Robot-delivered tourism and hospitality services: How to evaluate the impact of health and safety considerations on visitors' satisfaction and loyalty?

Due to the COVID-19 epidemic, visitors' worries about health and safety in tourist destinations have become paramount. Consequently, this research aims to evaluate the impact of health and safety considerations on visitors' satisfaction. It also examines the influence of health consciousness and satisfaction on visitors' willingness toward robot-delivered tourism and hospitality services usage and the impact of destination healthcare system and satisfaction on loyalty intentions. Applying a quantitative-based methodology, 650 responses were collected from domestic tourists visiting Egyptian tourism destinations using multiple non-probability sampling techniques. Using PLS-SEM, the results articulated that emotional well-being, perceived safety, and perceived green image positively impacted visitors' satisfaction, which in turn, positively affected their willingness toward service robot's usage and loyalty. Tourists' health consciousness also positively affected their satisfaction and intentions to use robot-delivered services. Additionally, destination healthcare system significantly influenced visitors' satisfaction and loyalty intentions. Theoretical and managerial contributions as well as future research are outlined.

Antecedents and moderators of positive word of mouth communication among tourist destination residents during the COVID-19 pandemic

Word of mouth (WOM) communication has been recognized for a long time as an activity to improve the efficiency and success of communication efforts. The purpose of this paper is to examine potential antecedents and moderators of positive WOM of tourist destination residents during the COVID-19 pandemic. Three WOM predictors are examined, i.e. trustworthiness in communication of authorities, destination reputation, and prevention measures, whereas two sociodemographic variables are considered as moderators, i.e. gender and age, and more specifically, generational cohorts. Empirical research has been conducted among residents of a tourist destination, considered important stakeholders who actively participate in tourist destination branding. Data were collected through an online survey distributed through e-mail marketing lists and online panels. After obtaining 480 valid responses, data were subject to normality tests, Partial Least Squares-Structural Equation Modeling (PLS-SEM), measurement invariance assessment, and multi-group analysis. Findings reveal that residents' positive WOM is caused by their perceptions of destination reputation and adoption measures, being the role of trustworthiness in communication of authorities insignificant in residents' WOM engagement. Among the two sociodemographic variables, only gender is found to moderate one relationship (i.e. reputation-WOM), with stronger effects among women. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

Analysis by Canonical Correlations of Labor Activity in Mi- Lagro City during Covid-19 Pandemic

The labor activity of Milagro city is preceded in many cases by agriculture, representing at the same time as the only source of income for the large number of people who inhabit these areas, which has also been affected to some extent by the appearance of the covid-19 virus, for this reason the possibility of analyzing the degree of influence of this situation on the labor sector is raised. In this research work, the statistical methodology of Analysis by Canonical Correlations was used, to a set of variables located in two different periods of time on the people who exercise some activity within the labor field, where the difficulties that they present are highlighted re-flected mainly in the delicate economic situation that they demonstrated to have. In addition, the collection of different points of view summarizes how complica-ted it is to be able to get a good workplace, which provides them with better benefits, given that in most cases their work is compromised by the variability of working hours, the lack of insurance, and the cons-tant challenges of living in rural areas.

The impact of COVID-19 on route-level changes in transit demand an analysis of five transit agencies in Florida, USA

This study examined the impacts of COVID-19 on changes in route-level transit demand across five transit agencies in the state of Florida. Data for 120 routes from five transit agencies were used to develop two-stage instrumental variable models. Data from January of 2019 to December of 2020 were used in the analysis. Routes that served a greater mix of land-uses experienced a smaller decline in ridership. The impacts of several other land-use variables were, however, not consistent across the five transit agencies. Fare suspension was estimated to have a positive impact on ridership. In contrast, occupancy reduction measures (to promote social distancing within the transit vehicle) had a very strong negative impact on demand. The magnitude of the negative impact of occupancy reduction was larger than the positive impacts of fare suspension. Extending this analysis to a larger set of routes across more agencies would be useful in enhancing the robustness of the findings from our models. Extending our analysis to include data from 2021 and later to capture the recovery phase is also an important direction for future work. © 2022

Word embeddings for topic modeling: An application to the estimation of the economic policy uncertainty index

