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The study aimed to explore the differential impact of various types of sales promotion on consumers' variety-seeking behaviour and the roles of utilitarian and hedonic shopping motivations in promotion-induced variety-seeking. The study further assessed the moderation impact of risk-taking tendencies and deal proneness in the promotion-induced variety-seeking buying episodes.

Based on the temporality of gratification of promotional rewards (immediate/delayed) and the type of promotional rewards (monetary/non-monetary), we classified consumer sales promotions into four types (MI: Monetary/Immediate; NMI: Non-monetary/Immediate; MD: Monetary/Delayed and NMD: Non-monetary/Delayed). We conducted survey research across four major metro cities in India. We collected data from the buyers of two supermarket chains in four major metro cities of India and analyzed the data using SEM techniques.

The study’s findings revealed that only MI and NMI sales promotions lead to variety-seeking buying, whereas MD and NMD do not influence variety-seeking. The study further revealed that MI, NMI and NMD influence hedonic shopping motivations and play a role in variety-seeking buying episodes. NMD does not influence utilitarian shopping motivation or play a role in inducing variety-seeking buying behaviour.

The study is one of the very few studies that explored the differential impact of various types of sales promotions on variety-seeking buying behaviour. The study’s findings enable the retailer to devise promotional strategies to induce variety-seeking among the shoppers. Further, the findings of the instrumentality of CSP in inducing HSM may help the retailer create a promotional environment and induce the shopper (in a good mood) to buy more, thus improving store performance.

Post-Sarbanes Oxley Act (SOX), the audit committee has been empowered greatly to play a central role in the corporate governance of firms. Embedded in agency theory, this study aims to examine the effect of the audit committee on the likelihood by firms to pay dividends.

The study population is US firms in the Institutional Shareholder Services (ISS) database from 2007 to 2018. The authors apply the multivariate logit fixed-effect regression for the analyses after conducting the appropriate statistical tests.

From the results of the research model, the authors find that there is a positive relationship between the size and gender diversity of the audit committee and the propensity to pay dividends suggesting that a larger audit committee with substantial women representation improve the information environment in firms leading to higher dividend distribution. The extent of busyness of the audit committee impacts negatively on the propensity to pay dividends. The results are driven by high-performing firms and not driven by specific levels of firm size.

The findings of the study give impetus to the audit committee as an important component of the corporate governance mechanism that advances the interest of stakeholders. Thus, efforts that seeks to promote the audit committee’s resourcefulness must be embraced by all stakeholders.

To the best of the authors’ knowledge, this study is the first to focus on audit committee and dividend payout policy of US firms post-SOX. The study demonstrates how the audit committee characteristics including its size, gender diversity and busyness affect dividend policy by mitigating information asymmetry problems.

Coronary artery disease is one of the most common cardiovascular disorders in the world, and it can be deadly. Traditional diagnostic approaches are based on angiography, which is an interventional procedure having side effects such as contrast nephropathy or radio exposure as well as significant expenses. The purpose of this paper is to propose a novel artificial intelligence (AI) approach for the diagnosis of coronary artery disease as an effective alternative to traditional diagnostic methods.

In this study, a novel ensemble AI approach based on optimization and classification is proposed. The proposed ensemble structure consists of three stages: feature selection, classification and combining. In the first stage, important features for each classification method are identified using the binary particle swarm optimization algorithm (BPSO). In the second stage, individual classification methods are used. In the final stage, the prediction results obtained from the individual methods are combined in an optimized way using the particle swarm optimization (PSO) algorithm to achieve better predictions.

The proposed method has been tested using an up-to-date real dataset collected at Basaksehir Çam and Sakura City Hospital. The data of disease prediction are unbalanced. Hence, the proposed ensemble approach improves majorly the F-measure and ROC area which are more prominent measures in case of unbalanced classification. The comparison shows that the proposed approach improves the F-measure and ROC area results of the individual classification methods around 14.5% in average and diagnoses with an accuracy rate of 96%.

This study presents a low-cost and low-risk AI-based approach for diagnosing heart disease compared to traditional diagnostic methods. Most of the existing research studies focus on base classification methods. In this study, we mainly investigate an effective ensemble method that uses optimization approaches for feature selection and combining stages for the medical diagnostic domain. Furthermore, the approaches in the literature are commonly tested on open-access dataset in heart disease diagnoses, whereas we apply our approach on a real and up-to-date dataset.

