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This study aims to examine decision factors for global sourcing, differentiated into transcontinental and continental sourcing to obtain insight into locational aspects of sourcing decisions and global trends. This study analyzed various country perceptions to reveal their influence on sourcing decisions. The country of origin (COO) theory explains why certain country perceptions and images influence purchasing experts in their selection of suppliers.

This study used a two-study approach. In Study 1, the authors conducted discrete choice card experiments with 71 purchasing experts located in Europe and the USA to examine the importance of essential decision factors for global sourcing. Given the clear evidence that location is a factor in sourcing decisions, in Study 2 the authors investigated purchasers’ perceptions and images of countries, adding country ranking experiments on various perceived characteristics such as quality, price and technology.

Study 1 provides evidence that the purchasers’ personal relationship with the supplier plays a decisive role in the supplier selection process. While product quality and location impact sourcing decisions, the attraction of the buying company and cultural barriers are less significant. Interestingly, however, these factors seem as important as price to respondents. This implies that a strong relationship with suppliers and good quality products are essential aspects of a reliable and robust supply chain in the post-COVID-19 era. Examining the locational aspect in detail, Study 2 linked the choice card experiments with country ranking experiments. In this study, the authors found that purchasing experts consider that transcontinental countries such as Japan and China offer significant advantages in terms of price and technology. China has enhanced its quality, which is recognizable in the country ranking experiments. Therefore, decisions on global sourcing are not just based on such high-impact factors as price and availability; country perceptions are also influential. Additionally, the significance of the locational aspect could be linked to certain country images of transcontinental suppliers, as the COO theory describes.

The new approach divides global sourcing into transcontinental and European sourcing to evaluate special decision factors and link these factors to the locational aspect of sourcing decisions. To deepen the clear evidence for the locational aspect and investigate the possible influence of country perceptions, the authors applied the COO theory. This approach enabled authors to show the strong influence of country perception on purchasing departments, which is represented by the locational effect. Hence, the success of transcontinental countries relies not only on factors such as their availability but also on the purchasers’ positive perceptions of these countries in terms of technology and price.

To propose the use of indirect survey protocols, in general and the item count technique (ICT), in particular, that ensure participant anonymity in organizations to explore the effect of employee perceived abusive supervision on job performance.

We apply ICT to a sample of 363 employees (52.6% female) from Greek organizations. Utilizing multivariate statistical techniques, we investigated how employees assess the impact of their personal encounters with abusive supervision on job performance. This approach allowed us to explore the percentage of employees perceiving negative effects on job performance, distinguishing our study from previous studies that primarily focus on quantifying the extent or magnitude of abusive supervision in organizational settings. Also, we investigated how employee socio-demographic characteristics, human capital characteristics and affective traits relate to the evaluation of experienced abusive supervision as a negative factor for their job performance.

We found that approximately 62% of the respondents evaluated personal experience of abusive supervision as negatively affecting their job performance. We also found that the likelihood of employees evaluating personal experience of abusive supervision as having a negative impact on their job performance is: (1) higher for female employees, (2) does not depend on employee age, job tenure and education; (3) is lower for employees with managerial roles and (4) increases with employee trait negative affectivity.

The study is a response to the call for researchers to use innovative methods for advancing abusive supervision research. The study highlights the significance of taking a proactive stance towards addressing abusive supervision in the workplace, by using indirect survey methods that ensures employee anonymity. The results have implications for organizational strategies aimed at increasing awareness of abusive supervision and its impact on employee performance.

This paper aims to introduce the experience-dominant (Ex-D) logic model, which synthesizes the creation, perceptions and outcomes of Ex-D logic. It is designed to offer valuable insights for strategic managerial applications and future research directions.

Employing a qualitative approach by using eight selected product launch events from reviewed 100 event videos and 55 in-depth interviews with industrial managers to develop an Ex-D logic model, and data were coded and analysed via NVivo.

Results show that the firm’s Ex-D logic is operationalized as the mentalizing of the three types of customer needs (service competence, hedonic excitations and meaning making), the materializing of three types of customer experiences and customer journeys (service experience, hedonic experience and brand experience) and the moderating of three types of customer values (service values, hedonic values and brand values).

This study has implications for adding new insights into existing theory on dominant logic and customer experience management and also offers actionable recommendations for managerial applications.

This study sheds light on the importance of Ex-D logic from a strategic point of view and provides an organic view of the firm. It distinguishes firm perspective from customer perspective, firm experience from customer experience and firm journey from consumer journey.

Social media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. Content creators in the field, as well as researchers who study the impact of scientific information online, are interested in how people react to these information resources and how they judge them. This study aims to devise a framework for extracting large social media datasets and find specific feedback to content delivery, enabling scientific content creators to gain insights into how the public perceives scientific information.

