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We use unique data on the travel history of confirmed patients at a daily frequency across 31 provinces in China to study how spatial interactions influence the geographic spread of pandemic COVID-19. We develop and simultaneously estimate a structural model of dynamic disease transmission network formation and spatial interaction. This allows us to understand what externalities the disease risk associated with a single place may create for the entire country. We find a positive and significant spatial interaction effect that strongly influences the duration and severity of pandemic COVID-19. And there exists heterogeneity in this interaction effect: the spatial spillover effect from the source province is significantly higher than from other provinces. Further counterfactual policy analysis shows that targeting the key province can improve the effectiveness of policy interventions for containing the geographic spread of pandemic COVID-19, and the effect of such targeted policy decreases with an increase in the time of delay.
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With destinations steadily ‘opening back up for business' (while COVID-19 cases are still high in many areas), there is an increasing need to consider residents. Integrating the cognitive appraisal theory and the affect theory of exchange, this work tests a structural model examining the degree to which residents' perceptions of COVID-19 precautionary measures explain emotions directed toward visitors, and ultimately their willingness to engage in shared behaviors with tourists. Data were collected from 530 residents in 25 U.S. counties with the highest percentages of historical COVID-19 cases per population. A total of 10 of the 12 tested hypotheses were significant, contributing to 60% and 85% of the variance explained in contending and accommodating emotions, and 53% and 50% of the variance explained in engaging in less intimate–distal and more intimate–proximal behaviors with tourists. The implications highlight the complementary use of the two frameworks in explaining residents' preference for engagement in less intimate–distal interactions with tourists.
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The paper investigates whether credit default swaps (CDSs) spread of Indian banks is a leading indicator for bank default risk. The paper uses Merton-type models to estimate theoretical CDS spread of the sample of Indian banks and then compares it to their balance sheet ratios. Though theoretically, higher CDS spreads indicate higher default risk, the paper finds that in times of shocks, like the present COVID-19 crisis, it becomes difficult to isolate the spread movements due to true default risk versus those based on panic and speculation. The paper then correlates equity premiums and CDS premiums and finds negative correlation. The equity market returns lead the CDS market returns in capturing default risk. As default risk is priced better in equity markets, it is preferable for institutional investors to trade default risk of Indian banks in the stock markets rather than the CDS markets.
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Macao is a tourist city. It is home to the Ruins of Saint Paul's, a unique 100-year-old landmark, which is still standing with manual maintenance, even after three fires and reconstruction events. Therefore, the continuous preservation of its culture, heritage education, and construction are important issues for Macao. With the development of digital technology in recent years, users can quickly search historical sites and save two-dimensional and three-dimensional images and videos through smartphones. These methods also enhance the communication power of culture. Virtual browsing on a smartphone requires computing power and storage space;yet, virtual reality devices are not widely used. Therefore, augmented reality and virtual reality are rarely used simultaneously for three-dimensional interactive guided tours and operation experiences on the same theme. However, by quickly creating virtual reality scenarios and preserving historical sites on mobile devices, 4DAGE's 4DKanKan technology can provide augmented reality and metaverse virtual reality experiences. 4DKanKan can also integrate mobile guides and navigation software to connect mobile devices and assist in cultural inheritance and conduct sustainable education. This research linked this technology to the web by incorporating augmented reality and virtual reality technology to make designs and discussed the influences among service design, behavioral intentions, and learning effects. We collated and analyzed relevant data and text materials through systematic testing, observation, operation processes, and semi-structured interviews. The PLS multigroup structural model was used to explore and analyze the degree of influence and explanatory power of system quality, information quality, behavioral intention, and learning effects among themselves. The results of this study show that most users accepted the proposed innovative mode of operation and found it to be interesting and fun. Augmented reality is not limited by space or time;however, virtual reality devices taking too long to operate, switching too frequently, and having too many functional interfaces can cause operational problems. This study identified and modified the influencing factors and problems of the proposed system, with the aim of continuing to expand the applications of 4DKanKan to other cultural attractions or museums in the future. In addition, the research results can provide a reference for the sustainable development of related cultural sites.
