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This study aims to explore the relationship between the objectives of Islamic banking, customer satisfaction and customer loyalty in the South African context. Diving deep, this study also explores the relationship between customer satisfaction and customer loyalty.

Purposive and snowball sampling techniques were used, resulting in 163 respondents participating in this study. The data was collected using an online survey and analysed using a structural equation model based on the partial least squares method.

The results indicate that the construct related to the objectives of Islamic banking influences both customer satisfaction and customer loyalty. In addition, customer satisfaction is also found to influence a customer’s loyalty to the Islamic bank.

In South Africa, to the best of the authors’ knowledge, this study is the first of its kind; thus, the results provide context-specific insights into the extant literature on Islamic banking for Muslims residing in a non-Muslim majority country.

Drug sales facilitated through digital communication on the surface web and on darknet cryptomarkets have increased during the past two decades. This has resulted in an increase in drug law enforcement efforts to combat these markets and a subsequent increase in judicial sentencing of people selling drugs online. The aim of this study was to analyze how Swedish courts describe sentenced sellers and how the courts apply case law.

The empirical material consists of 71 sentencing documents produced by Swedish courts in cases of online drug selling between January 1, 2010 and January 1, 2020. In total, 99 sentenced persons occur in the documents. Using a qualitative research design, the authors analyzed the material through thematic text analysis.

Overall, in their descriptions of online drug sale operations, the courts’ characterizations of the concepts of street capital and digital capital show a dichotomy. These forms of capital are situationally described as both aggravating and mitigating aspects in the application of case law, indicating that it may be fruitful to view both street and digital capital as resources used on contemporary drug markets in general.

Very little research exists into how judicial systems describe and perceive the developing phenomenon of online drug sales. Using a relatively large sample from a decade of sentencing, the authors provide an analysis of how Swedish courts view and valuate capital forms in the online drugs trade.

Patronage of local footwear have not been encouraging in Nigeria despite recent investments. The purpose of the study is to evaluate celebrity endorsement and customer patronage of small and medium-scale enterprises (SMEs) products in African context, with focus on trustworthiness, expertise, attractiveness, respect and similarity (TEARS) model.

The research was designed as a descriptive survey. An online structured questionnaire was applied for data collection. Cronbach Alpha and content validity were used for reliability and validity, respectively. TEARS model was used to ascertain key dimensions, and Pearson correlation coefficient and logistic regression were applied into the analysis.

The findings reveal that celebrity endorsement is not associated with patronage of local footwears, though TEARS model analysis indicates the direction of consumers rating on celebrity endorsement. Factors such as recommendation and quality impact the consumer willingness to buy local footwear.

The small sample size calls for caution in generalization.

The study suggests that although the TEARs model is viable, all the dimensions are mutually exclusive. However, this depends on the characteristics of the brand. In driving patronage, managers must pay attention to personal and non-personal cues such as price, quality and source of information about their brand.

The originality is buttressed from the value it provides for local product production and patronage. The significant factors are indicated as key to addressing low patronage.

This exploratory study examines and comprehends the relative importance of mobile app attributes from a consumer perspective. Both quantitative and qualitative analysis approaches explore users' behavior and attitudes toward the priorities of mobile app attributes and preferences, identifying correlations between attributes and aggregating individual attributes into groups.

Online convenience sampling and snowball sampling resulted in 417 valid responses. The numerical data are analyzed using the relative to an identified distribution (RIDIT) scoring system and gray relational analysis (GRA), and qualitative responses are investigated using text-mining techniques.

This study finds enhanced nuances of user preferences and provides data-driven insights that might help app developers and marketers create a distinct app that will add value to consumers. The latent semantic analysis indicates relationship structure among the attributes, and text-based cluster analysis determines the subsets of attributes that represent the unique functions of the mobile app.

This study reveals the essential components of mobile apps, paying particular attention to the consumer value component, which boosts user approval and encourages prolonged use. Overall, the results demonstrate that developers must concentrate on its functional, technical and esthetic features to make an app more exciting and practical for potential users.

Most scholarly research on apps has focused on their technological merits, aesthetics and usability from the user's perspective. A post-adoption multi-attribute app analysis using both structured and unstructured data is conducted in this study.

The study employs two independent experimental studies to collect data. It focuses on the matching effect between advertising appeals and product types. The Elaboration Likelihood Model serves as the theoretical framework for understanding the cognitive processing involved in consumers' responses to these advertising appeals and product combinations.

This paper aims to investigate the impact of advertising appeals on consumers' intentions to purchase organic food. We explored the interaction between advertising appeals (egoistic vs altruistic) and product types (virtue vs vice) and purchase intention. The goal is to provide insights that can enhance the advertising effectiveness of organic food manufacturers and retailers.

The analysis reveals significant effects on consumers' purchase intentions based on the matching of advertising appeals with product types. Specifically, when egoistic appeals align with virtuous products, there is an improvement in consumers' purchase intentions. When altruistic appeals match vice products, a positive impact on purchase intention is observed. The results suggest that the matching of advertising appeals with product types enhances processing fluency, contributing to increased purchase intention.

