Search results

This study aims to explore the dynamic interplay of key resources (i.e. research and development (R&D), advertising and exports) in affecting the performance of USA manufacturing firms. Specifically, the authors examine the dynamic impact of joint resources and predict differential effect scales contingent on firm capabilities.

This study presents a combined multiple regression analysis (MRA)-multilayer perceptron (MLP) neural network modeling and investigates the complex interlinkage of capabilities, resources and performance. As an innovative approach, the MRA-MLP model investigates the effect of capabilities under the combinatory deployment of joint resources.

This study finds that the impact of joint resources and synergistic rents is not uniform but rather distinctive according to the combinatory conditions and that the pattern is further shaped by firm capabilities. Accordingly, besides signifying the contingent aspect of capabilities across a range of resource combinations, the result also shows that managerial sophistication in adaptive resource control is more than a managerial ethos.

The proposed analytic process provides scientific decision support tools with control mechanisms with respect to deploying multiple resources and setting actionable goals, thereby presenting pragmatic benchmarking options to industry managers.

Using the theoretical underpinnings of the resource-based view (RBV) and resource orchestration, this study advances knowledge about the complex interaction of key resources by presenting a salient analytic process. The empirical design, which portrays holistic interaction patterns, adds to the uniqueness of this study of the complex interlinkages between capabilities, resources and shareholder value.

This study aims to assess the predictive performance of various factors on Bitcoin returns, used for the development of a robust forecasting support decision model using machine learning techniques, before and during the COVID-19 pandemic. More specifically, the authors investigate the impact of the investor's sentiment on forecasting the Bitcoin returns.

This method uses feature selection techniques to assess the predictive performance of the different factors on the Bitcoin returns. Subsequently, the authors developed a forecasting model for the Bitcoin returns by evaluating the accuracy of three machine learning models, namely the one-dimensional convolutional neural network (1D-CNN), the bidirectional deep learning long short-term memory (BLSTM) neural networks and the support vector machine model.

The findings shed light on the importance of the investor's sentiment in enhancing the accuracy of the return forecasts. Furthermore, the investor's sentiment, the economic policy uncertainty (EPU), gold and the financial stress index (FSI) are the top best determinants before the COVID-19 outbreak. However, there was a significant decrease in the importance of financial uncertainty (FSI and EPU) during the COVID-19 pandemic, proving that investors attach much more importance to the sentimental side than to the traditional uncertainty factors. Regarding the forecasting model accuracy, the authors found that the 1D-CNN model showed the lowest prediction error before and during the COVID-19 and outperformed the other models. Therefore, it represents the best-performing algorithm among its tested counterparts, while the BLSTM is the least accurate model.

Moreover, this study contributes to a better understanding relevant for investors and policymakers to better forecast the returns based on a forecasting model, which can be used as a decision-making support tool. Therefore, the obtained results can drive the investors to uncover potential determinants, which forecast the Bitcoin returns. It actually gives more weight to the sentiment rather than financial uncertainties factors during the pandemic crisis.

To the authors’ knowledge, this is the first study to have attempted to construct a novel crypto sentiment measure and use it to develop a Bitcoin forecasting model. In fact, the development of a robust forecasting model, using machine learning techniques, offers a practical value as a decision-making support tool for investment strategies and policy formulation.

The purpose of this study is to review the overall trends in diversity, equity and inclusion (DEI) research in the management field.

We performed a scoping review to comprehensively understand how DEI research has been conducted and to inform future research and practices in the management field. We reviewed 725 articles published from 1976 to 2022 in SSCI-indexed management journals. We examined publication profiles (number of articles and research focus), publication tendency (journals and years), chronological publication trends, work environments (traditional vs remote/virtual work settings) and dimensions of DEI, including age, gender, race and culture.

We identified six common DEI research themes: (1) DEI management and practice, (2) perspectives on DEI, (3) team/group diversity, (4) DEI conceptualization, (5) leadership for DEI and (6) DEI climate. Finally, discussion, implications and recommendations for future research are presented.

