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This chapter explores how perceived product innovation influences brand loyalty among UK Apple users, extending the technology acceptance model. Using a quantitative approach, data were collected from 101 respondents via an online survey and analysed using SPSS. Objectives included examining the mediating role of perceived product usefulness and ease of use, exploring the relationship between product/brand attitudes and brand loyalty and assessing gender's impact on loyalty perceptions. Findings reveal a significant positive relationship among variables, with perceived product usefulness and ease of use jointly mediating the link between product innovation and attitude, affecting brand loyalty. Gender was found to have no moderating effect. The research contributes by highlighting these mediating effects and deepening understanding of product innovation's impact on loyalty. Managerially, prioritising innovation, usefulness and ease of use can enhance satisfaction and loyalty. Future research could explore additional variables to enrich understanding further.

The primary purpose of this study is to explore the effects of demographic transition toward aging populations on the performance of stock market indices across various economic developments. The research aims to provide valuable insights into the life-cycle hypothesis on savings patterns, investment behavior and the potential reverberations on global financial markets.

The study adopts a comprehensive global perspective, scrutinizing the effects of aging populations on stock market indices across developed, developing and transitional economies through the panel data analysis. Using annual data spanning the period from 1991 to 2020, encompassing a sample of 10 countries from each economic development level, the study employs the panel autoregressive distributed lag (ARDL) model with fixed effect estimation.

The findings unveil a statistically significant positive impact of the elderly population proportion on global stock market indices. However, the magnitude and contours of this impact exhibit considerable heterogeneity across different country groups. Specifically, the study finds that while the aging population significantly influences stock market performance in developed nations, its effect is overshadowed by other economic factors, such as consumer price indices and interest rates, in developing countries and economies in transition.

The originality and value of this study lie in its comprehensive global perspective, which encompasses a diverse array of economies at varying developmental stages. The research contributes to an understanding of the effects of demographic transitions on stock market performance on a global scale. The insights derived from this study hold significant implications for policymakers, financial institutions and investors seeking to navigate the challenges and opportunities posed by aging societies in an increasingly interconnected global economy. Additionally, the findings highlight the need for specific strategies and policies that account for the unique economic characteristics and developmental stages of different nations.

While oft-ignored, grievances remain a central part of revolutions. We argue that the theorization of grievances requires conceptually unpacking specific complaints and relating them to mobilizing mechanisms. We thus focus on one set of grievances – corruption – that is especially prevalent in 21st century revolutionary episodes. Drawing on prior conceptualizations of corruption, we hypothesize that four different configurations of corruption influence five different mechanisms of contention. First, everyday street-level corruption creates the potential for sudden and spontaneous protest and creates the basis for widespread, coalitional mobilization. Second, institutional corruption focuses attention on the regime to make it a target of revolutionary claims. Third, competition among elites creates the potential for cross-class alliances but may forestall durable sociopolitical change and, in some cases, even allow for authoritarian consolidation of power through anti-corruption drives. We illustrate these dynamics through one clearly successful case of revolution in Tunisia in 2011, one case of mixed results from political revolution in Ukraine from 2004 to 2014, and a negative case of revolution in China since 2013.

To examine the employer brand attributes that attract students to various organisations. This paper studies the perceived importance levels of students in the context of employer brand dimensions.

This paper is based on a well-administered and structured questionnaire with survey results to test the effectiveness of the employer brand model. The researcher received a convenience sample of 416 filled questionnaires out of the 550 distributed questionnaires. Independent sample and paired sample t-tests were run to test the hypotheses.

Development value emerged as the most potent factor, whereas interest value emerged as the least favoured characteristic of an employer brand. Further analyses revealed no significant differences regarding the perceived levels of importance for dimensions of employer brand in relation to gender. Distinct significance levels are associated with various dimensions of employer brand, so companies should carefully emphasise and facilitate those dimensions. The managers could design their job advertisements to attract skilled employees based on the essential values depicted in this study.

The study contributes valuable suggestions for organisations to formulate an effective employer brand for successful recruitment strategies.

