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In the modern digital realm, while artificial intelligence (AI) technologies pave the way for unprecedented opportunities, they also give rise to intricate cybersecurity issues, including threats like deepfakes and unanticipated AI-induced risks. This study aims to address the insufficient exploration of AI cybersecurity awareness in the current literature.

Using in-depth surveys across varied sectors (N = 150), the authors analyzed the correlation between the absence of AI risk content in organizational cybersecurity awareness programs and its impact on employee awareness.

A significant AI-risk knowledge void was observed among users: despite frequent interaction with AI tools, a majority remain unaware of specialized AI threats. A pronounced knowledge difference existed between those that are trained in AI risks and those who are not, more apparent among non-technical personnel and sectors managing sensitive information.

This study paves the way for thorough research, allowing for refinement of awareness initiatives tailored to distinct industries.

It is imperative for organizations to emphasize AI risk training, especially among non-technical staff. Industries handling sensitive data should be at the forefront.

Ensuring employees are aware of AI-related threats can lead to a safer digital environment for both organizations and society at large, given the pervasive nature of AI in everyday life.

Unlike most of the papers about AI risks, the authors do not trust subjective data from second hand papers, but use objective authentic data from the authors’ own up-to-date anonymous survey.

The purpose of this study is to investigate the relationship between investments in quality and lean practices, and their impact on factory fitness. Using concepts originating in the theory of swift even flow, this study asserts that manufacturers, in order to improve their production swiftness and evenness, must leverage the potential synergetic effects between quality and lean practices.

This research uses data from the Global Manufacturing Research Group (GMRG) survey project (with data collected from 922 manufacturing plants, across 18 countries). The constructs and measurement model were assessed using confirmatory factor analysis (CFA) and the hypotheses were tested using ordinary least square (OLS) models.

This study highlights that both investments in quality and lean practices have direct impact factory fitness. The results provide insights into the efficacy of the investments in manufacturing practices and their role in augmenting the operational performance. The investments in quality practices were found to enhance the efficacy of investments in lean practices, which in turn impact the factory fitness.

From a practical perspective, the study informs managers on how to leverage investment in quality practices to enhance the impact of lean practice on performance. The results provide empirical evidence to support management decision-making concerning the development of competences in quality and lean practices, which may create competitive advantage.

This study contributes to the quality and lean literature and provides empirical evidence of the synergetic effects between investments in quality and lean practices. The analysis offers a greater understanding of the mechanisms that can be used to maximise the impact of investments in lean practices, from a global perspective. The findings are important to the advancement of theory in operations management, as it integrates three research streams: quality practices, lean practices and swift even flow research.

This chapter investigates the relevance of sustainability accounting practice (SAP) in the actualisation of the United Nations (UN) sustainable development goals (SDGs) 2030. Whilst the SDGs appear general, broad and far-reaching, the sustainable development agenda (SDA) impliedly places responsibilities on member nations to evolve strategies that will ensure the achievement of the SDGs in their respective countries in accordance with national circumstances and peculiar challenges. This brings to bear the need to consider measures to translate the SDGs to realities, especially in developing countries. We use a structured questionnaire to collect data on the application of SAP from publicly listed manufacturing companies in Nigeria. Secondary data on economic performance were obtained from the annual reports of companies for 5 years (2014–2018). Structural Equation Modelling and Mann-Whitney test were applied to analyse data. Result suggests that whilst the implementation level of SAP by companies is generally moderate, internalities/‘pull factors’ such as market orientation and deliberate strategy formulation significantly determine the sophistication level of SAP. The insignificant effect of the externalities/‘push factors’ (i.e. environmental uncertainty, structure of ownership and control, and intensity of competition) on SAP suggests that external pressure on companies to implement sustainability initiatives is weak. We also find that extensive usage of SAP can sustain economic performance in the long run. The chapter provides empirical evidence that manufacturing companies extensively implementing SATs can sustain economic performance and would likely have enough economic resources to implement some initiatives that are fundamental to the actualisation of the SDGs 2030. The chapter contributes to the sparse literature on sustainability practice in developing countries, and incrementally adds to knowledge on the factors driving SAP in a jurisdiction characterised by lax regulatory framework and weak institutional apparatus on sustainability. As evident in our findings, SAP engenders sustainable economic performance.