The quantification of economic uncertainty is key to the prediction of macroeconomic variables, such as gross domestic product (GDP), and is particularly crucial in regard to real-time or short-time prediction methodologies, such as nowcasting, where a large amount of time series data is required. Most of the data comes from official agency statistics and non-public institutions, but these sources are susceptible to lack of information due to major disruptive events, such as the COVID-19 pandemic. Because of this, it is very common nowadays to use non-traditional data from different sources. The economic policy uncertainty (EPU) index is the indicator most frequently used to quantify uncertainty and is based on topic modeling of newspapers. In this paper, we propose a methodology to estimate the EPU index that incorporates a fast and efficient method for topic modeling of digital news based on semantic clustering with word embeddings, allowing us to update the index in real time, which is something that other studies have failed to manage. We show that our proposal enables us to update the index and significantly reduce the time required for new document assignation into topics. © 2022 Elsevier Ltd

Does employee empowerment moderate the effect of situational and dispositional variables on emotional exhaustion differently? The COVID-19 crisis context

Purpose: Building on the conservation of resources (COR) theory, this study aims to investigate employee empowerment's moderation effect on the relationship of situational (job satisfaction, affective commitment) and dispositional (positive affectivity, emotional intelligence) variables toward the emotional exhaustion of service employees amidst the pandemic. Design/methodology/approach: In total, 288 service employees from various sectors in Indonesia participate as the study's respondents. This study applies a two-stage structural equation modeling approach to test the hypotheses. Findings: The results show that employee empowerment moderates situational and dispositional variables differently. While employee empowerment significantly influences situational variables, a different situation is found on dispositional variables, that employee empowerment does not significantly influence these variables. This study's findings portray the COR theory in practice and clarify the importance of employee empowerment for employees with particular attributions. Research limitations/implications: The present study bears four limitations: the cross-sectional design;no exploration of dispositional and situational variables' antecedents;the findings are limited to the service workers;and lastly, this study only takes Indonesian samples. Practical implications: From a practical perspective, this study reveals which type of service employees are responsive to empowerment policy and which are prone to experience emotional exhaustion, particularly during a crisis. Social implications: By understanding what factors determine employee empowerment's effectiveness, managers could maximize the impacts of their empowerment policies. Subsequently, it will create better service deliveries which might benefit the broader societal scope. Originality/value: This study contributes to both theoretical and practical understanding. Theoretically, this study adds and promotes using a categorical lens to examine the pattern of interactions between organizations and employees. © 2022, Jaya Addin Linando and M. Halim.

Augmenting commercial banks' other income through off-balance sheet activities in relation to their determinants in the Indian banking system

COVID-19 has a devastating impact on the global economy, particularly on robustness and resilience of emerging and developing economies' (EMDE's) economic-cum-financial systems. Reinventing banking practices with strategies are indispensable for sustainable growth. EMDEs like India have distinct country-specific business models. We aim to devise a sustainable model for augmenting banks' other income;analyzing off-balance sheet (OBS) activities in India, which may be applied in EMDEs' efficacy. We apply least-squares dummy variables and ordinary least squares models for fixed-effect regression analysis on OBS from 1996-2019. Regulatory determinants like capital adequacy, net non-performing assets, liquidity have more significant impact on OBS than bank-specific variables like bank size or macroeconomic like GDP. OBS can generate revenue is exemplified by strong relation to other income. Findings reveal that while assessing impact of COVID-19 on-balance sheets, banks should prioritize capital and contingency liquidity planning, focusing on OBS activities to augment other income in the revival strategy. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

Urban green spaces management during the COVID-19 pandemic: Experiences from Kraków, Poland

SARS-CoV-2 (COVID-19) has spread all over the world, significantly affecting our everyday lives. People changed their habits during the pandemic and made use of urban green spaces (UGS). Our Web of Science and Scopus queries confirm a knowledge gap in green space planning and public space management studies in the field of UGS availability during COVID-19. Therefore, the purpose of our research was to determine the impact of the COVID-19 pandemic on urban green spaces management, identify the needs of the residents in terms of urban green spaces furniture, and assess the accessibility of urban green spaces to propose recommendations for the institution charged with managing urban green spaces in the City (Krakow). To this end, we used an expert interview, spatial analyses, and survey research among residents of Kraków, one of the largest cities in Poland. The survey involved 1350 respondents. The spatial analyses employed geoprocessing algorithms and GIS tools. The results showed that 96% of Kraków citizens have access to urban green spaces within 300 m from their homes. Therefore, UGS are an important part of the City's identity, making their reasonable management vital, especially during crises. The respondents indicated that the existing UGS needed more lighting, rubbish bins, and benches as places of respite. Results of the expert interview showed that the COVID-19 pandemic has affected urban green spaces management. The results may influence urban green spaces management, and the research is an innovative combination of spatial analysis, a qualitative approach (expert interview), and a quantitative method (a survey) proposing new procedures for analysing UGS. © 2022 John Wiley & Sons Ltd.