The purpose of this paper is to develop a reliable model that would predict the compressive strength of slurry infiltrated fiber concrete (SIFCON) modified with various supplementary cementitious materials (SCMs) using artificial intelligence approach.

This study engaged the artificial intelligence to predict the compressive strength of SIFCON through deep neural networks (DNN), artificial neural networks, linear regression, regression trees, support vector machine, ensemble trees, Gaussian process regression and neural networks (NN). A thorough data set of 387 samples was gathered from relevant studies. Eleven variables (cement, silica fume, fly ash, metakaolin, steel slag, fine aggregates, steel fiber fraction, steel fiber aspect ratio, superplasticizer, water to binder ratio and curing ages) were taken as input to predict the output (compressive strength). The accuracy and reliability of the developed models were assessed using a variety of performance metrics.

The results showed that the DNN (11-20-20-20-1) predicted the compressive strength of SIFCON better than the other algorithms with R² and mean square error yielding 95.89% and 8.07. The sensitivity analysis revealed that steel fiber, cement, silica fume, steel fiber aspect ratio and superplasticizer are the most vital variables in estimating the compressive strength of SIFCON. Steel fiber contributed the highest value to the SIFCON’s compressive strength with 16.90% impact.

This is a novel technique in predicting the compressive strength of SIFCON optimized with different SCMs using supervised learning algorithms, improving its quality and performance.

Consumer well-being (CWB) is a flourishing area of research. It is an important field of study for the Sustainable Development Goals (SDGs), particularly SDG-3 (Good Health and Well-Being). However, despite some recent reviews, there is a lack of a comprehensive overview of the broad themes emerging in the CWB literature. The study aims to thoroughly integrate and organize the fragmented existing literature on CWB by uncovering its emergent themes and their impact.

To address this gap, this study presents a systematic literature review (SLR) of 166 peer-reviewed journal articles from 2013–2023 following the Scientific Procedures and Rationales for Systematic Literature Reviews protocol from the Scopus and Web of Science databases.

This study’s SLR uncovered seven themes: subjective well-being; psychological well-being; food well-being; financial well-being; environmental well-being; social well-being; and societal well-being. Further, this study identifies that these themes impact consumers on three levels: micro, meso and macro. Thus, this study provides a comprehensive overview of the emergent themes along the levels of impact.

This paper anticipates that the study, which is a thorough overview of the CWB literature, will provide managers, academics and students with an introduction to the topic.

To the best of the authors’ knowledge, this is one of the first articles that shows the themes of the CWB literature along the respective levels of impact and draws avenues for future research.

El bienestar del consumidor (CWB) es un área de investigación en auge. Es un campo de estudio clave para los Objetivos de Desarrollo Sostenible (ODS), en particular el ODS-3 (Salud y Bienestar). Sin embargo, a pesar de algunas revisiones recientes, falta una visión general completa de los amplios temas emergentes en la literatura de CWB. El objetivo de este estudio es integrar y organizar exhaustivamente la literatura existente sobre CWB, revelando sus temas emergentes y su impacto.

Para abordar esta brecha, el estudio presenta una revisión sistemática de la literatura (SLR) de 166 artículos de revistas revisados por pares, publicados entre 2013 y 2023, siguiendo el protocolo SPAR-4-SLR, extraídos de las bases de datos Scopus y Web of Science.

Nuestra SLR reveló siete temas: (a) bienestar subjetivo; (b) bienestar psicológico; (c) bienestar alimentario; (d) bienestar financiero; (e) bienestar ambiental; (f) bienestar social; y (g) bienestar societal. Además, identificamos que estos temas afectan a los consumidores en tres niveles: (i) micro, (ii) meso y (iii) macro. Así, este estudio proporciona una visión completa de los temas emergentes a lo largo de los niveles de impacto.

Anticipamos que este estudio, que ofrece una revisión exhaustiva de la literatura de CWB, proporcionará a gestores, académicos y estudiantes una introducción al tema.

Según nuestro conocimiento, este es uno de los primeros artículos que muestra los temas de la literatura de CWB junto con sus respectivos niveles de impacto y traza rutas para futuras investigaciones.

