Search results

When is it more profitable for multinational manufacturers to manufacture in high-cost environments and when in low-cost environments? While the literature offers many cues to answer this question, too little empirical research directly addresses this. In this study, we quantitatively and empirically investigate the financial effect of companies' production footprint in low-cost and high-cost environments for different types of production networks.

Using the data of 770 multinational manufacturing companies, we analyze the relationship between production footprints and profitability during four calendar semesters in 2018 and 2019 (N = 2,940), investigating the moderating role of companies' production network type.

We find that companies with networks distinguished by both high levels of product complexity and process sophistication profit the most from producing to a greater extent in high-cost countries. For these companies, shifting production to low-cost countries would be associated with negative performance implications.

Our findings suggest that the production geography of companies should be attuned to their network type, as defined by the companies' process sophistication and product complexity. Manufacturing in low-cost countries is not always the best choice, as doing so can adversely affect profits if the products are highly innovative and the production processes are complex.

We contribute to the scarce empirical literature on managing global production networks and provide a data-driven analysis that contributes to answering some of the enduring questions in this critical area.

This study seeks to explore the intricate relationship among supply chain transparency, digitalization and idiosyncratic risk, with a specific focus on newly public firms. The objective is to determine whether supply chain transparency effectively mitigates idiosyncratic risk within this context and to understand the potential impact of digitalization on this dynamic interplay.

The study utilizes data from Initial Public Offerings (IPOs) on China’s Growth Enterprise Board (ChiNext) over the last five years, sourced from the CSMAR database and firms’ annual reports. The research covers the period from 2009 to 2021, observing each firm for five years post-IPO. The final sample comprises 2,645 observations from 529 firms. The analysis employs the Hausman test, considering the panel-data structure of the sample and favoring fixed effects over random effects. Additionally, it applies the high-dimensional fixed effects (HDFE) estimator to address unobserved heterogeneity.

The analysis initially uncovered an inverted U-shaped relationship between supply chain transparency and idiosyncratic risk, indicating a delicate equilibrium where detrimental effects diminish and beneficial effects accelerate with increased transparency. Moreover, this inverted U-shaped relationship was notably more pronounced in newly public firms with a heightened level of firm digitalization. This observation implies that firm digitalization amplifies the impact of transparency on a firm’s idiosyncratic risk.

This study distinguishes itself by providing distinctive insights into supply chain transparency and idiosyncratic risk. Initially, we introduce and substantiate an inverted U-shaped correlation between supply chain transparency and idiosyncratic risk, challenging the conventional linear perspective. Secondly, we pioneer the connection between supply chain transparency and idiosyncratic risk, especially for newly public firms, thereby enhancing comprehension of financial implications. Lastly, we pinpoint crucial digital conditions that influence the relationship between supply chain transparency and idiosyncratic risk management, offering a nuanced perspective on the role of technology in risk management.

The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility.

This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area.

The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area.

This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.

This paper aims to conduct a systematic review of the literature on the quality of integrated reports (IR) and highlight the gaps in the existing research to provide directions and suggestions for future research.

This study was conducted through a systematic literature review using content analysis based on 40 papers from the Scopus, Web of Science and EBSCOhost databases on IR quality. While reading the full-text papers, the authors found six additional papers referenced by the literature being reviewed that were relevant to IR quality. Thus, there were 46 papers in the final review. The analysis begins with the definition and dimension of IR quality and theoretical lenses. Furthermore, this study outlines constructs or variables used in the previous literature.

The authors found that most studies used the quantitative method (41 papers or 89%). Five papers in the literature used qualitative methods (11%). Most researchers (34 papers or 72%) defined IR quality as consistent with the International Integrated Reporting Council framework, specifically the eight content elements. In particular, with the constructs that make up the quality of the IR, variations between researchers were found. Furthermore, there were some gaps that could be the directions for future research.

The literature that provides academic knowledge about IR quality is still limited, and research on IR is still growing. The literature review conducted by this study can provide an overview of the current research positions on the quality of IR and directions for future research in this area.

