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This research aims to understand the smartphone security behavior using protection motivation theory (PMT) and tests the current PMT model employing statistical and predictive analysis using machine learning (ML) algorithms.

This study employs a total of 241 questionnaire-based responses in a nonmandated security setting and uses multimethod approach. The research model includes both security intention and behavior making use of a valid smartphone security behavior scale. Structural equation modeling (SEM) – explanatory analysis was used in understanding the relationships. ML algorithms were employed to predict the accuracy of the PMT model in an experimental evaluation.

The results revealed that the threat-appraisal element of the PMT did not have any influence on the intention to secure smartphone while the response efficacy had a role in explaining the smartphone security intention and behavior. The ML predictive analysis showed that the protection motivation elements were able to predict smartphone security intention and behavior with an accuracy of 73%.

The findings imply that the response efficacy of the individuals be improved by cybersecurity training programs in order to enhance the protection motivation. Researchers can test other PMT models, including fear appeals to improve the predictive accuracy.

This study is the first study that makes use of theory-driven SEM analysis and data-driven ML analysis to bridge the gap between smartphone security’s theory and practice.

This paper aims to discuss innovations in the training and development practices of companies and delineate a new approach to training and development in the context of the hybrid workplace using the ADDIE and Kirkpatrick training models.

This paper discusses innovations in training and development in modern times and builds on the instructional training design approach or the ADDIE Model and the Kirkpatrick Model of training evaluation.

The paper presents new approaches to training and development in the context of the hybrid work model applying the ADDIE Model and the Kirkpatrick Model. These new approaches are both necessitated and also made possible due to the technological advancements of modern times.

With the rapid transition of companies to the hybrid model of work in recent times, several human resource management practices need to be transformed to suit the requirements of the new work model. Training and development is one function that needs to change in the hybrid work model to ensure its effectiveness. This paper analyses innovations in the training and development practices of companies and discusses new approaches while applying existing training models, the ADDIE and Kirkpatrick Models, to adapt to the changes associated with the hybrid work model.

This study proposes a machine learning framework to predict customer complaints from production line tests in an automotive company's lot-release process, enhancing Quality 4.0. It aims to design and implement the framework, compare different machine learning (ML) models and evaluate a non-sampling threshold-moving approach for adjusting prediction capabilities based on product requirements.

This study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) and four ML models to predict customer complaints from automotive production tests. It employs cost-sensitive and threshold-moving techniques to address data imbalance, with the F1-Score and Matthews correlation coefficient assessing model performance.

The framework effectively predicts customer complaint-related tests. XGBoost outperformed the other models with an F1-Score of 72.4% and a Matthews correlation coefficient of 75%. It improves the lot-release process and cost efficiency over heuristic methods.

The framework has been tested on real-world data and shows promising results in improving lot-release decisions and reducing complaints and costs. It enables companies to adjust predictive models by changing only the threshold, eliminating the need for retraining.

To the best of our knowledge, there is limited literature on using ML to predict customer complaints for the lot-release process in an automotive company. Our proposed framework integrates ML with a non-sampling approach, demonstrating its effectiveness in predicting complaints and reducing costs, fostering Quality 4.0.

The purpose of this paper is to examine the relevance of information, transparency and information efficiency in short-run performance of new issues. The current research evaluates the short-run performance of IPOs during 2005-2012, which even includes the recessionary period. The present study evaluates the impact of informational variables on first-day returns.

The short-run performance of the IPOs is measured through market adjusted excess return. A structural equation model (SEM) has been designed to identify how information influences the short-run performance of IPOs.

The results of structural model reveal that the sale of promoters’ stake and underwriters’ reputation are the major contributors towards information and are found to be highly significant statistically. The model also shows that the issue size (a component of information) is statistically insignificant at 5 per cent. The model suggests that the availability of information has negative impact on the first day returns indicating that the issuer which disclose maximum information to the public get lower returns on the listing day and hence, their issues are less underpriced.

The present study has a contribution in investment decisions for global investors, as the participation of international investors is common in IPOs of emerging markets. The findings of the study are expected to be useful to the practitioners in predicting the pricing of IPOs based on the informational variables influencing their performance.

The pressing issues of climate change and environmental degradation call for a reevaluation of how we approach economic activities. Both leaders and corporations are now shifting their focus, toward adopting practices and embracing the concept of circular economy (CE). Within this context, the Food and Beverage (F&B) sector, which significantly contributes to greenhouse gas (GHG) emissions, holds the potential for undergoing transformations. This study aims to explore the role that Artificial Intelligence (AI) can play in facilitating the adoption of CE principles, within the F&B sector.

This research employs the Best Worst Method, a technique in multi-criteria decision-making. It focuses on identifying and ranking the challenges in implementing AI-driven CE in the F&B sector, with expert insights enhancing the ranking’s credibility and precision.

The study reveals and prioritizes barriers to AI-supported CE in the F&B sector and offers actionable insights. It also outlines strategies to overcome these barriers, providing a targeted roadmap for businesses seeking sustainable practices.

This research is socially significant as it supports the F&B industry’s shift to sustainable practices. It identifies key barriers and solutions, contributing to global climate change mitigation and sustainable development.

The research addresses a gap in literature at the intersection of AI and CE in the F&B sector. It introduces a system to rank challenges and strategies, offering distinct insights for academia and industry stakeholders.

Consumers, governments and regulatory agencies are concerned about the social and environmental aspect that pushes firms to move towards the circular economy. The transformation of the existing linear model into a circular model depends on several circular economy practices. Therefore, the purpose of this study is to identify and analyse the critical factors that are responsible for the adoption of circular practices.

In total, 15 critical factors are identified through the literature review and 12 are finalised with the grey Delphi method. Further, these critical factors are prioritised using the weighted aggregated sum/product assessment (WASPAS) method. A sensitivity analysis is also conducted to test the robustness of the ranking of critical factors obtained from WASPAS.

The finding of this study show that “top management participation,” “market for recovered products” and “circular economy oriented R&D activities promotion” are the most significant factors for circular practice adoption. These factors need to address on the highest priority by the stakeholders.

This study is beneficial for the managers to formulate their strategies for the adoption of circular practices. The prioritisation of critical factors supports the managers and professionals to optimise their effort and resources to adopt the circular practice.

This study explores and analyses the critical factor for circular economy practice adoption in the supply chain in the context of emerging economies.

Many cities across the world are actively investing in ways to excel in the innovation economy through the development of innovation districts as one of the most popular policy options. While innovation districts are among the leading drivers of innovation activities in cities, they are also high-cost and high-risk investments. Besides, holistic approaches for assessing these districts’ multifaceted performances are scarce. Bridging this knowledge gap is critical, hence, this paper aims to explore how innovation district performance can be assessed through a classification framework.

The paper introduces a multidimensional innovation district classification framework and applies it into Australian innovation districts with divergent features, functions, spatial and contextual characteristics. The study places 30 innovation districts from South East Queensland under the microscope of the framework to assess the multifaceted nature of innovation district performance. It uses qualitative analysis method to analyse both the primary and secondary data, and descriptive analysis with basic excel spreadsheet calculations to analyse the validity of the data.

The data analysis clusters 30 innovation districts from South East Queensland under three performance levels – i.e. desired, acceptable and unsavoury – concerning their form, feature and function characteristics.

The results disclose that the framework is a practical tool for informing planners, developers and managers on innovation district performances, and it has the capability to provide guidance for policymakers on their policy and investment decisions regarding the most suitable innovation district types and characteristics to consider.