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Frontline employees (FLEs) play a pivotal role in service delivery. Beyond their expected in-role behaviors, FLEs often have to perform extra-role behaviors such as providing additional help to customers. The purpose of this study is to investigate how customers’ power distance belief (PDB) influences their perceptions of FLEs’ warmth and competence when FLEs perform extra-role helping behaviors.

Four experiments were conducted to test the hypotheses. The first three experiments used a one factor two-level (PDB: low vs high) between-participants design. The fourth one used a 2 (PDB: low vs high) × 2 (firm reputation: low vs high) between-participants design.

The results indicate that, compared to high-PDB customers, low-PDB customers perceive greater warmth in FLEs’ extra-role helping behaviors but no significant difference in FLEs’ perceived competence. Importantly, these effects are mediated by customer gratitude. Moreover, these effects are moderated by firm reputation such that customers’ perceptions of FLEs’ warmth and competence are both enhanced when the firm has a favorable reputation.

To the best of the authors’ knowledge, the study is the first to identify the differential effects of PDB on customer perceptions of FLEs’ warmth and competence in the context of FLEs’ extra-role helping behaviors and to reveal the mediating role of gratitude. These findings contribute to the literatures on FLEs’ extra-role behaviors and social perceptions of both warmth and competence.

This paper aims to design a vision-based non-contact real-time accurate heart rate (HR) measurement framework for home nursing assistant.

The study applied Second-Order Blind Signal Identification (SOBI) algorithm to extract remote HR signal and analyzed it with Fast Fourier Transform (FFT). Multiple regions of interest are chosen and analyzed to obtain a more accurate result.

An accurate non-contact hear rate (HR) measurement framework is proposed and proved to be efficient.

The contributions of this HR measurement framework are as follows: accurate measurement of HR, real-time performance, robust under various scenes such as conversation, lightweight computation which is suitable and necessary for home nursing assistance. This framework is designed to be flexibly used in various real-life scenes such as domestic health assistance and affectively intelligent agents and is proved to be robust under such scenes.

The purpose of this study is to explore what is synergistic innovation in the logistics service outsourcing context and how this strategy can potentially benefit an organization by improving performance outcomes.

A total of 21 in-depth interviews were conducted to conceptualize the construct of synergistic innovation. In addition, a theory-based model is tested using data collected from 282 Chinese manufacturing firms that are currently engaged in logistics service outsourcing.

The results from the in-depth interviews reveal that synergistic innovation goes beyond just interactions on innovation but requires both parties to achieve synergies that otherwise cannot be achieved by individual party alone. In addition, the empirical analysis suggests that synergistic innovation can help firms improve both logistics service performance and market performance. This process is dynamic as knowledge integration capability influences the outcomes of synergistic innovation.

This study is among the first to conceptualize synergistic innovation. It also offers a practical scale that helps future studies effectively measure this construct. Moreover, the results of this study provide compelling insights into how managers can use logistics service outsourcing as an important source for improving innovation outcomes.

Theory is a kind of condensed human knowledge. This paper is to examine the mechanism of interdisciplinary diffusion of theoretical knowledge by tracing the diffusion of a representative theory, the Technology Acceptance Model (TAM).

Based on the full-scale dataset of Web of Science (WoS), the citations of Davis's original work about TAM were analysed and the interdisciplinary diffusion paths of TAM were delineated, a supervised machine learning method was used to extract theory incidents, and a content analysis was used to categorize the patterns of theory evolution.

It is found that the diffusion of a theory is intertwined with its evolution. In the process, the role that a participating discipline play is related to its knowledge distance from the original disciplines of TAM. With the distance increases, the capacity to support theory development and innovation weakens, while that to assume analytical tools for practical problems increases. During the diffusion, a theory evolves into new extensions in four theoretical construction patterns, elaboration, proliferation, competition and integration.

The study does not only deepen the understanding of the trajectory of a theory but also enriches the research of knowledge diffusion and innovation.

The study elaborates the relationship between theory diffusion and theory development, reveals the roles of the participating disciplines played in theory diffusion and vice versa, interprets four patterns of theory evolution and uses text mining technique to extract theory incidents, which makes up for the shortcomings of citation analysis and content analysis used in previous studies.