The link between corruption and firm performance has received considerable critical attention. However, until now, far too little attention has been paid to different dimensions of corruption and their relationship with firm performance. The aim of this paper is, therefore, to examine the relationship between dimensions of corruption and firm productivity.

This paper uses the fifth wave of Business Environment and Enterprise Performance Survey, which is based on the survey questionnaire with senior managers of 16,566 randomly selected firms across 32 developing countries. Empirical analysis employs structural equation modeling (SEM) and instrumental variable two-stage least square (IV 2SLS) estimation techniques.

Our results revealed that political corruption and red tape have a positive relationship with firm productivity. We also found that perceived extent of bribe and corruption experience are positively related to the firm productivity.

Findings suggest that multifaceted dimensions of corruption exist. Therefore, policymakers should develop anticorruption measures by taking into account these dimensions. In addition, policymakers should focus on improving institutional quality by reforming laws and measures to detect and punish government officials.

So far only few studies have looked at the dimensions of corruption; therefore, this study seeks to examine the effect of different dimensions of corruption together on firm performance in postcommunist countries.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-08-2019-0476

Industrialisation has contributed to global environmental problems, especially in developed countries, but increasingly so in developing ones as well. The rising public concern for the natural environment is compelling business entities to revise their business models towards green lean (GL) management. Most manufacturing firms have realised that GL implementation is a critical factor that drives their success. Therefore, keeping in view the above said aspects, the purpose of this paper is to empirically assess the complementary impact of GL practices on environmental performance.

Data from a sample of 124 Indian manufacturing industries are analysed using a structural equation modelling technique.

Evidence suggests that GL practices such as top management commitment, government support, human resource management, health and safety of employees and public pressure and legislature have significantly positive effect on environmental performance of manufacturing industries.

The sample is limited to Indian manufacturing industries situated in northern region, with a low response rate.

Successful implementations of GL practices can lead to improved environmental performance. Manufacturing industries within emerging economies like India can improve on their GL practices by incorporating these findings into their business models, while research could be guided to focus their inquiries on this and related genres of scholarly work.

To the best of the authors’ knowledge, this study is one of the first to empirically assess the complementary impact of GL practices on environmental performance within the Indian context.

Ideally, a pervasive location‐based system should allow mobile users to go anywhere and benefit from personalized and pro‐active services tailored for their current activity. However, existing location‐based systems are still far from this vision, as they are either aimed at very specific activities or too generic to provide real value to specific user activities. The objective of this work is to identify and characterize the most fundamental challenges involved in the design of pervasive location‐based systems. The approach is based on the idea that such challenges will correspond to the key design tradeoffs for location‐based systems. Building on what is believed to be the high‐level requirements for a truly pervasive locationbased system, this paper proposes a design space with four design dimensions: functional scope, added‐value, pro‐activity, and adaptability. After analyzing a representative set of location‐based systems under that design space, the combinations between functional scope and any of the other dimensions are identified as the main contradictions in the design of such systems. The paper finally discusses the research challenges associated to the identified tradeoffs.

This study aims to determine the direct and mediating effects of time pressure, work–family conflict, role ambiguity, work stress and audit quality reduction behavior.

The sample selection in this research is done by purposive sampling method based on the criteria specified by the researcher. In addition, random sample selection does not necessarily produce a sample that can represent auditors at all levels and types of Kantor Akuntan Publik (KAP). This research uses quantitative analysis with approach of structural equation modeling (SEM) method to analyze direct and indirect effect. The main method for data analysis is SEM.

Based on the results of the research note that time pressure, work–family conflict and role ambiguity each have a significant effect on work stress. In testing the direct effect on audit quality reduction behavior, only time pressure influential significant, while work–family conflict and role ambiguity not significant. However, in the test of indirect influence/influence of mediation, there is a significant influence on all tests that time pressure, work–family conflict and role ambiguity each have a significant effect on audit quality reduction behavior through work stress.

The originality of this paper is in SEM used by involving new variable which is work–family conflict. The research location is public accounting offices (KAP) in Indonesia.

This study aims to assess the essential elements of internal organisational capability that influence the cybersecurity effectiveness of a construction firm. An extended McKinsey 7S model is used to analyse the relationship between a construction firm's cybersecurity effectiveness and nine internal capability elements: shared values, strategy, structure, systems, staff, style, skills, relationships with third parties and regulatory compliance.