Our research provides a comprehensive outline of the DEI research and suggests future directions to contribute to and advance knowledge on DEI in the management field.

Introduction: The bank plays a significant role in financial intermediation and serves as market-makers and agents. The emergence of new financial technology, commonly known as “FinTech”, is anticipated to revolutionize these industries. FinTech encompasses technological advancements in the financial sector to enhance consumer satisfaction, streamline operational procedures, create novel products, and foster greater competition.

Purpose: The primary aim is to discuss the banking industry’s current and potential future state with the emergence of financial technology. The specific objective is to explore the emergence of new-age technologies in the banking industry and the challenges faced in their implementation.

Methodology: A qualitative method is employed to analyze the FinTech landscape. Researchers conducted an extensive literature review and analyzed recent news articles to explore the banking industry’s digital transformation through FinTech. The literature review encompassed academic journals, industry reports, and relevant books to provide a theoretical framework and historical context to capture current developments, real-world applications, and emerging trends.

Findings: The evolution and impact of financial technology on the global economic landscape have been explored. It highlights how FinTech innovations, such as blockchain, artificial intelligence, and mobile payments, have revolutionized traditional banking, enhancing efficiency, accessibility, and security. It discusses regulatory challenges and the need for a balanced approach to foster innovation while ensuring consumer protection. The findings underscore FinTech’s potential to drive economic growth and the importance of continuous adaptation to evolving technological trends.

Consumer behavior is evolving rapidly due to the increasing role of technology in daily life. Online food ordering has emerged as a key channel in this changing landscape. This paper investigates the relationships between online promotions, consumer skepticism, information sharing on social media and the intention to purchase food and beverages through online delivery services.

Measures were developed based on a review of existing literature. Data from 402 participants were analyzed using Structural Equation Modeling (SEM).

The study reveals that online promotions significantly impact consumers' sharing of restaurant posts. Additionally, consumer skepticism about online food sales affects both their sharing behavior and their intention to purchase online. Engagement in sharing restaurant posts online is a strong predictor of online food purchasing intentions.

The findings offer valuable insights for restaurant operators, policymakers and technology developers in the competitive online food delivery sector. They emphasize the importance of implementing innovative promotions and crafting appealing food presentations. These strategies can accelerate customer decision-making, attract new customers and contribute to market expansion and customer base sustainability.

This research provides significant insights for restaurant owners and contributes to the limited literature on online promotions, consumer skepticism and information sharing in the restaurant industry. It also lays the groundwork for future studies aimed at deepening understanding in this field.

Aficionados of wine festivals, a component of wine tourism experience, engage in vigorous online discussions that influence fellow travelers’ purchase behaviors. This study aims to delve into these overlooked discussions, identifying emotions, topics and assessing their usefulness in TripAdvisor’s Travel Forums for two US wine festivals: Taste of Yountville and Epcot International Food and Wine Festival, located in traditional and nontraditional wine tourism destinations.

The study uses state-of-art sentiment analysis and topic modeling methods to extract emotions and underlying latent topics in travel forum discussions. Drawing from information theory, two regression analyses are performed on 10,677 forum posts to examine how the extracted Ekman’s emotions and key underlying topics influence the helpfulness of wine forum posts for each festival.

While three topics were identified in Epcot and four in Yountville, both festival platforms highlight travelers’ common preferences for “culinary experience” and “planning” attributes but reveal notable differences in their utility. Other shared novel findings include the importance of “anger” and “surprise” emotions on the helpfulness of forum posts.

These findings enhance wine festival managers’ and destination planners’ understanding of online travelers’ preferences and cognitive evaluation of user-generated contents’ usefulness. This marketing intelligence informs strategies for boosting the wine destination’s economic development.

This research offers a novel comparative analysis of social media on wine festival tourism experiences in diverse regions. Unlike hotel reviews, typically posted after consumption, forums offer unique and broader perspectives on discussions before, during, and after experiencing the wine festival.

This study aims to elucidate the dynamics of social media discourse during global health events, specifically investigating how users across different platforms perceive, react to and engage with information concerning such crises.

