The Influence of Online Social and Physical Presence on User Consumption Decisions in TikTok Livestreaming: A Scoping Review.

This scoping review aimed to clarify and redefine the concepts of social and physical presence in the context of online livestreaming environments. Physical presence involves technical elements and nonhuman-to-human interaction, factors that inevitably influence social presence, which has human-to-human interactions at its core. Considering one type of presence to the exclusion of the other may not provide sufficiently informed decisions for user consumption. However, most previous studies have only studied either physical or social presence factors; few have systematically examined both to explain their influence. This review examined how the factors under these two presence influenced users' consumption decision-making process in TikTok live-stream retail purchases by synthesizing 60 studies conducted from 2019 to 2023 using Arksey and O'Malley's 5-step framework. Evidence of the specific attributes by which presence affects users' consumption decisions was elicited and reorganized. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework for scoping review and guided by the joanna briggs institute (JBI) methodological guidelines, the results reveal that out of the 60 studies, 36 were influenced by social presence and 24 by physical presence. When livestreaming online, social presence tends to include the user and anchor perspectives, which prompts users to make consumption decisions. However, online physical presence includes products, technology, and scenes as its main dimensions, and users make consumption decisions through perceptual control. This review clarifies new media livestreaming communication and the key factors influencing users' consumption decision-making systems. It also suggests that integrating online social and physical presence in future research will yield a better understanding of livestreaming purchase decisions.

Who's watching? Classifying sports viewers on social live streaming services.

The newly emergent social live streaming services (SLSSs) provide the sport consumers with a synchronised and more interactive viewing experience. In order to help the sport SLSSs firms understanding and engaging with the viewers effectively, this research aims to classify the sports SLSS viewers based on their engagement behaviour, and identify the perceived value and value contribution of each group of viewers. Firstly, 52,545 sports SLSSs viewers' viewing duration time is predicted by a feedforward neural network. Second, the predicted viewing duration time and other extracted viewer behavioural data (number of messages, number of virtual gifts, and value of virtual gifts) are analysed through two-step clustering in SPSS, and classified viewers into four types. Semi-structured interviews were then conducted to understand how each type of viewer co-creates value. The results identified four groups of viewers, namely content consumers, super co-creators, co-creators, and tourists, and identified their distinct value co-creations and perceived value. This study sheds light on combining engagement behaviour and value co-creation literature to classify the sports viewers in the context of SLSSs. This understanding assists the decision-making processes of marketers and operators to promote viewers' co-creation effectively.

Decision optimization in cooperation innovation: the impact of big data analytics capability and cooperative modes.

Data-driven innovation enables firms to design products that are more responsive to market needs, which greatly reduces the risk of innovation. Customer data in the same supply chain has certain commonality, but data separation makes it difficult to maximize data value. The selection of an appropriate mode for cooperation innovation should be based on the particular big data analytics capability of the firms. This paper focuses on the influence of big data analytics capability on the choice of cooperation mode, and the influence of their matching relationship on cooperation performance. Specifically, using game-theoretic models, we discuss two cooperation modes, data analytics is implemented individually (i.e., loose cooperation) by either firm, or jointly (tight cooperation) by both firms, and further discuss the addition of coordination contracts under the loose mode. Several important conclusions are obtained. Firstly, both firms' big data capability have positive effects on the selection of tight cooperation mode. Secondly, with the improvement of big data capability, the firms' innovative performance gaps between loose and tight mode will increase significantly. Finally, when the capability meet certain condition, the cost subsidy contract can alleviate the gap between the two cooperative models.

Investigating the relationship between digital technologies, supply chain integration and firm resilience in the context of COVID-19.

The outbreak of the COVID-19 pandemic has significantly augmented the complexity of information, adding to the challenges that firms face in effectively processing and grasping accurate information. As a result, the production uncertainty of firms has been seriously intensified during the pandemic, disrupting the normal operation of firms and their supply chains. Digital technologies serve as salient tools that help firms to process and analyse information, consequently enhancing firm resilience in the face of supply chain disruptions. This study aims to examine how digital technologies affect firm resilience in the context of COVID-19 through the lens of information processing theory and a large-scale survey conducted among Chinese manufacturers. Specifically, our study evaluates the mediating effect of supply chain integration (internal integration, customer integration and supplier integration) and the moderating effect of information complexity. The results show that supply chain integration plays a mediating role in the effect of digital technologies on firm resilience, and the mediation effect is particularly significant for customer integration. Furthermore, digital technologies have a stronger impact on firm resilience when information complexity is high. The findings advance our understanding and recognition of the resilience implications of digital technologies and provide important managerial implications for improving firm resilience in the context of COVID-19.

Speak like a Native English Speaker or Be Judged: A Scoping Review.