消费者幸福感(CWB)是一个蓬勃发展的研究领域, 对可持续发展目标(SDGs), 尤其是 SDG-3(良好健康与幸福感), 具有重要意义。然而, 尽管近期已有一些综述, CWB 文献中出现的广 泛主题仍缺乏全面概述。本研究旨在深入整合和组织现有 CWB 文献, 通过揭示其新兴主题及其影响, 填补这一空白。

为解决这一问题, 本研究按照 SPAR-4-SLR 协议, 从Scopus和Web of Science数据 库中选取了 2013 年至 2023 年的 166 篇经过同行评审的期刊文章, 进行系统文献综述(SLR)。

我们的文献综述揭示了七个主题:(a)主观幸福感; (b)心理幸福感; (c)食品幸福感; (d)金融幸福感; (e)环境幸福感; (f)社会幸福感; (g)社会整体幸福感。此外, 我们发现这些 主题在三个层面上影响消费者:(i)微观层面; (ii)中观层面; (iii)宏观层面。因此, 本研究提供 了沿影响层面的新兴主题的全面概述。

我们期待这篇全面概述 CWB 文献的研究能为管理者、学者和学生提供关于该主题的全 面介绍。

据我们所知, 这是首篇阐释 CWB 文献主题及其相应影响层次的文章之一, 并为未来研究指 明了方向。

This study aims to explore the relationship between employer branding and talent retention, also examining the mediating role of organizational advocacy. Additionally, it investigates the moderating effect of supervisor support and coworker support in the connection between organizational advocacy and talent retention.

To achieve the objectives, a cross-sectional survey with convenience sampling was used to collect data from employees in the IT sector. PLS-SEM modeling was then employed to conduct the analysis.

The study revealed that dimensions of employer branding positively influenced organizational advocacy, supporting talent retention. Moreover, the study observed a significant moderating effect of supervisor support and coworker support concerning organizational advocacy and talent retention.

This study offers valuable insights for a wide range of IT professionals. The results emphasize the significance of employer branding in shaping organizational advocacy and talent retention. Additionally, the supportive attitude of supervisors and coworkers plays a crucial role in retaining talented employees within an organization. The study validates the connections between employer branding dimensions and key variables, aiding in the refinement of existing theories and the creation of new conceptual frameworks in organizational behavior and human resource management. These findings can help managers and practitioners improve employer branding to engage their workforce effectively.

The authors have advanced social exchange theory by studying the link between employer branding and talent retention. The paper highlights the uniqueness of their investigation, which explores how employer branding influences talent retention with a focus on organizational advocacy, influenced by the level of support from supervisors and coworkers. This study will be a valuable resource for future academia, revealing the significant impact of employer branding on organizational advocacy and talent retention.

This paper aims to examine desirable post-adoption outcomes related to food delivery apps using the involvement-commitment model (ICM) and the boundary of (Islamic) religiosity, which is an important facet of communities in many parts of the world today. Importantly, the study provides an in-depth understanding of the boundary role of religiosity in the links between involvement, commitment, resistance to negative information and advocacy intention in relation to food delivery app use.

Data collected from 498 respondents in Saudi Arabia was used to test the research hypothesized model.

The results from the partial least squares structural equation modeling technique lend credence to past research calling for the contextualization of theories, especially since this paper find religiosity to be an important boundary condition to the ICM in relation to food delivery apps in an Arab nation.

This paper focuses specifically on the ICM and the boundary of (Islamic) religiosity. The cardinal contribution of this study, therefore, lies in the contextualization of ICM within the Arab world concerning post-adoption behavior related to food delivery apps. To the best of the authors’ knowledge, this is likely the first study to do so in the marketing, hospitality and technology-based literatures.

By addressing the dearth of literature on the subject of cybersecurity risks and artificial intelligence (AI), this study aims to close a research gap by concentrating on the ever-changing environment of online social networks (OSNs) and technology. The main goals are to classify cyberattacks into categories like malware, phishing/spam and network intrusion detection; to identify efficient algorithms for preventing cyber threats; to review relevant literature from 2019 to 2020; and to use machine learning algorithms to detect suspicious behavior related to malware. The study offers a novel framework that suggests particular machine learning algorithms for every kind of cyber threat, hence improving cybersecurity knowledge and reaction capacities. This makes the research useful for examining the impact of cybersecurity on smart cities.