A mixed-method approach was employed, combining both quantitative and qualitative data collection. Initially, thematic analysis was applied to a data set of social media posts across four major platforms over a 12-month period. This was followed by sentiment analysis to discern the predominant emotions embedded within these communications. Statistical tools were used to validate findings, ensuring robustness in the results.

The results showcased discernible thematic and emotional disparities across platforms. While some platforms leaned toward factual information dissemination, others were rife with user sentiments, anecdotes and personal experiences. Overall, a global sense of concern was evident, but the ways in which this concern manifested varied significantly between platforms.

The primary limitation is the potential non-representativeness of the sample, as only four major social media platforms were considered. Future studies might expand the scope to include emerging platforms or non-English language platforms. Additionally, the rapidly evolving nature of social media discourse implies that findings might be time-bound, necessitating periodic follow-up studies.

Understanding the nature of discourse on various platforms can guide health organizations, policymakers and communicators in tailoring their messages. Recognizing where factual information is required, versus where sentiment and personal stories resonate, can enhance the efficacy of public health communication strategies.

The study underscores the societal reliance on social media for information during crises. Recognizing the different ways in which communities engage with, and are influenced by, platform-specific discourse can help in fostering a more informed and empathetic society, better equipped to handle global challenges.

This research is among the first to offer a comprehensive, cross-platform analysis of social media discourse during a global health event. By comparing user engagement across platforms, it provides unique insights into the multifaceted nature of public sentiment and information dissemination during crises.

This study aims to develop a halal procurement strategy for the halal pharmaceutical industry in Indonesia.

This qualitative research is based on in-depth interviews, followed by a strength, weakness, opportunity and threat analysis (using analytical network process technique) to develop halal procurement strategies for the halal pharmaceutical industry in Indonesia.

Access to halal-certified raw materials is the most difficult issue for pharmaceutical business procurement departments. Because Indonesia is heavily dependent on raw material imports, developing the raw material industry should be a top priority. Recommendations are drafted for the government, pharmaceutical industry and education to strengthen Indonesia’s halal pharmaceutical sector and procurement strategies.

Few studies have been conducted on halal procurement in the pharmaceutical industry, and case studies are recommended to further explore halal procurement best practices. Quantitative research is also recommended to better understand existing halal procurement strategies and the purchasing process of halal critical items for pharmaceutical industries in Muslim-majority countries like Indonesia.

Halal critical items sourced for the halal pharmaceutical industry require halal certification, multiple supplier sourcing, evaluation mechanisms and benefit from horizontal collaboration. The main bottleneck for the halal pharmaceutical industry is lack of raw materials with the right halal certificates.

To the best of the authors’ knowledge, this is the first study on halal procurement strategies in the pharmaceutical industry. Its findings are relevant to regulatory, technical and business strategies in Muslim-majority countries.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

This study aims to investigate the influence of inventors’ abilities to acquire external knowledge, provide broad and professional knowledge and patenting output (i.e. different types of inventors) on the formation of structural holes.

The authors collected 59,798 patents applied for and granted in the USA by 33 of the largest firms worldwide in the pharmaceutical industry between 1975 and 2014. A random-effects tobit model was used to test the hypotheses.

The inventors’ ability to acquire external knowledge contributes to the formation of structural holes. While inventors’ ability to provide broad knowledge positively affects the formation of structural holes, their ability to provide professional knowledge works otherwise. In addition, key inventors and industrious inventors are more likely to form structural holes than talents.

The results identify individual factors that affect the formation of structural holes and improve the understanding of structural hole theory. This study is unique in that most scholars have studied the consequences of structural hole formation rather than their antecedents. Studies on the origin of structural holes neglect the effect of inventors’ knowledge abilities and patenting output. By addressing this gap, this study contributes to a more comprehensive theoretical understanding of structural holes. The results can guide managers in managing structural holes in accordance with inventors’ knowledge abilities and patenting outputs, which optimize the allocation of network resources.