The nexus between oil and airline stock returns: Does time frequency matter?

This paper investigates the relationship between oil and airline stock returns under different time frequencies. First, we propose an Autoregressive moving average model with mixed frequency exogenous variable to analyse the different impacts of oil on airline stock returns on daily, weekly, and monthly basis. We consistently find a negative oil-airline stock return nexus on a daily basis, but a positive relationship on a weekly basis. While the former supports the economic-based channel, the latter is in line with the market inertia channel. Our findings help explain mixed results reported in the literature. Further, our time frequency connectedness analysis shows that the economic-based channel dominates the market inertia channel since the connectedness is more pronounced in the short-run compared to the medium- and long-run. Our block connectedness results highlight that business models of airline firms can play a significant role in affecting the connectedness, in which the low-cost airlines are more sensitive to the oil price changes. It is worth noting that there are distinguished drivers of the oil-airline stock return nexus in different time frequencies. The drivers also vary between the Global Financial Crisis and the COVID-19 pandemic. Our results are consistent under a battery of robustness checks and deliver important implications to investors, portfolio managers, and executives of airline firms. © 2022 Elsevier B.V.

Decomposing energy poverty in three components

In this paper, we decompose a family of energy poverty indices into incidence, intensity and inequality among the poor. When continuous variables such as income are used, it is well known that any income poverty index should take into account these three poverty terms. Therefore, every energy poverty measure should also take into account these three. We propose to follow an analogous idea to decompose a family of energy poverty indices in these three components. The novelty of this proposal is that the multidimensional family of energy poverty measures is applied to multiple dichotomous variables and not to a single continuous variable, when, to date, no decomposition has been proposed based on these types of variables. We then extend this decomposition to a further decomposition by population subgroups. In this way, we will be able to know more precisely the origins of energy poverty in order to implement more specific anti-poverty policies targeting the incidence, intensity or/and inequality among the energy poor. Finally, we apply the proposed decomposition to Spain in 2020 as regards certain individual and household characteristics, so as to identify the individuals most likely to suffer from energy poverty in the first year of the Covid-19 pandemic. © 2022

A Two-Step Algorithm to Estimate Variable Importance for Multi-State Data: An Application to COVID-19

Survival data with a multi-state structure are frequently observed in follow-up studies. An analytic approach based on a multi-state model (MSM) should be used in longitudinal health studies in which a patient experiences a sequence of clinical progression events. One main objective in the MSM framework is variable selection, where attempts are made to identify the risk factors associated with the transition hazard rates or probabilities of disease progression. The usual variable selection methods, including stepwise and penalized methods, do not provide information about the importance of variables. In this context, we present a two-step algorithm to evaluate the importance of variables for multi-state data. Three different machine learning approaches (random forest, gradient boosting, and neural network) as the most widely used methods are considered to estimate the variable importance in order to identify the factors affecting disease progression and rank these factors according to their importance. The performance of our proposed methods is validated by simulation and applied to the COVID-19 data set. The results revealed that the proposed two-stage method has promising performance for estimating variable importance. © 2023 Tech Science Press. All rights reserved.

Women's behaviours towards cervical cancer screening in the COVID-19 pandemic: A moderated-mediation-model based on Information-Motivation-Behavioural Skills.