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In a closed-loop supply chain (CLSC), acquiring end-of-life vehicles (ELVs) and their components from both primary and secondary markets has posed a huge uncertainty and risk. Moreover, the constant supply of ELV components with minimization of cost and exploitation of natural resources is another pressing challenge. To address the issues, the present study has developed a risk simulation framework to study market uncertainty/risk in a CLSC. In the first phase of the framework, a total of 12 important variables are identified from the existing studies. The total interpretive structural model (TISM) is used to develop a causal relationship network among the variables. Then, Matriced Impacts Cruoses Multiplication Applique a un Classement is used for determining the nature of relationships (i.e., driving or dependence power). In the second phase, the relationship of TISM is used to derive a Bayesian belief network model for determining the level of risks (i.e., high, medium, and low) associated with the CLSC through the generation of conditional probabilities across 1) multi-, 2) single-, and 3) without-parent nodes. The study findings will help decision-makers in adopting strategic and operational interventions to increase the effectiveness and resiliency of the network. Furthermore, it will help practitioners to make decisions on change management implementation for stakeholders'performance audits on the attributes of the ELV recovery program and developing resilience in the CLSC network. Overall, the present study holistically contributes to a broader investigation of the implications of strategic decisions in automobile manufacturers and resellers. IEEE
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PurposeDespite big data analytics capability (BDAC) has received extensive attention, how and under what conditions BDAC influences green supply chain integration (GSCI) remains unclear. This study draws on organizational information processing theory to examine the mediating effect of supply chain visibility in the BDAC–GSCI link and the moderating effects of flexibility- and control-oriented culture.Design/methodology/approachThe authors examined the research model using two-waved survey data gathered from 317 Chinese firms. The authors employed hierarchical regression analysis and bootstrapping method to test hypotheses and assessed the robust of theoretical model using structural model.FindingsThe authors found that BDAC facilitates all three dimensions of GSCI. Supply visibility mediates the relationship between BDAC and all three dimensions of GSCI, whereas demand visibility only mediates the positive effects of BDAC on green internal and customer integration. In addition, control-oriented culture strengthens the positive impacts of BDAC on supply and demand visibility, while the moderating effects of flexibility-oriented culture are insignificant.Originality/valueThis research contributes to opening the "black box” of how BDAC affects GSCI and provides novel guidelines for firms enhancing the degree of GSCI.
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PurposeCOVID-19 has galvanized educational institutions to rapidly adopt technology-enhanced learning (TEL) environment where students are required to use and manage a diverse set of information and communication technologies (ICTs). Using the Transactional Theory of Stress, the authors investigate the impact of a TEL environment on students' stress, cognitive appraisal and coping. The authors also explore how the TEL environment impacts students' learning satisfaction and performance.Design/methodology/approachA survey using Qualtrics was developed to collect the data from 275 undergraduate students. The authors used the Partial Least Squares (PLS) through SmartPLS for instrument validation and testing of the structural model. The reflective-formative model was applied as the measures used to evaluate the first-order constructs are unidimensional, and the second-order construct has a formative measurement.FindingsStudents experienced technology-related stress due to ICT use. The negative appraisal such as harm and threat leads to emotion-focused coping among students, while the constructive appraisal such as positive and challenge leads to problem-focused coping. Emotion-focused coping was found to negatively impact learning satisfaction, while problem-focused coping was found to positively impact satisfaction. The authors also found that students with a higher level of experience with online and hybrid classes, higher confidence in computer usage and lower anxiety are better equipped to deal with technostress.Research limitations/implicationsThis study provides the first comprehensive technostress model in the IS literature that uses formative modeling to explain technostress creators and inhibitors and emotion-focused coping and problem-focused coping, as was intended by the Transactional Theory of Stress. The result allows for rethinking TEL environment by drawing attention to strategies that can reduce technological complexity and uncertainty. For future research, it may be helpful to perform a longitudinal or experimental study to obtain rigorous causal inference. Originality/valueThere is limited research on the impact of technostress in the field of higher education. Also, technostress has been used inconsistently in Information Systems research, with the majority of research focusing on technostress creators and inhibitors only. This research incorporates all the constructs of the original theory adding new knowledge to the IS literature about the nature and causes of stress created by the use of technology.
