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Misperceptions hinder our ability to effectively respond to health crises such as the COVID-19. We aimed to examine the dynamic influences between information exposure, information trust and misperceptions during the early phase of the COVID-19 pandemic. Specifically, we focused on the relative influence of exposure to COVID-19-related information via social media versus interpersonal offline communication.

The current study conducted a two-wave national survey of US adults in May and June of 2020 with a two-week time interval. A professional polling firm recruited participants, and 911 and 679 respondents participated in the first and the second wave survey, respectively. To test proposed hypotheses, researchers conducted path analyses using AMOS 27.0.

Findings show that individuals exposed to COVID-19-related information via social media are likely to hold increased misperceptions. In contrast, exposure to COVID-19-related information offline did not elicit any effects on misperceptions. The exposure to information on social media was positively associated with trust in that information, which, in turn, contributed to an increase in misperceptions. Furthermore, when examining the effects of misperception, it was found that misperceptions increased the likelihood of individuals being exposed to and having trust in COVID-19-related information on social media. The findings provide valuable insights into the role of social media as a platform where a detrimental cycle thrives, shaping the formation of misperceptions and cultivating a heightened dependence among individuals with elevated misperceptions.

The current study significantly extends the findings of prior research by examining the differential effects of social media and interpersonal communication offline on misperception and by revealing the intricate dynamics between information exposure and misperception by focusing on the role of trust. The findings emphasize the detrimental role of social media in generating a vicious information cycle. That said, seemingly superficial discussions about health crises within a social media environment rich in misinformation can contribute to fueling a self-reinforcing loop, making it challenging to effectively counteract misperceptions.

This paper investigates the buy online and pick up in-store cooperation (BOPSC) of online and offline retailers. Specifically, this study solves the following questions: (1) What is the impact of BOPSC on their optimal price and sales volume of products? (2) When should an online retailer and an offline retailer conduct the BOPSC strategy with each other?

The paper first establishes two game models to explore the equilibriums of online and offline retailers in non-BOPSC and BOPSC. Then the condition for online and offline retailers to implement BOPSC strategy are determined. Furthermore, the applicability of the BOPSC strategy is enhanced by incorporating numerical analysis.

The study’s findings reveal that BOPSC strategy will not always beneficial to online and offline retailers, which depends on the total cost of online shopping and the product valuation of consumers. BOPSC strategy leads to the increase of prices and online orders, and the demand of offline retailer is eroded. Moreover, BOPS cooperation between different retailers is easier to achieve than omni-channel integration strategy. When the convenience difference between offline shopping and BOPSC pick-up is moderate, the effectiveness of BOPSC strategy can be improved.

This study has the following two main contributions: Firstly, the authors investigate the effects of BOPSC strategy on the prices of online and offline retailers. The study results show that the BOPSC strategy alleviates price competition and promotes a win–win situation between online retailers and offline retailers. Secondly, this paper mainly studies the cooperative behavior between online and offline retailers and reveals the optimal conditions for online and offline retailers to adopt BOPSC strategy. It can help small- and medium-sized online and offline retailers to choose suitable products for BOPSC strategy, so as to achieve the purpose of increasing profit.

An omni-channel is a retailing strategy that the behavior of companies adopts many retail channel types to combine and integrate cross-channel sales to meet the comprehensive needs of customers in shopping, entertainment and social networking both online and offline. This leads to several research questions of retailing omni-channel in this study. First, do channel brand trust (CBT) and store image (SI) affect re-patronage intentions through customer satisfaction (CS)? Second, regarding online rating (OR) and online involvement (OI), will CS be determined by consumer perception as well as the relevance of those attributes to the customer's re-patronage intentions? Third, do OR and OI have a role in exploring the moderating effect in the research model? Fourth, if there are positive effects from this relationship, it can generate a positive power return online. In addition, online-to-offline moves on a path for case firm omni-channel with customers' OR and OI with a reciprocal positive influence.

This study aims to investigate the relationship between CBT, SI, CS and re-patronize intention in an omni-channel. By using structural equation models (SEMs), this study aims to investigate offline-to-online and online-to-offline re-patronizing in the Taiwan Chunghwa Telecom (CHT) omni-channel (N = 1,642). Two moderated mediation models are investigated in this study.

This study first found that CBT and CS use SI to influence re-patronize intention in the omni-channel. SI plays a mediating role in this process; OR and OI influence the relationships of CBT, CS and re-patronize intention with two moderating roles.

This study first found that there is a reciprocal way with two paths in an omni-channel, starting with offline-to-online, in terms of confirming the relationship of CBT, CS and re-patronize intention in an omni-channel. There are positive effects from this relationship, and it can generate a positive power return online. In addition, we found that online-to-offline moves on a path that has a stronger influence of re-patronize intention for omni-channel with customers' OR and OI.