AIMS: (1) To establish a predictive model based on the Information-Motivation-Behavioural Skills model, which can analyse the factors affecting the behaviours of women towards cervical cancer screening in the COVID-19 pandemic, and (2) to test the mediating effects of behavioural skills in the model, and (3) to test the moderated mediation effect of age. DESIGN: A cross-sectional study was conducted among 354 women aged 30-65 between May and August 2021 in Turkey. METHODS: Data were collected by using an online survey. The direct and indirect effects were tested in the structural equation model and the moderated-mediation effect was tested in the PROCESS macro. RESULTS: Behavioural skills mediate the effect of motivation on cervical cancer screening behaviours. In addition, age has a moderated mediation effect on this mediation effect. CONCLUSION: Our study revealed that as women's motivation for cervical cancer screening increased, their behavioural skills also increased. It can be stated that middle-aged and older women with higher behavioural skills are more likely to have screening during the pandemic and to comply with national recommendations. IMPACT: This study is the first quantitative study to test the impact of the components of the Information-Motivation-Behavioural Skills model on cervical cancer screening during the COVID-19 pandemic. In addition, the results reveal the mediating effect of behavioural skills in the relationship between motivation and cervical cancer scanning behaviour and the moderated mediation effect of age. Our results can provide insight for nurses into how to triage women with delayed cervical cancer screening, how to build screening capacity, and how intervention strategies should be developed to improve compliance with cervical cancer screening and follow-up recommendations in women at risk during and after the pandemic.

Statistical modelling of the impact of online courses in higher education on sustainable development

Purpose>The concept of sustainable development (SD) is a popular response to society's need to preserve and extend the life span of natural resources. One of the 17 goals of the SD is "education quality” (Fourth Goal of Sustainable Development [SDG-4]). Education quality is an important goal because education is a powerful force that can influence social policies and social change. The SDG-4 must be measured in different contexts, and the tools to quantify its effects require exploration. So, this study aims to propose a statistical model to measure the impact of higher education online courses on SD and a structural equation model (SEM) to find constructs or factors that help us explain a sustainability benefits rate. These proposed models integrate the three areas of sustainability: social, economic and environmental.Design/methodology/approach>A beta regression model suggests features that include the academic and economic opportunities offered by the institution, the involvement in research activities and the quality of the online courses. A structural equation modelling (SEM) analysis allowed selecting the key variables and constructs that are strongly linked to the SD.Findings>One of the key findings showed that the benefit provided by online courses in terms of SD is 62.99% higher than that of offline courses in aspects such as transportation, photocopies, printouts, books, food, clothing, enrolment fees and connectivity.Research limitations/implications>The SEM model needs large sample sizes to have consistent estimations. Thus, despite the obtained estimations in the proposed SEM model being reliable, the authors consider that a limitation of this study was the required time to collect data corresponding to the estimated sample size.Originality/value>This study proposes two novel and different ways to estimate the sustainability benefits rate focused on SDG-4, and machine learning tools are implemented to validate and gain robustness in the estimations of the beta model. Additionally, the SEM model allows us to identify new constructs associated with SDG-4.

Tpack, Organizational Support, and Technostress in Explaining Teacher Performance during Fully Online Learning

Aim/Purpose This study aims to analyze (1) the effect of organizational support on Techno-logical Pedagogical Content Knowledge (TPACK), (2) the effect of organiza-tional support and TPACK on teacher performance, (3) the effect of organiza-tional support and TPACK on technostress, and (4) the effect of technostress on teacher performance.Background The disruption of Information Technology (IT) innovation in educational prac-tice happened two decades ago. However, the more massive and intense IT inte-gration in teaching and learning practice was demanded during the COVID-19 pandemic. These circumstances made teachers and students face a new teaching and learning environment with complete IT mediation. Therefore, they will show a unique response valuable for managing effective education and further research regarding teaching and learning in the online environment.Methodology Using a purposive sampling technique, data was collected from 419 pre-service teachers in the economics and business field. The data was then tabulated and analyzed using PLS-SEM.Contribution This study connects the concept of TPACK as knowledge to organizational support and technostress as the organizational and personal response to deal with massive IT integration in fully online learning during the COVID-19 pan-demic. This study bridges the educational concept of teacher competence to the behavioral framework of IS users to deal with the online environment. Teach-ing and learning are tasks that engage human -to-human interaction, which is different from other productive activities like the business sector. Therefore, this study may give fruitful findings, both theoretically and practically, to im-prove educational practice in this digital age.Findings Researchers found that organizational support and TPACK were valuable ante-cedents of teacher performance in an online environment. At the same time, technostress is not a critical threat to teacher performance. However, tech-nostress exists among teachers and is uncontrollable by TPACK and organiza-tional support. Researchers argue it is an unavoidable circumstance. The educa-tional system demands a rapid shift to fully online learning due to the COVID-19 pandemic. Therefore, the teacher should accept the challenge to maintain the continuity of teaching and learning activities.Recommendations for Practitioners Recommendations for Researchers (1) Teachers' knowledge and organizational support should become an essential concern for policy makers and school leaders to maintain teacher performance in this dynamic online environment. (2) The educational leader should develop a strategy to manage technostress among teachers from another aspect beyond TPACK and organizational support. (3) Policymakers should develop a strategy to compensate for teacher effort and sacrifices resulting from IT disruption in their working experience. Researchers should confirm and refine the framework developed in the private sector to the educational sector to generate more theoretical and empirical un-derstanding regarding the functional integration of IT devices on certain enti-ties' productive tasks. Impact on Society This study gives more understanding of how teachers respond to IT-integrated tasks in their academic activity. This discussion will give more wisdom to under-stand the threshold of IT usefulness in the educational field besides giving pref-erence to managing it to maintain teachers' work quality.Future Research Further research is required to identify the critical factors to manage teachers' technostress effectively. A qualitative research method may be helpful in explor-ing teachers' complex responses regarding IT-integrated tasks.