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Vietnam is increasingly exposed to climate change. The COVID-19 crisis offers an opportunity to push for green investment and climate-resilient adaptation. In this context, the paper uses a DSGE structural model, calibrated to features of the Vietnamese economy, to simulate the macroeconomic trade-offs of investing in resilient infrastructure. Compared to scenarios of a baseline no policy change and additional standard infrastructure spending, the model findings illustrate the long-term benefits of adaptation infrastructure investments on growth and public debt dynamics. Specifically, while adaptation infrastructure is initially slightly costlier during the scale-up period, it can better withstand natural disaster shocks, and over time, would have lower maintenance costs and higher return than conventional infrastructure. Other model scenarios illustrate the trade-offs of different financing sources as well as the benefits of public financial management reforms that improve investment efficiency. © 2021 AESS Publications. All Rights Reserved.
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The way an organization operates has a pattern to it. A knowledge-based way of understanding these patterns and implementing according to them retains the competitive advantage of the organizations. Thus, identifying factors is important because, if successful, it results in shared intellectual capital. Changing the core of the pattern upon which the organization works creates several problems in retaining an organization's competitiveness. This research focuses on identifying the elements which have a significant influence on an organization's operations due to the remote working of employees during situations like the COVID-19 pandemic. Further, the relationships of factors among each other have been explored from the available research. Based on the study of various organizations it has been found that not much work has been done to identify such factors even though several organizations have suddenly opted for their workforce to work remotely due to the COVID-19 pandemic. This has resulted in lost productivity and opportunities, organizational dis-balances, and a slower rate of development. The generated model may help organizations to understand the weak notes of remote working and implement structural changes accordingly to improve the productivity in remote working and tackle the productivity and opportunity loss due to remote workforce. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Purpose: The purpose of this study was to demonstrate a cloud business intelligence model for industrial SMEs. An initial model was developed to accomplish this, followed by validation and finalization of the cloud business intelligence model. Additionally, this research employs a mixed-techniques approach, including both qualitative and quantitative methods. This paper aims to achieve the following objectives: (1) Recognize the Cloud business intelligence concepts. (2) Identify the role of cloud BI in SMEs. (3) Identify the factors that affect the design and presenting a Cloud business intelligence model based on critical factors affecting SMEs during pandemic COVID-19. (4) Discuss the importance of Cloud BI in pandemic COVID-19 for SMEs. (5) Provide managerial implications for using Cloud BI effectively in Iran's SMEs. Design/methodology/approach: In the current study, an initial model was first proposed, and the cloud business intelligence model was then validated and finalized. Moreover, this study uses a mixed-methods design in which both qualitative and quantitative methods are used. The fuzzy Delphi Method has been applied for parameter validation purposes, and eventually, the Cloud business intelligence model has been presented through exploiting the interpretive structural modeling. The partial least squares method was also applied to validate the model. Data were also analyzed using the MAXQDA and Smart PLS software package. Findings: In this research, from the elimination of synonym and frequently repeated factors and classification of final factors, six main factors, 24 subfactors and 24 identifiers were discovered from the texts of the relevant papers and interviews conducted with 19 experts in the area of BI and Cloud computing. The main factors of our research include drivers, enablers, competencies, critical success factors, SME characteristics and adoption. The subfactors of included competitors pressure, decision-making time, data access, data analysis and calculations, budget, clear view, clear missions, BI tools, data infrastructure, information merging, business key sector, data owner, business process, data resource, data quality, IT skill, organizational preparedness, innovation orientation, SME characteristics, SME activity, SME structure, BI maturity, standardization, agility, balances between BI systems and business strategies. Then, the quantitative part continued with the fuzzy Delphi technique in which two factors, decision-making time and agility, were deleted in the first