Search results

This extensive literature review critically examines the theoretical underpinnings of governance mode choices between strategic alliances and mergers and acquisitions (M&As). By synthesizing insights from transaction cost economics (TCE), resource-based view (RBV), social network theories, institutional theory, and real options theory, the author provides a holistic framework to guide decision-makers navigating the complex landscape of strategic decision-making. By meticulously exploring each theory and in-depth analysis of empirical findings, the author uncovers the consistency and inconsistency in the literature, shedding light on the multifaceted considerations that shape governance mode decisions and offer future research opportunities.

New product introductions, particularly line extensions (LEs), are common in consumer goods categories. Despite their commonality, the success of LEs are not guaranteed. The purpose of this study is to provide brands that introduce LEs a benchmark about what success to expect.

This study investigates the success of 36,994 LEs in each quarter for the first three years after introduction. Four indicators are calculated using consumer panel data to benchmark how long LEs survive (failure rate), how competitive they are in the category (market share) and how they are adopted by category buyers (penetration and repeat buyer rate).

Most LEs survive after the first year, but many cease to exist or perform well in the long term. Around 50% of LEs fail a year after launch, but this failure rate halves once seasonal LEs are removed. Failure rates start to approach 80% after three years. Most LEs do not perform better than existing products. Around three in four LEs have a market share or penetration near or below the category norm. Although this percentage decreases the longer after launch, most LEs are still below the category norm.

These new product success benchmarks provide guidelines to practitioners about what success the “typical” LE will achieve. This research can help guide new product investment decisions because it provides context on what is feasible to achieve.

Four market success measures are used, a departure from past benchmarking research which uses practitioner evaluation on metrics seldom used in practice. The authors provide guidelines about when and how to measure LE and new product success more broadly.

This paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.

Using the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.

Our results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.

This research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.

This paper aims to focus on the selection of an appropriate nature-inspired texture pattern for cutting tool tribological surface. The selection process uses the recognized skin textures of different snakes scrolling on highly rough and projected surface conditions to analyze suitability of texture based on the texture geometry and machining conditions. The work also aims to propose a texture pattern selection process to incorporate on cutting tool tribological surface.

The selection of alternative nature-inspired texture patterns based on the texture pattern geometry and machining properties leads to a multi-criteria decision-making problem. Thirteen criteria are considered for selecting an appropriate texture pattern among 14 alternatives, i.e. nature-inspired texture patterns. In the present work, an integrated analytical hierarchy process (AHP)-TOPSIS, AHP-multi-objective optimization on the basis of ratio analysis (MOORA) and AHP-Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) approaches have been proposed for the selection of an appropriate nature-inspired texture pattern. AHP is used for the formulation of decision-making matrix and criteria weight calculations and ranking of alternatives is done by three methods. Spearman’s correlation compared and found positive relations between rank assigned by methods. Experimental validation is done in Lathe for selected texture effects.

The texture parameters C-1 (Width of texture) and C-2 (Depth of texture) are found significant, while T-2 (Blended Krait) and T-6 (Banded Racer-1) texture is found optimal to generate on cutting tool surface.

Only some nature-inspired texture patterns have been recognized before the selection; an infinite number of textures are available in nature. The size of the texture pattern is difficult to identify by the selection process because each texture pattern may have different effects on tribological surfaces.

The proposed selection methodology of nature-inspired texture patterns will help identify optimal texture geometry for specific tribological applications. The nature-inspired texture patterned tool has a significant impact on the cutting force and temperature due to its tribological effect on the cutting tool surface; it decreases the power required for machining. The machining characteristics like roughness are found to decrease by using nature-inspired texture patterned tools.

Various nature-inspire texture studies to generate specific effects on the tribological surfaces may be started study for the surface of aircraft, ships, bearings, etc. Small and big fabrication industries may benefit by decreasing the cost of machining using nature-inspired texture-patterned tools. Research society will pay attention to nature’s inspiration.