This study traces the boundaries of online-based social networks and its possible extensions and intersections with offline social networks. It focuses on the massive multiplayer online (MMO) gaming community. Most online gaming research has only addressed one side of the equation, that is, the online aspect of social interaction, omitting the offline context. The primary objective is to look at both offline and online social contexts of gamers. The data analyzed here are part of a bigger research project. Using a sample of 242 respondents and a total of 1,452 social ties (three online and three offline) this work addresses the differences and similarities between online gamer’s offline and online networks. Around 72% of the participants were between the ages of 18 and 37. This group provides insight into the management of social interactions and ties in the digital age among millennials and the coming-of-age of Generation Z. The analysis suggests that overall offline ties are slightly more important than online. Still, this does not imply that online ties are not meaningful at all. The length of their online relationships plays a significant role in how participants qualify their ties. Most participants that had not met face-to-face were willing to meet their online ties. They also reported sharing personal and everyday life matters with their online social network at a lower rate of their offline network. Time spent with online relationships stemming from online gaming and a cooperative environment is more likely to be considered higher quality time. This suggests that in MMOs the gap between online and offline relationships is becoming narrower.

This paper aims to examine why retail firms seldom achieve full integration of online and offline channels as prescribed in omni-channel literature. It examines the intermediate process of channel integration from an internal, operational perspective.

This study is composed of two parts. In the first part, the authors interviewed informants from nine firms that were engaged in channel integration. In the second part, the authors conducted case studies with three firms from the cosmetics and skincare industry against the backdrop of the COVID-19 pandemic to find evidence to support or negate the propositions made in the first part.

The first part identified six operational challenges to channel integration. The authors categorized these challenges into two groups: inter-channel communication and inter-channel competition. Inter-channel competition carries more weight at the latter stage of integration. The authors also identified two antecedents that affect the seriousness of these challenges: heterogeneity among channels in business operation and external competitive pressure. In the second part, the authors found that both inter-channel communication and inter-channel competition were improved because of the external competitive pressure exerted by the COVID-19 pandemic. However, the heterogeneity of offline channels against online channels in business operation is a double-edged sword.

The study identifies the changing effects of the challenges of channel integration and their antecedents in the midst of integration. The positive influence of a specific dimension of channel heterogeneity against other channels increases and then decreases along channel integration. The identification of the changing effects lays the foundation for a finer stage model of channel integration.

The paper attempts to discuss the impact of reference price effect on pricing decisions.

With the growth of the Internet and e-commerce, more and more customers purchase products in through online channels and choose products by comparing different prices and services, and the reference price effect has an impact on pricing decisions. To investigate the impact of consumers' reference price effect on the dual-channel supply chain, the authors establish a basic model consisting of a single dominant manufacturer and a single downstream retailer, and analyze the optional decisions under different situations and discuss the influence of reference price effect. Finally, a number case verifies the validity and rationality of the proposed model.

The results show that (1) the reference price effect has varying effects on the price, channel demand and income of manufacturers and retailers in the channel depending on the role of customers' channel preferences. (2) The manufacturer's online channel demand and profits always increase with the reference pricing effect, whereas the retailer's offline demand and profits always decline. (3) When the proportion of consumers preferring offline is higher, the manufacturer's network price and wholesale price increase with the reference price effect, while the retailer's retail price decreases with the reference price effect; when the proportion of consumers preferring offline is lower, the opposite is true, and the centralized decision results are consistent with the decentralized decision results.

This paper can clarify the impact of consumer reference price effects on the operation of dual-channel supply chains, and help inform pricing decisions of manufacturers and retailers in dual-channel supply chains.

The proposed approach can well analyze the impact of consumer reference price effect and give channel their optional decisions.

Informal learning networks are critical to response to calls for practitioners to reskill and upskill in off-site construction projects. With the transition to the coronavirus disease 2019 (COVID-19) pandemic, social media-enabled online knowledge communities play an increasingly important role in acquiring and disseminating off-site construction knowledge. Proximity has been identified as a key factor in facilitating interactive learning, yet which type of proximity is effective in promoting online and offline knowledge exchange remains unclear. This study takes a relational view to explore the proximity-related antecedents of online and offline learning networks in off-site construction projects, while also examining the subtle differences in the networks' structural patterns.

Five types of proximity (physical, organizational, social, cognitive and personal) between projects members are conceptualized in the theoretical model. Drawing on social foci theory and homophily theory, the research hypotheses are proposed. To test these hypotheses, empirical case studies were conducted on two off-site construction projects during the COVID-19 pandemic. Valid relational data provided by 99 and 145 project members were collected using semi-structured interviews and sociometric questionnaires. Subsequently, multivariate exponential random graph models were developed.

The results show a discrepancy arise in the structural patterns between online and offline learning networks. Offline learning is found to be more strongly influenced by proximity factors than online learning. Specifically, physical, organizational and social proximity are found to be significant predictors of offline knowledge exchange. Cognitive proximity has a negative relationship with offline knowledge exchange but is positively related to online knowledge exchange. Regarding personal proximity, the study found that the homophily effect of hierarchical status merely emerges in offline learning networks. Online knowledge communities amplify the receiver effect of tenure. Furthermore, there appears to be a complementary relationship between online and offline learning networks.