Psychosocial impact of COVID-19 2 years after outbreak on mental health of medical workers in Iran

BackgroundThe COVID-19 pandemic had a substantial influence on the mental health of healthcare workers. This study investigated general health status, the prevalence, and the severity of depressive spectrum and anxiety-related disorders. It evaluated the association between various factors and depression, anxiety, and stress among healthcare workers in the Khatam-Alanbia Hospital in Iran, after 2 years since the corona virus disease 2019 (COVID-19) pandemic.ResultsIn this online cross-sectional study, 409 participants were selected and given a questionnaire about demographic, personal, and clinical characteristics as well as stressors related to COVID-19. The participants completed the General Health Questionnaire (GHQ-28) and the 42-item Depression, Anxiety, and Stress Scale (DASS-42) to report depression, anxiety, and stress/tension levels. We found that the overall incidence of depression, anxiety and stress among health care workers during the COVID-19 pandemic was 44.25%, 50.62%, and 43.76%, respectively. Participants with severe to very severe depression, anxiety and stress accounted for 19.2%, 26.6%, and 18.2% of the sample, respectively. Being female was associated with higher odds of depression, anxiety, and stress.ConclusionsTwo years after the COVID-19 outbreak, health workers are still showing a significant level of depression, anxiety, stress, and remarkable signs of psychological distress. The situation of a health care worker is worrying. The long-term psychological implications of infectious diseases should not be ignored. Mental health services could play an essential role in rehabilitation.

Modelling lifestyle-based segments of Indian wine consumers using the latent profile analysis approach

Purpose>This study aims to identify and validate the different clusters of wine consumers in India based on the wine-related lifestyle (WRL) instrument. It also investigates how the identified clusters differ in terms of socio-demographic characteristics, such as age, gender, income, education, employment and marital status.Design/methodology/approach>The authors conducted a survey using a structured questionnaire to collect data from wine consumers in India. The number of participants totalled to 432. The authors first identified the clusters using latent profile analysis. The authors then used the decision tree analysis based on a recursive partitioning algorithm to validate the clusters. Finally, the authors analysed the relationship between the identified clusters and socio-demographic characteristics using correspondence analysis.Findings>Three distinct segments emerged after data were subjected to latent profile analysis, namely, curious, ritualistic and casual. The authors found that the curious cluster had a high mean score for situational and social consumption while the ritualistic cluster had a high mean for ritualistic consumption. The findings also suggest that the casual cluster had more female wine consumers.Originality/value>This study makes methodological contributions to the wine consumer segmentation approach. First, it adopts a latent profile analysis to profile Indian wine consumers. Second, it validates the obtained clusters using the decision tree analysis method. Third, it analyses the relationship between the identified clusters and socio-demographic variables using correspondence analysis, a technique far superior to the Chi-square methods.