Smart cities are where dreams are made true for the future. Abu Dhabi, UAE has been given the title of smartest city in the Middle East region in the “Smart City Index 2021.” UAE is known for its rich natural resources that established much business connectivity and developed the country economically and socially. It built an innovative infrastructure with equipment for healthcare and connected people through smartphones avoiding patient travel. This enhances the patient’s life expectancy and mortality rate. UAE’s net zero emission by 2050 will reduce the carbon footprint from its industries. UAE, on the other hand, is building sustainable, innovative, smart, and energy-efficient cities. It is the leading country in the world with digital transformation in the Arab world. In this study, the scope of Gulf countries moving toward smart cities are analyzed with air pollution. The slope of regression for PM₁₀ from linear regression was Khadija (0.9442), CI 0.9237 to 0.9647 and Khalifa City (0.9745), CI 0.9591 to 0.9900. In 2022, the CO₂ per capita emissions of UAE (25.8t) are higher over the world (4.7t). However, PM₁₀ and AQI seem to be pretty good in a few cities, enhancing the living style, and climate change mitigation.

This paper aims to present research carried out on the influence of GUI graphical elements design for an integrated mission management system (IMMS) display flight planning process.

Surveys and research were conducted among students/pilots to explore graphic presentation methods for flight planning displays. Guidelines for graphical layout of the IMMS flight planning interface are proposed.

A research concept was obtained, enabling GUI tests for IMMS using prepared templates and questionnaires.

This study improves cockpit information readability, understanding and presentation, particularly for flight planning elements such as terrain, weather, traffic and zones influencing route organisation.

This study targets possible improvements to the flight path planning process in aviation, inducing a reduction in errors related to human factors while processing the visual data on-board.

The study verified the impact of drawing and rendering methods on IMMS flight planning, suggesting that current display methods may be error-prone when showing hazard information from multiple sources on a single screen.

In innovation management, the complexity inherited in the supply network may be necessary for success. This study aims to holistically examine innovation complexities and system attractors within a hierarchically nested supply network and explore how they dynamically interact and influence adaptive innovation processes.

Taking a complexity theory perspective, we employed a methodological bricolage approach using a single case study with multiple embedded units of analysis – namely, a supply network encompassing 36 firms. We drew upon primary data obtained from 42 interviewees and rich secondary data, and we employed a temporal exponential random graph model to examine the micro-foundations of the evolution of the sampled supply network over a decade.

This study presents a comprehensive overview of the innovation complexities—relational, temporal, dynamic, operational and structural – and how they manifest within a supply network. It also identifies three systemic attractors – point, periodic and strange – and elucidates their relationships with the complexities and their impact on innovative supply network dynamics. The resulting conceptual framework and working propositions provide a detailed perspective on the complex interplay between balanced order and chaos and the potentially unbalanced innovation states within a supply network.

This research offers an in-depth perspective on the innovation complexities and dynamic attractors within a supply network from a holistic, multilevel perspective. It advances complexity theory and deepens the understanding of supply networks as complex adaptive systems.

This paper aims to present the result of an assessment of the potential effect of building information modelling (BIM) adoption on the reduction of construction waste (CW) from a developing country's perspective. This was done with a view to reducing the waste generated in construction projects particularly at the design and pre-contract stages.

The study adopted a post-positivism philosophical approach, which informed the use of a quantitative research design and a questionnaire as instrument for data collection. The data gathered from construction professionals in the Nigeria construction industry were analysed using an array of statistical tools such as frequency, percentage, Kruskal–Wallis H-test, Kendall's coefficient of concordance, chi-square and exploratory factors analysis.

The study revealed five major groups of factors causing CW at the design and pre-contract stages that can be avoided or minimised through BIM implementation. These are; (1) errors in design and documentation, (2) specification and quality factors, (3) estimating and site condition factors, (4) planning of work factors and (5) procurement related factors.

The findings of the study offer practical insight for industry participants on the need for BIM implementation to reduce CW by identifying the diverse areas responsible for these waste generation.

While there has been significant literature on BIM implementation, contributions on the effect of this technology in reducing waste generation particular at the design and pre-contract stages in developing countries has been almost non-existent. This study strives to fill in this gap by showcasing the major waste generating activities that can be avoided through the use of BIM.

This research looks at the difficulties managers have in adapting to the modern financial environment in the context of globalization. It looks at several topics related to international financial strategy, such as managing multinational teams, managing market entrance strategies, managing currency risk, adapting managerial style to a global workplace, adhering to international rules and assessing geopolitical risk. The study focuses special attention on the complexity involved in the decision-making process in terms of market entry tactics and currency risk management, drawing on an extensive literature review and real-world examples. It emphasizes the significance of effective communication and leadership styles when managing global teams, as well as the necessity of intercultural competency for managers to function in various cultural contexts. The study also looks at the significance of regulatory compliance for global corporate operations and how geopolitical risk affects investment choices. This report uses critical analysis to pinpoint the opportunities and problems brought about by globalization while offering managers practical advice on how to adapt to the shifting financial landscape.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Grey Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Grey Wolf Optimization, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.