Novel snake-skin-inspired texture patterns are recognized and hybrid MCDM methods are proposed to select optimal texture pattern. Proposed method used single time normalization to effectively rank the alternatives. The insights gained from this research can be extrapolated to address similar challenges in selecting nature-inspired textures for various applications.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0163/

By a thorough bibliometric examination of the area through time, this paper analyses the research landscape of metaverse in education. It is an effort that is focused on the metaverse research trends, academic production and conceptual focus of scientific publications.

The Web of Science (WoS) database was explored for information containing research articles and associated publications that met the requirements. For a thorough analysis of the trend, thematic focus and scientific output in the subject of metaverse in education, a bibliometric technique was used to analyse the data. The bibliometrix package of R software, specifically the biblioshiny interface of R-studio, was used to conduct the analysis.

The analysis of the metaverse in education spanning from 1995 to the beginning of 2023 reveals a dynamic and evolving landscape. Notably, the field has experienced robust annual growth, with a peak of publications in 2022. Citation analysis highlights seminal works, with Dionisio et al. (2013) leading discussions on the transition of virtual worlds into intricate digital cultures. Thematic mapping identifies dominant themes such as “system,” “augmented reality” and “information technology,” indicating a strong technological focus. Surprisingly, China emerges as a leading contributor with significant citation impact, emphasising the global nature of metaverse research. The thematic map suggests ongoing developments in performance and future aspects, emphasising the essential role of emerging technologies like artificial intelligence and virtual reality. Overall, the findings depict a vibrant and multidimensional metaverse in education, poised for continued exploration and innovation.

The study is among the pioneers that provide a comprehensive bibliometric analysis in the area of metaverse in education which will guide the novice researchers to identify the unexplored areas.

Recent technological developments have encouraged the United Nations to promote the adoption of digital technologies to achieve the Sustainable Development Goals (SDGs). In addition to initiatives from businesses, an increasing number of studies indicate that public service agencies may gain benefits from adopting digital transformation. On a global scale, policymakers are examining the integration of digital technologies, specifically artificial intelligence (AI), into public service delivery (PSD), acknowledging the potential advantages and obstacles for the public sector. Therefore, the objective of this study is to investigate the impact of AI on PSD to support the SDGs initiative.

The research used a qualitative approach to explore the intersection of AI, SDGs and PSD. This approach involved scrutinising relevant publications and conducting an extensive literature review. The research also used bibliographic analysis to discern patterns within the field. Findings from the literature review and bibliographic analysis contributed to identifying research trends that explore the complex relationship among AI, PSD and the SDGs. The model derived from this comprehensive review and analysis elucidates the potential of AI to enhance PSD and contribute to the achievement of the SDGs.

The bibliographic study revealed significant research trends concerning AI, PSD and SDGs through an empirical investigation of an extensive array of peer-reviewed articles. This investigation focused on how the public sector can improve its delivery of services to citizens and all stakeholders to advance the SDGs. AI holds the promise of revolutionising PSD and bolstering the SDGs. By leveraging AI’s capabilities in data analysis, automation and customisation, governments can enhance the efficiency, effectiveness and accessibility of public services. This, in turn, enables public servants to tackle more complex tasks while providing citizens with personalised and relevant experiences. Additionally, the study advocates modelling the intersection of PSD and AI to achieve sustainable development.

The employed research methodologies, such as literature reviews and bibliographic analysis, enrich the context of AI, SDGs and PSD. They offer a comprehensive perspective, identify knowledge gaps and furnish policymakers, practitioners and academics with a conceptual framework for informed decision-making and sustainable development endeavours.

The study provides an agenda for AI and SDGs research on application in PSD. It emphasises varied research viewpoints, methods and gaps. This study helps researchers as well as practitioners identify subtopics, intersecting themes and new research pathways.

From a target perspective, this paper aims to examine the impact of environmental, social and governance (ESG) performance on mergers and acquisitions (M&A) transaction valuations.

This paper uses a sample of 629 international transactions conducted between 2002 and 2020. Ordinary least squares (OLS) regression was applied by using ESG aggregate score and the three ESG pillars: environment, social and governance.