round, and the second round was conducted for the rest of the factors. In that step, 24 factors were assessed based on the opinions of 19 experts. In the second round, none of the factors were removed, and thus the Delphi analysis was concluded. Next, data analysis was carried out by building the structural self-interaction matrix to present the model. According to the results, adoptability is a first-level or dependent variable. Regarding the results of interpretive structural modeling (ISM), the variable of critical success factors is a second-level variable. Enablers, competencies and SME characteristics are the third-level and most effective variables of the model. Accordingly, the initial model of Cloud BI for SMEs is presented as follows: The results of ISM revealed the impact of SME characteristics on BI critical success factors and adoptability. Since this category was not an underlying category of BI;thus, it played the role of a moderating variable for the impact of critical success factors on adoptability in the final model. Research limitations/implications: Since this study is limited to about 100 SMEs in the north of Iran, results should be applied cautiously to SMEs in other countries. Generalizing the study's results to other industries and geographic regions should be done with care since management perceptions, and financial condition of a business vary significantly. Additionally, the topic of business intelligence in SMEs constrained the sample from the start since not all SMEs use business int lligence systems, and others are unaware of their advantages. BI tools enable the effective management of companies of all sizes by providing analytic data and critical performance indicators. In general, SMEs used fewer business intelligence technologies than big companies. According to studies, SMEs understand the value of simplifying their information resources to make critical business choices. Additionally, they are aware of the market's abundance of business intelligence products. However, many SMEs lack the technical knowledge necessary to choose the optimal tool combination. In light of the frequently significant investment required to implement BI approaches, a viable alternative for SMEs may be to adopt cloud computing solutions that enable organizations to strengthen their systems and information technologies on a pay-per-use basis while also providing access to cutting-edge BI technologies at a reasonable price. Practical implications: Before the implementation of Cloud BI in SMEs, condition of driver, competency and critical success factor of SMEs should also be considered. These will help to define the significant resources and skills that form the strategic edge and lead to the success of Cloud BI projects. Originality/value: Most of the previous studies have been focused on factors such as critical success factors in cloud business intelligence and cloud computing in small and medium-sized enterprises, cloud business intelligence adoption models, the services used in cloud business intelligence, the factors involved in acceptance of cloud business intelligence, the challenges and advantages of cloud business intelligence, and drivers and barriers to cloud business intelligence. None of the studied resources proposed any comprehensive model for designing and implementing cloud business intelligence in small and medium-sized enterprises;they only investigated some of the aspects of this issue. © 2021, Emerald Publishing Limited.
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Public and private owners of critical infrastructures all over the world are taking high-quality standards to face the consequences of pandemics, particularly critical infrastructure such as dams that needs more attention to maintain and operate during coronavirus disease (Covid-19) pandemics. In this study, critical strategies have been identified through literature review and with the support of experts' opinions. The rough Decision-making Trial and Evaluation Laboratory and interpretive structural modelling methods were integrated to determine the most important strategies that were identified by literature review and experts' opinions. Moreover, the methodology was used to find the relationships, cause and effect between the critical strategies. Interviews were completed with professional managers and experts in the field of dam operation and maintenance to help in finding the influence degree between these critical strategies. Among 11 initial strategies, six critical strategies were selected for this study from the experts' points of view. By applying Matriced Impacts Croisés Multiplication Appliquée á un Classement analysis, driving and dependence powers were also determined and classified for these strategies. The outcomes indicate that the strategy of reviewing emergency action plans and planning for how routine and unplanned work will be implemented during pandemic staffing restrictions is the most driving among these strategies in dam asset management in Canada during pandemics. © 2021 ICE Publishing: All rights reserved.