Proximity offers a novel relational perspective for understanding the formation of knowledge exchange connections. This study enriches the literature on informal learning within project teams by revealing how different types of proximity shape learning networks across different channels in off-site construction projects.

Research comparing offline and cyberbullying is relatively sparse, with scholars suggesting the need for empirical investigations to clarify whether cyberbullying and offline bullying are similar or different constructs.

Using an experimental vignette methodology, the current study of 163 working participants obtained via social media examines the effect of medium (offline vs cyberbullying), type (person-related vs work-related) and the interaction between medium and type on perceptions of definitional criteria (severity, frequency, power and intent) and outcomes (negative emotion, fairness, job satisfaction and turnover intention).

Significant differences between offline and cyberbullying were seen only for ratings of severity, job satisfaction and turnover intention, with cyberbullying perceived as more severe and as having a more detrimental impact on job satisfaction and turnover intention. Stronger effect sizes emerged for type of bullying, with person-related bullying having a stronger negative impact on definitional criteria and outcomes than work-related bullying. Moreover, interaction effects suggested differences between the two media were dependent on type of act – with person-related/cyberbullying acts seen more negatively than other acts.

This paper is the first to use a vignette approach to test the similarity or difference hypothesis between offline and cyberbullying. Overall, limited support is seen for the notion that offline bullying and cyberbullying are perceived as different constructs, with type of behaviour suggesting a more complex relationship between the two.

Offline reinforcement learning (RL) acquires effective policies by using prior collected large-scale data, while, in some scenarios, collecting data may be hard because it is time-consuming, expensive and dangerous, i.e. health care, autonomous driving, seeking a more efficient offline RL method. The purpose of the study is to introduce an algorithm, which attempts to sample the high-value transitions in the prioritized buffer, and uniformly sample from the normal experience buffer, improving sample efficiency of offline reinforcement learning, as well as alleviating the “extrapolation error” commonly arising in offline RL.

The authors propose a new structure of experience replay architecture, which consists of double experience replies, a prioritized experience replay and a normal experience replay, supplying samples for policy updates in different training phases. At the first training stage, the authors sample from prioritized experience replay according to the calculated priority of each transitions. At the second training stage, the authors sample from the normal experience replay uniformly. The combination of the two experience replies is initialized by the same offline data set.

The proposed method eliminates out-of-distribution problem in an offline RL regime, and promotes training by leveraging a new efficient experience replay. The authors evaluate their method on D4RL benchmark, and the results reveal that the algorithm can achieve superior performance over the state-of-the-art offline RL algorithm. The ablation study proves that the authors’ experience replay architecture plays an important role in terms of improving final performance, data-efficiency and training stability.

Because of the extra addition of prioritized experience replay, the proposed method increases the computational burden and has the risk of changing data distribution due to the combined sample strategy. Therefore, researchers are encouraged to use the experience replay block effectively and efficiently further.

Offline RL is susceptible to the quality and coverage of pre-collected data, which may be not easy to be collected from specific environment, demanding practitioners to handcraft behavior policy to interact with environment for gathering data.

The proposed approach focuses on the experience replay architecture for offline RL, and empirically demonstrates the superiority of the algorithm on data efficiency and final performance over conservative Q-learning across diverse D4RL tasks. In particular, the authors compare different variants of their experience replay block, and the experiments show that the stages, when to sample from the priority buffer, play an important role in the algorithm. The algorithm is easy to implement and can be combined with any Q-value approximation-based offline RL methods by minor adjustment.

A new business model online to offline (O2O) has emerged in recent years. Similar to many new models at an early stage, O2O has inconsistent definitions which not only inhibit its adoption but also poorly differentiate O2O from other existing business models. To resolve the two issues, the authors propose an approach of definition development.

To show the usefulness of the approach, the authors demonstrate the differences among O2O and other business models with the use of the distinctive definition and thereby evaluate adoption of O2O from a practical perspective and identify research directions from a theoretical perspective based on the differences.

The authors' proposed approach of definition development integrates the work of Tatarkiewicz (1980) and Nickerson et al. (2013). The approach generates a distinctive definition of O2O with important analytical dimensions which help decision-making of adoption of O2O.

The paper aims to make several contributions. First, on theoretical contribution, the authors confine the scope of O2O studies and facilitate accumulation of more coherent knowledge of O2O. The authors help O2O evolve from a “buzz word” of successful stories in real businesses to a more serious concept from an academic perspective. Second, from a practical perspective, the authors' definition provides business executives with critical evaluative dimensions for gauging the adoption of O2O. Lastly, from a methodological perspective, the proposed approach can be used in future to define an emerging concept in real life businesses.