This study intends to show corporate executives a framework demonstrating the quality of corporate reporting. It can impact not only investors as a specific stakeholder group but also other stakeholder groups.

To the best of the authors’ knowledge, this study is the first literature review to examine the quality of IR, thus providing a map of current research to suggest directions for future research. Most of the previous literature reviews have been focused on integrated reporting (IR) in general and not quality.

The purpose of this paper is to study the effects of sports sponsorship on brand equity using two managerially related outcomes: price premium and market share.

This study uses a best–worst discrete choice experiment (BWDCE) and compares the outcome with that of the purchase intention scale, an established probabilistic measure of purchase intention. The total sample consists of 409 fans of three soccer teams sponsored by three different competing brands: Nike, Adidas and Puma.

With sports sponsorship, fans were willing to pay more for the sponsor’s product, with the sponsoring brand obtaining the highest market share. Prominent brands generally performed better than less prominent brands. The best–worst scaling method was also 35% more accurate in predicting brand choice than a purchase intention scale.

Future research could use the same method to study other types of sponsors, such as title sponsors or other product categories.

Sponsorship managers can use this methodology to assess the return on investment in sponsorship engagement.

Prior sponsorship studies on brand equity tend to ignore market share or fans’ willingness to pay a price premium for a sponsor’s goods and services. However, these two measures are crucial in assessing the effectiveness of sponsorship. This study demonstrates how to conduct such an assessment using the BWDCE method. It provides a clearer picture of sponsorship in terms of its economic value, which is more managerially useful.

The existing technology acceptance models have not yet investigated functional and motivational factors impacting trust in and use of conversational artificial intelligence (AI) by integrating the feedback and sequential updating mechanisms. This study challenged the existing models and constructed an integrated longitudinal model. Using a territory-wide two-wave survey of a representative sample, this new model examined the effects of hedonic motivation, social motivation, perceived ease of use, and perceived usefulness on continued trust, intended use, and actual use of conversational AI.

An autoregressive cross-lagged model was adopted to test the structural associations of the seven repeatedly measured constructs.

The results revealed that trust in conversational AI positively affected continued actual use, hedonic motivation increased continued intended use, and social motivation and perceived ease of use enhanced continued trust in conversational AI. While the original technology acceptance model was unable to explain the continued acceptance of conversational AI, the findings showed positive feedback effects of actual use on continued intended use. Except for trust, the sequential updating effects of all the measured factors were significant.

This study intended to contribute to the technology acceptance and human–AI interaction paradigms by developing a longitudinal model of continued acceptance of conversational AI. This new model adds to the literature by considering the feedback and sequential updating mechanisms in understanding continued conversational AI acceptance.

Additive manufacturing also known as 3D printing is an evolving advanced manufacturing technology critical for the new era of complex machinery and operating systems. Manufacturing systems are increasingly faced with risk of attacks not only by traditional malicious actors such as hackers and cyber-criminals but also by some competitors and organizations engaged in corporate espionage. This paper aims to elaborate a plausible risk practice of designing and demonstrate a case study for the compromised-based malicious for polymer 3D printing system.

This study assumes conditions when a machine was compromised and evaluates the effect of post compromised attack by studying its effects on tensile dog bone specimens as the printed object. The designed algorithm removed predetermined specific number of layers from the tensile samples. The samples were visually identical in terms of external physical dimensions even after removal of the layers. Samples were examined nondestructively for density. Additionally, destructive uniaxial tensile tests were carried out on the modified samples and compared to the unmodified sample as a control for various mechanical properties. It is worth noting that the current approach was adapted for illustrating the impact of cyber altercations on properties of additively produced parts in a quantitative manner. It concurrently pointed towards the vulnerabilities of advanced manufacturing systems and a need for designing robust mitigation/defense mechanism against the cyber altercations.