The Effect of the Coronavirus Pandemic on the Prediction Accuracy of Stock Price

In late 2019, the coronavirus began to spread around the world and impact international politics and economies significantly. In the face of the pandemic, stock markets around the world fluctuated sharply. The study aims to investigate the impact of the pandemic on the predictive variables of a stock prediction model, formed using chip-based variables and sentiment variables derived from comments posted on a social media platform. This study first performs feature engineering analysis to identify the indicators suitable for constructing the prediction model. The analysis then establishes a set of phrase rules to assign sentiment scores to the opinions expressed in replies and evaluates the effect on the accuracy of predictions. The results show that the major chip-based indicators affecting changes in the stock market differ before and after the pandemic. Hence, prediction models should be established separately for analysis in either period. In addition, the results indicate that the model relying on reply-based sentiment scores as a predictive variable provides more accurate predictions of stock price change.

Mental health providers' responses to the COVID-19 pandemic

The COVID-19 pandemic resulted in closures, increased stressors, and a high need for mental health services. To provide continuity of care and meet the rising need for therapy, mental health providers rapidly transitioned to telehealth. This transition occurred with the support of policy changes and leniency in telehealth guidelines. A survey including participant and client demographic variables, readiness for telehealth, transition time, methods for telehealth practice, and therapist efficacy in-person and via telehealth was created in Qualtrics. It was completed by 79 mental health providers to provide insight into this transition and inform future practice. This study hypothesized that (1) there would be differences between mental health professionals and how they adapted to telehealth based on demographic factors;(2) those with prior training and experience would be better prepared for telehealth and adapt more quickly, (3) therapists would feel more efficacious in their in-person practice than over telehealth, and (4) those with prior training and experience with telehealth would report higher levels of therapist-efficacy. The first hypothesis was supported, and differences were found in training, telehealth platform use, data collection and storage, transition time, and readiness for telehealth across professions, type of facility, and years of experience. The second hypothesis was not supported as no significant predictors of transition time were found. The third hypothesis was supported and a large effect size was found indicating that therapists felt efficacious in both settings, but more efficacious in-person (t(78) = 7.29, P<.001, D = .854). Regarding the fourth hypothesis, client technology access was the only significant predictor of therapist-efficacy over telehealth. These findings have implications for clinical administration, graduate training, policy, and ethical considerations. First, clinicians can prepare themselves for future telehealth use by reviewing their consent process, technology, and data collection/storage strategies. Second, graduate programs can support future mental health providers by incorporating telehealth into their curricula. Third, policies regarding insurance reimbursement and initiatives targeting the digital gap may improve telehealth services and ensure more equitable access to healthcare. Finally, increased awareness of the risks of technology use and clarification of regulations regarding telehealth can protect both clients and clinicians. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Exploring Bhutanese Primary School Teachers' Technological Knowledge

The COVID-19 pandemic has disrupted the normal functions of schools globally. Online learning is a new concept in Bhutan. Nonetheless, classes have begun using various online learning platforms to continue their operations during the pandemic. This study examined primary school teachers' perceived information technology knowledge and proficiency. The participants of this study consisted of 124 primary teachers from two western districts of Bhutan. Data were collected using an online survey. The study's findings suggested that although the majority of participants (98.2%) had access to personal digital devices, slow internet connection and high internet data subscription charges (60.7%) were cited as significant challenges. Findings also revealed that a small percentage of the participants, less than 12 (9.7%) teachers in this study preferred to teach entirely in an online learning environment. The results of multiple linear regression suggested that only technological pedagogical knowledge (TPK) [t = 2.68, p = 0.008, [beta] = 0.236] and perceived information technology proficiency of teachers (PITP) [t = 3.55, p = 0.001, [beta] = 0.306] were statistically significant predictors of technological knowledge (TK).

Examining Social E-Commerce Platforms by Mediating the Effect of Perceived Usefulness and Perceived Trust Using the Technology Acceptance Model

In the post-COVID-19 era, promoting the healthy development of social e-commerce platforms, taking into account the coordinated development of online social networking and online consumption, enhancing the shopping and sharing atmosphere of the platform, increasing the enthusiasm of distribution members, driving consumption traffic with social traffic, and achieving low-cost publicity and promotion increase the exposure of products. The key to solving the problem is to promote consumer participation, improve user conversion, and improve customer acquisition capabilities. The study is based on the stimulus-organism-response (S-O-R) theory. It introduces the technology acceptance model (TAM) and, according to the member life cycle theory and member value mining theory, will affect consumers' social presence factors in social e-commerce shopping situations. As independent variables, perceived trust and perceived usefulness are used as mediating variables to construct a research model that shows social e-commerce affects user conversion.