Thirty papers have been examined on AI and machine learning algorithms, including K-nearest-neighbor (KNN), convolutional neural networks (CNN) and Random Forest (RF), that were published in 2019 and 2020. Using analytical software (NVivo), a qualitative approach is used to retrieve pertinent data from the chosen research. The researchers divide cyberattacks into three groups: network intrusion detection, phishing/spam and malware.

The study’s conclusions center on how AI and machine learning algorithms linked to cybersecurity are reviewed in the literature, how cyberattacks are classified and how an inventive framework for identifying and reducing risks is proposed. This makes the research useful for researching the implications of cybersecurity for smart cities.

The practical implications of this research are noteworthy, particularly in the realms of technology, AI, machine learning and innovation. The utilization of the NVivo technique enhances decision-making in uncertain situations, making the study’s results more reliable. The findings showcase the applicability of tools in analyzing malicious cyberattacks to address issues related to social media attacks, emphasizing their practical utility. The study’s relevance is further highlighted by a real-world example, where a Kuwaiti public sector fell victim to a malware attack, underlining the importance of cybersecurity measures aligned with the New Kuwait 2035 strategic development plan. The innovative framework presented in the research guides the selection of algorithms for detecting specific malicious attacks, offering practical insights for securing information technology (IT) infrastructure in Kuwait.

The rapid digitization in Kuwait, accelerated by the COVID-19 pandemic, underscores the pivotal role of technology in government services. Ma’murov et al. (2023) emphasize the significance of digitization, particularly in accessing and verifying COVID-19 information. The call for a dedicated digital library for preserving pandemic-related material aligns with the evolving digital landscape. Cybersecurity emerges as a critical concern in Kuwait and the Gulf Cooperation Council (GCC), necessitating transnational cooperation (Nasser Alshabib and Tiago Martins, 2022). In the local context, the inefficiency of information security systems and low awareness among government employees pose cybersecurity challenges (Abdulkareem et al., 2014). Social media’s role during the pandemic highlights its significance, yet the need for cybersecurity in this domain remains underexplored (Ma’murov et al., 2023; Safi et al., 2023).

The unique aspect of the paper is its in-depth investigation of the relationship between cybersecurity and AI in OSNs. It uses a special application of machine learning methods, including CNN, RF and KNN, to identify suspicious behavior patterns linked to malware. The detailed analysis of 30 research papers released between 2019 and 2020, which informs the choice of suitable algorithms for diverse cyber threats, further emphasizes the study’s uniqueness. The novel framework that has been suggested categorizes assaults and suggests certain machine learning techniques for identification, offering a useful instrument to improve comprehension and reactions to a variety of cybersecurity issues.

Climate change has led to a rise in the frequency, intensity and scope of droughts, posing significant implications for businesses. This study examines the impact of local community drought levels on audit pricing. Additionally, it explores the moderating effects of high-tech industries, auditor busyness and the level of local community concern regarding the drought crisis.

This study employs a mixed-methods approach to rigorously test the research hypotheses. The quantitative phase of the study utilizes a sample of 1,278 firm-year observations from Iran’s capital market. For the analysis of the quantitative data, ordinary least squares regression with clustered robust standard errors is used. Additionally, this research supplements its quantitative findings with qualitative evidence obtained through semi-structured interviews with 19 Iranian audit partners.

The results suggest that firms operating in provinces facing severe droughts experience notably higher audit fees. Furthermore, the positive relationship between drought and audit fees is weakened when auditors are busy, local community concern regarding the drought crisis is high or the firm operates within high-tech industries. These findings are supported by a range of robustness checks and qualitative evidence gathered from the field.

This research contributes to the growing literature on climate change by examining the influence of local community drought levels on audit pricing within an Iranian context. Additionally, our study sheds light on how high-tech industries, auditor workload and the level of local community concern regarding the drought crisis moderate the relationship between drought and audit fees. Importantly, our study pioneers in providing mixed-methods evidence of the association between drought severity and audit fees.