Density, Young’s modulus and maximum strength steadily decreased with an increase in the number of missing layers, whereas a no clear trend was observed in the case of % elongation. Post tensile test observations of the sample cross-sections confirmed the successful removal of the layers from the samples by the designed method. As a result, the current work presented a cyber-attack model and its quantitative implications on the mechanical properties of 3D printed objects.

To the best of the authors’ knowledge, this is the original work from the team. It is currently not under consideration for publication in any other avenue. The paper provides quantitative approach of realizing impact of cyber intrusions on deteriorated performance of additively manufactured products. It also enlists important intrusion mechanisms relevant to additive manufacturing.

In academia, assessment is often narrow in its focus on research productivity, its application of a limited number of standardised metrics and its summative approach aimed at selection. This approach, corresponding to an exclusive, subject-oriented concept of talent management, can be thought of as at odds with a broader view of the role of academic institutions as accelerating and improving science and scholarship and its societal impact. In recent years, open science practices as well as research integrity issues have increased awareness of the need for a more inclusive approach to assessment and talent management in academia, broadening assessment to reward the full spectrum of academic activities and, within that spectrum, deepening assessment by critically reflecting on the processes and indicators involved (both qualitative and quantitative). In terms of talent management, this would mean a move from research-focused assessment to assessment including all academic activities (including education, professional performance and leadership), a shift from focus on the individual to a focus on collaboration in teams (recognising contributions of both academic and support staff), increased attention for formative assessment and greater agency for those being evaluated, as well as around the data, tools and platforms used in assessment. Together, this represents a more inclusive, subject-oriented approach to talent management. Implementation of such changes requires involvement from university management, human resource management and academic and support staff at all career levels, and universities would benefit from participation in mutual learning initiatives currently taking shape in various regions of the world.

This study explores the role of knowledge management capabilities (KMCs) in enhancing competitive advantage and organisational performance in fashion retailing. Employing the resource-based view (RBV) and knowledge-based view (KBV) perspectives, it investigates the interplay between managing knowledge effectively and fashion products’ complexity. The goal is to provide new insights into optimising KMC for greater agility and success in the fashion retail industry.

The study analysed survey data from 322 US fashion retail professionals using partial least squares structural equation modelling (PLS-SEM).

The results revealed that knowledge infrastructure capability enhanced both competitive advantage and organisational performance significantly. In contrast, knowledge process capability did not significantly affect competitive advantage, it improved organisational performance. Importantly, product complexity moderated the relationship between competitive advantage and organisational performance negatively.

This study underscores the necessity for retailers in the fashion industry to enhance their KMC to bolster competitive advantage and organisational performance, while it also acknowledges product complexity’s effect on these strategies. These insights offer actionable guidance for industry leaders to optimise knowledge management to navigate the rapidly evolving retail landscape.

This research offers novel insights into the interplay of product complexity and KMC in fashion retail and highlights the unique effects on competitive advantage and organisational performance valuable for both academia and industry.

Although studies have shown that electronic media exposure can negatively affect infants’ and young children’s language development, exposure to these forms of media is increasing in North America. To better understand the types of electronic media exposure and their potential effects, we utilized naturalistic daylong recordings collected in the homes of bilingual Spanish–English infants of Latinx descent (n = 37). The present study examines contextual aspects of electronic media exposure, and the effects of electronic media on two types of parent–infant social interactions associated with child language development: parentese (a style of infant-directed speech distinguished by its higher pitch, slower tempo, and exaggerated intonation) and parent–infant turn-taking. Using Language ENvironment Analysis (LENA), two daylong audio recordings were collected from each family. These recordings were manually annotated for electronic media type, directedness, language, parental support, parentese, and turn-taking. Our results showed that the infants in our study experienced exposure to many different forms of electronic media, in both English and Spanish, and that the programming was predominantly adult-directed rather than child-directed. While both parentese and turn-taking were reduced in the presence of electronic media, the strength of these effects was modulated by electronic media sources, demonstrating that various devices differentially affect parental language input. These results provide a glimpse into what types of media young bilingual Spanish–English learning infants are experiencing and can help researchers design language interventions that are inclusive and relevant for families from these populations.