Country of origin (COO) effect refers to the influence of COO on consumers' perception and evaluation of a product. This research explores the impact of consumers' power distance on COO effect.

We conducted two experiments to test the relevant hypotheses.

The results indicate that power distance has a polarizing influence on COO effect. That means, for products from countries with good images, the higher the consumers' power distance, the better their evaluation of the products; while for products from countries with poor images, the higher the power distance, the worse their evaluation of the products. The research also finds the moderating effect of consumers' competence–related country-related affect (CRA). When holding positive competence–related CRA, for products from countries with good images, the higher the consumers' power distance, the better their evaluation of the products; for products from countries with poor images, consumers' power distance has no effect. When having negative competence–related CRA, for products from countries with poor images, the higher the consumers' power distance, the worse their evaluation of the products; for products from countries with good images, power distance has no effect.

This study finds that depending on the perception of COO image, power distance not only improves the evaluation of products but also lows such evaluation, reflecting a two-way polarizing feature.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

The purpose of this study is to investigate the effect of cross-border acquisitions on brand image dimensions (functional, symbolic and global image) of the acquirer brand from a consumer’s perspective.

The authors measured Chinese consumers’ perceptions of eight fictitious cross-border acquisition scenarios and tested the hypotheses by using multiple hierarchical regression.

First, the acquisition significantly improves functional, symbolic and global image of the acquirer brand. Second, both image perceptions of the acquirer and the acquired brands before acquisition significantly impact post-image of the acquirer. The effect is greater for pre-image of the acquirer (dominance effect). Finally, brand fit, product fit and country-of-origin fit influence attitude toward the acquisition significantly.

There are limitations in the generalizations of the findings due to its reliance on a single country (China) and one industry (home appliances).

First, engaging in cross-border acquisitions significantly enhances the brand image of the acquirer brand. The global image has the largest improvement. Second, practitioners should carefully consider different levels of fit before the acquisition.

This study contributes to the extant literature by investigating brand acquisitions from the perspective of home country consumers (acquirer) and integrating multiple brand image dimensions and various levels of fit simultaneously.

The purpose of this paper is to investigate the impact of Federal Open Market Committee (FOMC) meetings and the changes of the target rates on stock market uncertainty.

Multivariate regression analysis is applied to the historical data of VIX, FOMC meetings and target rates. Subtle relations are revealed by further categorizing the FOMC meetings into being scheduled and unscheduled and distinguishing the signs of the changes in VIX and target rates. CPI and the prime rate are used for robustness test.

The authors first examine the relation between FOMC meetings and target surprises; the results indicate that unscheduled FOMC meetings heavily impact the target surprises. Then, the authors investigate the relation between FOMC meetings and VIX changes; the results show that both unscheduled and scheduled FOMC meetings impact VIX, where the impacts of scheduled FOMC meetings are more substantial. The authors also analyze the responses of VIX to the target surprises, and the results reveal that there is an asymmetric effect of target surprises on VIX, where the influences of the scheduled positive target surprises are more significant. Finally, by examining the relation between the FOMC meeting and the risk-neutral density of the VIX option, the authors conclude that both KURT and SKEW are more affected by unscheduled FOMC meetings.

Deeper dimensions of the relations between VIX, FOMC meetings and target rates are analyzed and more insightful understandings of such relations are gained.

The new Silk Road provides cross-border-e-commerce firms with an opportunity to widen their markets. Under this circumstance, the preference recognition of countries and inventory allocation among overseas warehouses both become critical issues to solve. Three Chinese smartphone brands, including HTC, Huawei, and MI, are selected in this chapter for their relatively enormous sales. DHgate and AliExpress websites are chosen as platforms to analyze the sales for data availability. This chapter first depicts key features of the sales and then, based on which, divide countries into several groups according to their preference for phones by cluster analysis. Then, based on the results of cluster analysis, this chapter further models the inventory assignment among the seven major overseas warehouses that were built by AliExpress in 2015. The results show that the HTC seems to be pursuing the “high value with high price” strategy, while the other two companies seem to be pursuing a hybrid strategy of “low-price” strategy and “high value with low price” strategy. This chapter also provides an assignment pattern of inventory among the overseas warehouses based on the real data of sales and costs.