This research contributes to the field by providing nuanced insights into the interplay between advertising appeals and product types within the context of organic food. The findings highlight the importance of considering the synergy between egoistic appeals and virtuous products, as well as altruistic appeals and vice products. This understanding can be strategically employed by organic food manufacturers and retailers to optimize their advertising strategies, thereby improving their overall effectiveness in influencing consumers' purchase intentions.

Many organisations monitor statistics on the background of job applicants to inform diversity management, a practice known as equality monitoring (EM). The study examines perceptions of EM and employers that use it. Additionally, it aims to assess potentially salient group differences in attitudes towards EM, focussing on perceived history of employment discrimination, ethnicity, sex, and a comparison between the UK and Sweden – two countries which differ extensively in EM prevalence.

A cross-sectional self-report survey assessed attitudes toward EM, attraction to employers using it, pro-equality and diversity attitudes, perceived history of employment discrimination and background characteristics (e.g. ethnicity and sex), and compared a UK and Sweden sample (N = 925).

The results reveal positive perceptions of EM overall. Although no differences were observed between UK ethnic majority and minority respondents, White British men rate employers using EM as less attractive with increasing levels of perceived past discrimination. Women have more positive perceptions than men. Finally, the UK sample rated EM more positively than the Sweden sample.

Despite EM being widespread, the study is the first to investigate detailed perceptions of it, making group and country comparisons. Results support the use of EM in HRM but highlight the need for clear communication to avoid confusion with positive discrimination, which is perceived negatively in some majority group members, and to allay fears of data misuse. Recommendations are made for future implementation.

Reactance theory is applied to investigate consumer responses to “buy local” campaigns initiated by government to counteract the effects of an economic crisis, using the COVID-19 pandemic as an illustrative context.

A conceptual model is developed, aimed at revealing the extent to which “buy local” campaigns – explicitly justified by the need to fight an economic crisis – are likely to lead to (a) compliance (i.e. support for local products/retailers) or (b) freedom restoration (i.e. support for foreign products/retailers). The model is subsequently tested on samples of German (N = 265) and Italian (N = 268) consumers.

“Buy local” campaigns are likely to generate reactance amongst consumers and such reactance can lead to both non-compliance and, albeit less so, freedom restoration outcomes. At the same time, consumer ethnocentrism acts as a countervailing influence by attenuating the effects of generated reactance and its undesirable outcomes.

Psychological reactance theory offers a novel perspective for conceptually approaching the likely responses of consumers towards “buy local” campaigns and the empirical findings support the use of the theory in this context.

Policymakers seeking to encourage consumers to support the local economy during times of an economic crisis need to be aware that “buy local” campaigns may, against their intended communication goals, result in non-compliance as well as consumer responses in the opposite direction. Thus, the reactance-generating potential of such campaigns needs to be explicitly considered at the planning/implementation stage.

The findings confirm the relevance of reactance theory as a conceptual lens for studying the effects of “buy local” campaigns and have important implications for domestic/foreign firms as well as for policy makers seeking to encourage consumers to support the local economy during times of an economic crisis.

This paper aims to review the challenges of performance appraisal in organizations and argue that these challenges can and must be overcome.

The authors review research on performance appraisal in organizations and on claims that organizations are abandoning performance appraisal.

Structured performance appraisals are still the norm in organizations around the world. There are clear and practical strategies for improving appraisals. These include improving feedback and removing unnecessary complexity, clarifying the goals of appraisal systems, focusing appraisal on behaviors and outcomes under the employee's control and increasing the fairness of appraisal systems.

Research is needed on the effects of changing the ways performance appraisals are conducted in organizations.

Practical strategies for improving performance appraisal are outlined.

Better performance appraisals will benefit organizations and their members.

This paper refutes the growing claim that organizations are abandoning performance appraisal and illustrates practical strategies for improving performance appraisal.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.

This study aims to explore the impact of controversial firms’ corporate sustainability assessments on their risk exposure according to the environmental, social and governance (ESG) paradigm.

This study conducts a cross-sectional study using the ordinary least squares approach to test how corporate social responsibility practices affect firms’ risk exposure, testing the three single impacts of ESG components and the impact of an overall ESG assessment. This study considers the largest Standard & Poor’s (S&P) 500 stock market index companies and focus on a double-risk measurement – systematic and idiosyncratic – developing an empirical study on 132 controversial companies listed on the S&P index.

Empirical findings indicate that the overall ESG assessment and the environmental and social sub-dimensions decrease idiosyncratic firm risk. At the same time, no significant results are found according to the systematic risk component.

This study fits into the domain of risk management research, investigating whether additional and non-financial disclosures regarding sustainability issues decrease information asymmetries, improving investors’ decision-making and stakeholders’ relations. Prior literature has shown limited evidence on the relationship between corporate social performance (CSP) and firm risk based on controversial companies. The main contribution is to consider the controversy as an independent factor from the industry sector, given that the implications of CSP actions and practices are mainly firm-specific.