploratory and confirmatory factor analyses resulted in nine items and three factors for the final Logistics 4.0 measurement model. It combines “the leveraging of increased organizational capabilities” (factor 1) with “the rise of interconnection and material flow transparency” (factor 2) and “the setting up of autonomization in logistics processes” (factor 3).

Practitioners can use the proposed measurement model to assess their current level of maturity regarding the implementation of Logistics 4.0 practices. They can map the current state and derive appropriate implementation plans as well as benchmark against best practices across or between industries based on these metrics.

Logistics 4.0 is a relatively young research area, which necessitates greater development through empirical validation. To the best of the authors knowledge, an empirically validated multidimensional construct to measure Logistics 4.0 in manufacturing companies does not exist.

Opportunity recognition and opportunity exploitation are two central concepts in the entrepreneurial process. However, there is a lack of both a clear specification of the content domains of the constructs and valid and reliable multi-item scales for their measurement. The paper aims to discuss these issues.

This paper first reveals existing issues around the definitions and measures relating to the concepts, then defines their content domains, and also proposes scale items to measure the concepts. Four samples are used to develop the measurement instruments.

Two scales are suggested, one to measure opportunity recognition, and other to measure opportunity exploitation. The scales demonstrate reliability and construct, discriminant, and nomological validity.

The resulting instruments provide tools for research and practice that could prove valuable when examining the antecedents and consequences of both opportunity recognition and opportunity exploitation.

The subject of “well-being” has attracted attention from tourism scholars, but differences and misuses in approach have meant that academic contributions and knowledge accumulation to the tourism literature remain relatively little. This paper attempts to clarify the theoretical source of subjective well-being, and critically reflect on the problems existing in the study of well-being when applied to tourism. It is suggested that subjective well-being belongs to the category of “quality of life” and has multiple philosophical foundations and theoretical sources including theories of hedonism, expectation, happiness and various itemised lists of emotions. A hybrid research method is suggested when applying the concept to tourism.

This study evaluates a multidimensional hierarchical scale of smart policing service quality.

Qualitative and quantitative analysis tools were used to develop a smart policing service quality scale based on the integrative psychometric scale development methodology. A multidimensional hierarchical structure was proposed for smart policing service quality; a group of preliminary items selected from literature was used for the qualitative analysis. For data collection, users of smart policing services were selected through the United Arab Emirates (UAE) research centre. Several statistical methods were employed to verify reliability and validity of the construct and nomological validity of the proposed scale.

A smart policing service quality scale of 23 items was developed based on a hierarchical factor model structure. Nomological testing indicated that overall smart policing service quality is positive and significant, thus contributing to user satisfaction, intention to continue using the system and enhanced quality of life.

This study enables managers to evaluate types of policing quality and effectively implement strategies to address security and sustainability issues that exist currently in smart services.

Previous studies on policing service quality have not sufficiently addressed the role of smart policing service quality; the nature of discussion in this area is primarily based around concepts. The development of the smart policing service quality scale provides a measurement tool for researchers to use to enhance the understanding of smart policing service quality.

Cybersecurity attacks on critical infrastructures, businesses and nations are rising and have reached the interest of mainstream media and the public’s consciousness. Despite this increased awareness, humans are still considered the weakest link in the defense against an unknown attacker. Whatever the reason, naïve-, unintentional- or intentional behavior of a member of an organization, the result of an incident can have a considerable impact. A security policy with guidelines for best practices and rules should guide the behavior of the organization’s members. However, this is often not the case. This paper aims to provide answers to how cybersecurity-related behavior is assessed.

Research questions were formulated, and a systematic literature review (SLR) was performed by following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. The SLR initially identified 2,153 articles, and the paper reviews and reports on 26 articles.

The assessment of cybersecurity-related behavior can be classified into three components, namely, data collection, measurement scale and analysis. The findings show that subjective measurements from self-assessment questionnaires are the most frequently used method. Measurement scales are often composed based on existing literature and adapted by the researchers. Partial least square analysis is the most frequently used analysis technique. Even though useful insight and noteworthy findings regarding possible differences between manager and employee behavior have appeared in some publications, conclusive answers to whether such differences exist cannot be drawn.

Research gaps have been identified, that indicate areas of interest for future work. These include the development and employment of methods for reducing subjectivity in the assessment of cybersecurity-related behavior.

To the best of the authors’ knowledge, this is the first SLR on how cybersecurity-related behavior can be assessed. The SLR analyzes relevant publications and identifies current practices as well as their shortcomings, and outlines gaps that future research may bridge.

This study aims to explore which of four chosen factors (i.e. privacy concerns, FoMO, self-disclosure and time cost) induce a feeling of strain among Facebook users in terms of social media fatigue (SMF), and if this occurs, whether it further influences such outcomes as discontinuance of usage (DoU) and interaction engagement decrement (IED).

Through an online structured questionnaire, empirical data were gathered to verify the research model, based on the stressor-strain-outcome (SSO) framework. The SEM technique was employed for assessing the hypothesized relationships.

The findings show that privacy concerns and time cost are strong antecedents of SMF and contribute significantly to its occurrence; while FoMO and self-disclosure do not exhibit any significant influence. Moreover, SMF positively and significantly affects DoU and IED.

This study enhances the existing body of knowledge on SMF and it can help: (1) individuals to be aware of risks and adjust their activities in balance with their well-being, and (2) social media (SM) managers to develop unique strategies to address the specific needs of SM users.

This research contributes to the limited literature on SMF by (1) introducing the concept of IED – as a consequence of SMF, and (2) creating measurement scales for IED.

Factor analysis is the most used tool in organizational research and its widespread use in scale validations contribute to decision-making in management. However, standard factor analysis is not always applied correctly mainly due to the misuse of ordinal data as interval data and the inadequacy of the former for classical factor analysis. The purpose of this paper is to present and apply the Bayesian factor analysis for mixed data (BFAMD) in the context of empirical using the Bayesian paradigm for the construction of scales.

Ignoring the categorical nature of some variables often used in management studies, as the popular Likert scale, may result in a model with false accuracy and possibly biased estimates. To address this issue, Quinn (2004) proposed a Bayesian factor analysis model for mixed data, which is capable of modeling ordinal (qualitative measure) and continuous data (quantitative measure) jointly and allows the inclusion of qualitative information through prior distributions for the parameters’ model. This model, adopted here, presents considering advantages and allows the estimation of the posterior distribution for the latent variables estimated, making the process of inference easier.

The results show that BFAMD is an effective approach for scale validation in management studies making both exploratory and confirmatory analyses possible for the estimated factors and also allowing the analysts to insert a priori information regardless of the sample size, either by using the credible intervals for Factor Loadings or by conducting specific hypotheses tests. The flexibility of the Bayesian approach presented is counterbalanced by the fact that the main estimates used in factor analysis as uniqueness and communalities commonly lose their usual interpretation due to the choice of using prior distributions.

Considering that the development of scales through factor analysis aims to contribute to appropriate decision-making in management and the increasing misuse of ordinal scales as interval in organizational studies, this proposal seems to be effective for mixed data analyses. The findings found here are not intended to be conclusive or limiting but offer a useful starting point from which further theoretical and empirical research of Bayesian factor analysis can be built.