To collect public reactions to scientific information, the study focused on Twitter users who are doctors, researchers, science communicators or representatives of research institutes, and processed their replies for two years from the start of the pandemic. The study aimed in developing a solution powered by topic modeling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media datasets in search of documents related to reactions to scientific communication. The architecture developed in this paper can be replicated for finding any documents related to niche topics in social media data. As a final step of our framework, we also fine-tuned a large language model to be able to perform the classification task with even more accuracy, forgoing the need of more human validation after the first step.

We provided a framework capable of receiving a large document dataset, and, with the help of with a small degree of human validation at different stages, is able to filter out documents within the corpus that are relevant to a very underrepresented niche theme inside the database, with much higher precision than traditional state-of-the-art machine learning algorithms. Performance was improved even further by the fine-tuning of a large language model based on BERT, which would allow for the use of such model to classify even larger unseen datasets in search of reactions to scientific communication without the need for further manual validation or topic modeling.

The challenges of scientific communication are even higher with the rampant increase of misinformation in social media, and the difficulty of competing in a saturated attention economy of the social media landscape. Our study aimed at creating a solution that could be used by scientific content creators to better locate and understand constructive feedback toward their content and how it is received, which can be hidden as a minor subject between hundreds of thousands of comments. By leveraging an ensemble of techniques ranging from heuristics to state-of-the-art machine learning algorithms, we created a framework that is able to detect texts related to very niche subjects in very large datasets, with just a small amount of examples of texts related to the subject being given as input.

With this tool, scientific content creators can sift through their social media following and quickly understand how to adapt their content to their current user’s needs and standards of content consumption.

This study aimed to find reactions to scientific communication in social media. We applied three methods with human intervention and compared their performance. This study shows for the first time, the topics of interest which were discussed in Brazil during the COVID-19 pandemic.

This paper aims to propose a stable and precise recognition and pose estimation method to deal with the difficulties that industrial parts often present, such as incomplete point cloud data due to surface reflections, lack of color texture features and limited availability of effective three-dimensional geometric information. These challenges lead to less-than-ideal performance of existing object recognition and pose estimation methods based on two-dimensional images or three-dimensional point cloud features.

In this paper, an image-guided depth map completion method is proposed to improve the algorithm's adaptability to noise and incomplete point cloud scenes. Furthermore, this paper also proposes a pose estimation method based on contour feature matching.

Through experimental testing on real-world and virtual scene dataset, it has been verified that the image-guided depth map completion method exhibits higher accuracy in estimating depth values for depth map hole pixels. The pose estimation method proposed in this paper was applied to conduct pose estimation experiments on various parts. The average recognition accuracy in real-world scenes was 88.17%, whereas in virtual scenes, the average recognition accuracy reached 95%.

The proposed recognition and pose estimation method can stably and precisely deal with the difficulties that industrial parts present and improve the algorithm's adaptability to noise and incomplete point cloud scenes.

Patriotism is often described as the “love of country” that individuals display in the acclamation of their national community. Despite the prominence of this sentiment in various societies around the world, organizational research on patriotism is largely absent. This omission is surprising because entrepreneurs, human resource (HR) divisions, and firms frequently embrace both patriotism and patriotic organizational practices. These procedures include (among other interventions) national symbol embracing, HR practices targeted toward military members and first responders, the adulation of patriots and celebration of patriotic events, and patriotic-oriented corporate social responsibility (CSR). Here, the authors argue that research on HR management and organization studies will likely be further enhanced with a deeper understanding of the national obligation that can spur employee productivity and loyalty. In an attempt to jumpstart the collective understanding of this phenomenon, the authors explore the antecedents of patriotic organizational practices, namely, the effects of founder orientation, employee dispersion, and firm strategy. It is suggested that HR practices such as these lead to a patriotic organizational image, which in turn impacts investor, customer, and employee responses. Notably, the effect of a patriotic organizational image on firm-related outcomes is largely contingent on how it fits with the patriotic views of other stakeholders, such as investors, customers, and employees. After outlining this model, the authors then present a thought experiment of how this model may appear in action. The authors then discuss ways the field can move forward in studying patriotism in HR management and organizational contexts by outlining several future directions that span multiple levels (i.e., micro and macro). Taken together, in this chapter, the authors introduce a conversation of something quite prevalent and largely unheeded – the patriotic organization.

To address the shortcomings of existing academic user information needs identification methods, such as low efficiency and high subjectivity, this study aims to propose an automated method of identifying online academic user information needs.