This paper finds that the ESG performance of targets has a negative and significant impact on acquisition premiums. However, this paper finds that targets receive lower premiums by increasing their ESG score, suggesting that targets would do better to focus on ESG to increase shareholder wealth. Thus, results of this paper support the view that ESG-focused firms create shareholder value through the M&A process. Furthermore, results of this paper indicate that environmental and social aspects of ESG drive the acquisition premium. The governance score does not seem to be related to acquisition premiums.

To the best of the authors’ knowledge, this study is the first study to assess whether ESG performance impacts the valuation of M&A transactions by decomposing ESG into its three components.

This paper reviews and analyses renewable energy options, namely underground thermal, solar, wind and marine wave energy, in seaport cargo terminal operations.

Four renewable energy options that are deployed or tested in different ports around the world are qualitatively examined for their overall implementation potential and characteristics and their cost and benefits. An application to the port of Singapore is discussed.

Geophysical conditions are key criteria in assessing renewable energy options. In the case of Singapore, solar power is the only suitable renewable energy option.

Being a capital-intensive establishment with high intensities of cargo operations, seaports usually involve a high level of energy consumption. The study of renewable energy options contributes to seaport sustainability.

A key recommendation is to implement a smart energy management system that enables the mixed use of renewable energy to match energy demand and supply optimally and achieve higher energy efficiency.

The use of renewable energy as an eco-friendlier energy source is underway in various ports. However, there is almost no literature that analyses and compares various renewable energy options potentially suitable for cargo terminal operations in ports. This paper narrows the knowledge gaps.

Several chaotic system-based encryption techniques have been presented in recent years to protect digital images using cryptography. The challenges of key distribution and administration make symmetric encryption difficult. The purpose of this paper is to address these concerns, the novel hybrid partial differential elliptical Rubik’s cube algorithm is developed in this study as an asymmetric image encryption approach. This novel algorithm generates a random weighted matrix, and uses the masking method on image pixels with Rubik’s cube principle. Security analysis has been conducted, it enhances and increases the reliability of the proposed algorithm against a variety of attacks including statistical and differential attacks.

In this light, a differential elliptical model is designed with two phases for image encryption and decryption. A modified image is achieved by rotating and mixing intensities of rows and columns with a masking matrix derived from the key generation technique using a unique approach based on the elliptic curve and Rubik’s cube principle.

To evaluate the security level, the proposed algorithm is tested with statistical and differential attacks on a different set of test images with peak signal-to-noise ratio, unified average changed intensity and number of pixel change rate performance metrics. These results proved that the proposed image encryption method is completely reliable and enhances image security during transmission.

The elliptic curve–based encryption is hard to break by hackers and adding a Rubik’s cube principle makes it even more complex and nearly impossible to decode. The proposed method provides reduced key size.

Based on qualitative data from a large study exploring Muslim experiences in the workplace, this chapter explains how Muslim dress standards inform identity and are influenced by US cultural ideals about self-presentation and perceived anti-Muslim hostility. Theoretical sampling was used to find 25 men and 59 women, 32 of whom are veiled. These individuals worked at major corporations as numerical minorities or held professions where they encountered non-Muslims regularly. Informed by theories of orientalism and social identity, findings examine hegemonic representations of organizational power and describe how men could employ masculine practices to navigate anti-Muslim discourse and foster a sense of belonging at work. Within immigrant-centered workplaces, women face cultural backlash for appropriating Western styles deemed immodest. While working outside their community, women who wore hijabs emphasized their femininity through softer colors, makeup, or “unpinning” their veil to offset the visceral reaction to their hijab. Thus, adapting to workplace dress expectations is structured by intersections of gender, religion, and workplace location. This chapter illustrates how Muslim dress strategies indirectly reflect how Western standards of dress, behavior, and self-expression determine qualifications and approachability within workplace structures, marginalizing Muslims and reproducing racial and gender hierarchies.