Based on a quantitative research strategy, this study collected data through a cross-sectional survey of professionals working in the construction sector in the United Kingdom (UK). The collected data was analysed using descriptive and inferential statistical methods.

The findings underlined systems, regulatory compliance, staff and third-party relationships as the most significant elements of internal organisational capability influencing a construction firm's cybersecurity effectiveness, organised in order of importance.

Future research possibilities are proposed including the extension of the proposed diagnostic model to consider additional external factors, examining it under varying industrial relationship conditions and developing a dynamic framework that helps improve cybersecurity capability levels while overseeing execution outcomes to ensure success.

The extended McKinsey 7S model can be used as a diagnostic tool to assess the organisation's internal capabilities and evaluate the effectiveness of implemented changes. This can provide specific ways for construction firms to enhance their cybersecurity effectiveness.

This study contributes to the field of cybersecurity in the construction industry by empirically assessing the effectiveness of cybersecurity in UK construction firms using an extended McKinsey 7S model. The study highlights the importance of two additional elements, third-party relationships and construction firm regulatory compliance, which were overlooked in the original McKinsey 7S model. By utilising this model, the study develops a concise research model of essential elements of internal organisational capability that influence cybersecurity effectiveness in construction firms.

This study aims to provide empirical evidence on the return and volatility spillover effects between Southeast Asian stock markets, bitcoin and gold in the periods before and during the COVID-19 pandemic. The interdependence among different asset classes, the two leading stock markets in Southeast Asia (Singapore and Thailand), bitcoin and gold, is analyzed for diversification opportunities.

The vector autoregressive-Baba, Engle, Kraft, and Kroner-generalized autoregressive conditional heteroskedasticity model is used to capture the return and volatility spillover effects between different financial assets. The data cover the period from October 2013 to May 2021. The full period is divided into two sub-sample periods, the pre-pandemic period and the during-pandemic period, to examine whether the financial turbulence caused by COVID-19 affects the interconnectedness between the assets.

The stocks in Southeast Asia, bitcoin and gold become more interdependent during the pandemic. During turbulent times, the contagion effect is inevitable regardless of region and asset class. Furthermore, bitcoin does not provide protection for investors in Southeast Asia. The pricing mechanism and technology behind bitcoin are different from common stocks, yet the results indicate the co-movement of bitcoin and the Singaporean and Thai stocks during the crisis. Finally, risk-averse investors should ensure that gold constitutes a significant proportion of their portfolio, approximately 40%–55%. This strategy provides the most effective hedge against risk.

The mean return and volatility spillover is analyzed between bitcoin, gold and two preeminent stock markets in Southeast Asia. Most prior studies test the spillover effect between the same asset classes such as equities in different regions or different commodities, currencies and cryptocurrencies. Moreover, the time-series data are divided into two groups based on the structural break caused by the COVID-19 pandemic. The findings of this study offer practical implications for risk management and portfolio diversification. Diversification opportunities are becoming scarce as different financial assets witness increasing integration.

The purpose of this study is to understand the factors influencing Iranian tourists’ behavioural intention to use consumer-generated contents (CGC) websites whilst browsing the web when it comes to travel planning, based upon the Technology Acceptance Model (TAM) extension.

The total of 211 valid responses were collected through an online questionnaire survey. Data collected from Iranian online tourists were used to test the proposed research model by using the partial least squares structural equation modeling method of analysis.

The results of the study indicate that perceived source credibility is the strongest predictor for behavioural intention. Furthermore, in this study, the most significant relationship is between perceived enjoyment and perceived ease of use. However, the findings of this study show that perceived ease of use is unable to predict behavioural intention of Iranian tourists while using CGC websites.

This study provides clear evidence for the importance of additional key variables in improving the authors’ understanding of the Iranian adoption behaviour concerning CGC websites for travel planning purposes. Implications of this research can aid both academicians and practitioners towards a better understanding of tourists’ adoption behaviour when associated with the social media.

This paper adopts TAM and extends it with other variables, namely, perceived enjoyment, perceived source credibility and perceived risk. It examines their influence in the behavioural intention of using CGC for travel planning in the context of Iran for the first time.