This review aims to examine the discrimination and prejudices toward the accent of non-native English speakers and cyberbullying as the ripple effect of these negative consequences. Following Arksey and O'Malley's framework of conducting a scoping review, 60 studies from 2012 to 2021 were retrieved from the ERIC and Google Scholar databases. The studies were reviewed from two aspects: (1) psychological impact on speakers with a non-native English accent, (2) attitudes toward non-native English accents from the victim's and perpetrator's perspectives. The findings suggested that speaking with a non-native English accent drew negative cognitive, affective, and behavioral experiences. Biases toward non-native English accents were due to the general derogatory perception of an accent and the comprehensibility of speakers' accent and pronunciation. "Accent acceptability" can be inculcated at all levels of education, not only through multicultural education but also through the concerted effort of policy makers and practitioners to seriously address this social issue. Accent awareness can dispel unwarranted and undesirable judgements of non-native English accent speakers. Future studies should be conducted on the effects of social and mental health experiences, particularly of non-native ESL and EFL teachers, given that this may be the only profession required to teach "live" during the pandemic and thus be subjected to public praise or ridicule.

Artificial intelligence-based human-centric decision support framework: an application to predictive maintenance in asset management under pandemic environments.

Pandemic events, particularly the current Covid-19 disease, compel organisations to re-formulate their day-to-day operations for achieving various business goals such as cost reduction. Unfortunately, small and medium enterprises (SMEs) making up more than 95% of all businesses is the hardest hit sector. This has urged SMEs to rethink their operations to survive through pandemic events. One key area is the use of new technologies pertaining to digital transformation for optimizing pandemic preparedness and minimizing business disruptions. This is especially true from the perspective of digitizing asset management methodologies in the era of Industry 4.0 under pandemic environments. Incidentally, human-centric approaches have become increasingly important in predictive maintenance through the exploitation of digital tools, especially when the workforce is increasingly interacting with new technologies such as Artificial Intelligence (AI) and Internet-of-Things devices for condition monitoring in equipment maintenance services. In this research, we propose an AI-based human-centric decision support framework for predictive maintenance in asset management, which can facilitate prompt and informed decision-making under pandemic environments. For predictive maintenance of complex systems, an enhanced trust-based ensemble model is introduced to undertake imbalanced data issues. A human-in-the-loop mechanism is incorporated to exploit the tacit knowledge elucidated from subject matter experts for providing decision support. Evaluations with both benchmark and real-world databases demonstrate the effectiveness of the proposed framework for addressing imbalanced data issues in predictive maintenance tasks. In the real-world case study, an accuracy rate of 82% is achieved, which indicates the potential of the proposed framework in assisting business sustainability pertaining to asset predictive maintenance under pandemic environments.

Supply chain resilience reactive strategies for food SMEs in coping to COVID-19 crisis.

BACKGROUND: The ability of small- and medium-sized enterprises in the food industry (FSMEs) in cultivating resilience against the COVID-19 pandemic is vital food security. However, there is limited supply chain resilience literature to guide FSMEs in overcoming disruptions caused by pandemic. SCOPE AND APPROACH: This review aims to provide a broad view of SCRes reactive strategies for FSMEs in dealing with crises in the context of COVID-19. Attention is given to the literature on resilience in other types of supply chain and situated in the context of food settings. The factors are monitored or controlled to contribute to FSME resiliency.Key findings and conclusion: Four quadrants, i.e., (1) rapid with low cost, (2) rapid with high cost, (3) slow with low cost and (4) slow with high cost, are offered based on the limitations and the time needed to react, and the strategies of each quadrant are explained in depth. This review also provides a better understanding of and guidance on reactive strategies for SCRes as options for FSMEs in dealing with the COVID-19 pandemic. This review suggests future directions as extensions based on the logical flow of this review.

An intelligent payment card fraud detection system.

Payment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems.

Infection vulnerability stratification risk modelling of COVID-19 data: a deterministic SEIR epidemic model analysis.

Basic Susceptible-Exposed-Infectious-Removed (SEIR) models of COVID-19 dynamics tend to be excessively pessimistic due to high basic reproduction values, which result in overestimations of cases of infection and death. We propose an extended SEIR model and daily data of COVID-19 cases in the U.S. and the seven largest European countries to forecast possible pandemic dynamics by investigating the effects of infection vulnerability stratification and measures on preventing the spread of infection. We assume that (i) the number of cases would be underestimated at the beginning of a new virus pandemic due to the lack of effective diagnostic methods and (ii) people more susceptible to infection are more likely to become infected; whereas during the later stages, the chances of infection among others will be reduced, thereby potentially leading to pandemic cessation. Based on infection vulnerability stratification, we demonstrate effects brought by the fraction of infected persons in the population at the start of pandemic deceleration on the cumulative fraction of the infected population. We interestingly show that moderate and long-lasting preventive measures are more effective than more rigid measures, which tend to be eventually loosened or abandoned due to economic losses, delay the peak of infection and fail to reduce the total number of cases. Our calculations relate the pandemic's second wave to high seasonal fluctuations and a low vulnerability stratification coefficient. Our characterisation of basic reproduction dynamics indicates that second wave of the pandemic is likely to first occur in Germany, Spain, France, and Italy, and a second wave is also possible in the U.K. and the U.S. Our findings show that even if the total elimination of the virus is impossible, the total number of infected people can be reduced during the deceleration stage.