This study’s method consists of two main parts: the first is the automatic classification of academic user information needs based on the bidirectional encoder representations from transformers (BERT) model. The second is the key content extraction of academic user information needs based on the improved MDERank key phrase extraction (KPE) algorithm. Finally, the applicability and effectiveness of the method are verified by an example of identifying the information needs of academic users in the field of materials science.

Experimental results show that the BERT-based information needs classification model achieved the highest weighted average F1 score of 91.61%. The improved MDERank KPE algorithm achieves the highest F1 score of 61%. The empirical analysis results reveal that the information needs of the categories “methods,” “experimental phenomena” and “experimental materials” are relatively high in the materials science field.

This study provides a solution for automated identification of academic user information needs. It helps online academic resource platforms to better understand their users’ information needs, which in turn facilitates the platform’s academic resource organization and services.

This study aims to introduce an innovative approach to predictive maintenance by integrating time-series sensor data with event logs, leveraging the synergistic potential of deep learning models. The primary goal is to enhance the accuracy of equipment failure predictions, thereby minimizing operational downtime.

The methodology uses a dual-model architecture, combining the patch time series transformer (PatchTST) model for analyzing time-series sensor data and bidirectional encoder representations from transformers for processing textual event log data. Two distinct fusion strategies, namely, early and late fusion, are explored to integrate these data sources effectively. The early fusion approach merges data at the initial stages of processing, while late fusion combines model outputs toward the end. This research conducts thorough experiments using real-world data from wind turbines to validate the approach.

The results demonstrate a significant improvement in fault prediction accuracy, with early fusion strategies outperforming traditional methods by 2.6% to 16.9%. Late fusion strategies, while more stable, underscore the benefit of integrating diverse data types for predictive maintenance. The study provides empirical evidence of the superiority of the fusion-based methodology over singular data source approaches.

This research is distinguished by its novel fusion-based approach to predictive maintenance, marking a departure from conventional single-source data analysis methods. By incorporating both time-series sensor data and textual event logs, the study unveils a comprehensive and effective strategy for fault prediction, paving the way for future advancements in the field.

This paper aims to examine the impact of traditional banks’ financial technology (FinTech) adoption on corporate loan spreads and lending practices.

This study examines the impact of FinTech adoption by banks on corporate loan spreads and lending practices. By analyzing data from bank 10-K filings, we develop a novel metric to assess FinTech adoption at the individual bank level. Our analysis reveals a significant positive correlation between increased FinTech adoption and higher corporate loan spreads, particularly for loans that are relatively informationally opaque. This causality is further validated through a quasi-natural experiment. Additionally, we identify trends toward loans with smaller sizes and longer maturities in banks with advanced FinTech integration.

Using a sample of corporate loans issued from 1993 to 2020, this paper documents a significant positive relationship between a bank’s increased FinTech adoption and higher loan spreads. This correlation is especially noticeable for loans that are informationally opaque. Moreover, the paper reveals trends toward smaller loan sizes and longer maturities with advanced FinTech integration in banks. Overall, these findings indicate FinTech enhances efficiency in processing hard information and holds the potential to enhance financial inclusion.

This paper contributes to two significant strands of finance literature. First, it highlights how banks with advanced FinTech integration gain advantages through enhanced processing of hard information. Furthermore, it underscores the role of FinTech in promoting financial inclusion, particularly for those borrowers facing informational opacity.

In recent years, there has been significant interest in adopting XR (extended reality) technologies such as VR (virtual reality) and AR (augmented reality), particularly in retail. However, extending activities through reality-mediation is still mostly believed to offer an inferior experience due to their shortcomings in usability, wearability, graphical fidelity, etc. This study aims to address the research gap by experimentally examining the acceptance of metaverse shopping.

This study conducts a 2 (VR: with vs. without) × 2 (AR: with vs. without) between-subjects laboratory experiment involving 157 participants in simulated daily shopping environments. This study builds a physical brick-and-mortar store at the campus and stocked it with approximately 600 products with accompanying product information and pricing. The XR devices and a 3D laser scanner were used in constructing the three XR shopping conditions.

Results indicate that XR can offer an experience comparable to, or even surpassing, traditional shopping in terms of its instrumental and hedonic aspects, regardless of a slightly reduced perception of usability. AR negatively affected perceived ease of use, while VR significantly increased perceived enjoyment. It is surprising that the lower perceived ease of use appeared to be disconnected from the attitude toward metaverse shopping.

This study provides important experimental evidence on the acceptance of XR shopping, and the finding that low perceived ease of use may not always be detrimental adds to the theory of technology adoption as a whole. Additionally, it provides an important reference point for future randomized controlled studies exploring the effects of technology on adoption.