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Pujon Kidul Village, Pujon District, Malang Regency, is an area with tourism potential that has been developed since 2017 with the concept of agricultural tourism. Throughout the development of tourism villages, Pujon Kidul Village has succeeded in accelerating economic growth and providing jobs for the community. However, during the COVID-19 pandemic, tourism villages have been severely affected, leading to the temporary closure of tourist attractions and community businesses. This research aimed to identify what indicators form social capital variables and the relationship between social capital variables and community adaptation patterns in dealing with pandemics in the study location. This was quantitative research with confirmatory factor analysis to determine the indicators of forming social capital and structural equation modeling analysis to determine the relationship between the variables. Based on the findings, it is known that trust in forming a social network is 0.468. Furthermore, the social network forms community actions of 0.046 and influences community resilience by 0.007. Therefore, good social capital will make it easier for the community to participate in collective action as a form of caring for each other during the pandemic. This action also influences the community to survive in a pandemic crisis, thus creating an adaptation pattern for the Pujon Kidul Tourism Village community in facing a pandemic.
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With advances in medicine, many drugs and treatments become available. On the one hand, polydrug use (i.e. using more than one drug at a time) has been used to treat patients with multiple morbid conditions, and polydrug use may cause severe side effects. On the other hand, combination treatments have been successfully developed to treat severe diseases such as cancer and chronic diseases. Observational data, such as electronic health record data, may provide useful information for assessing drug interactions. In this article, we propose using marginal structural models to assess the average treatment effect and causal interaction of two drugs by controlling confounding variables. The causal effect and the interaction of two drugs are assessed using the weighted likelihood approach, with weights being the inverse probability of the treatment assigned. Simulation studies were conducted to examine the performance of the proposed method, which showed that the proposed method was able to estimate the causal parameters consistently. Case studies were conducted to examine the joint effect of metformin and glyburide use on reducing the hospital readmission for type 2 diabetic patients, and to examine the joint effect of antecedent statins and opioids use on the immune and inflammatory biomarkers for COVID-19 hospitalized patients. [ FROM AUTHOR] Copyright of Journal of Applied Statistics is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Purpose: Global construction has been affected by COVID-19 unprecedently. The construction sectors in the least developed countries are considered as vulnerable, but the covid made the countries experience the worst situation ever. To minimize the losses by effective measures, there needs to assess the COVID-19 impacts on the construction sector. So, the aim of this study is to investigate the most critical impacts of COVID-19 on construction in the least developed countries by considering the case study of Bangladesh. Design/methodology/approach: The authors adopted multistep research methods, including (1) literature analysis and discussion with experts to establish a comprehensive list of COVID-19 impacts;(2) through a questionnaire survey, data were collected from 217 construction professionals by email, Google Form and Skype for quantifying the significance of covid impacts;(3) reliability of the survey checked by the Cronbach Alpha test;(4) Relative Importance Index (RII) to determine the ranks of the impacts based on their significance;(5) Interpretive Structural Model (ISM) to explore the corelations and the hierarchical structure;and (6) cross-impact matrix multiplication applied to classification (MICMAC) analysis to classify the COVID-19 impacts. Findings: The study identified a total of 18 COVID-19 impacts on the construction sector. Among them, the job cuts, schedule delays, project suspension, cost overrun and effects on mental health are more influential and significant than others. Further, this study found that unpaid leave and job cuts are the two most fundamental impacts which influence other succeeding significant impacts. And ultimately all the impacts lead to hampering the national economy and development. Finally, MICMAC analysis suggested that unpaid leave and job cuts should be addressed first to resolve and effects on the national economy and development should be later. Research limitations/implications: This study does not consider all the COVID-19 impacts due to the relevant context and simplicity of the ISM method. Also, the respondent's attitude might be slightly different during the post-mass vaccination period. Practical implications: This study will help the company's management, employees and government to develop effective strategies to understand the insight of their interrelations and ultimately overcome the identified covid effects. This will must contribute to the industry, its employees, the government and society by ensuring the national economy and development, construction operations, investment, employment and social security. Originality/value: This study will contribute to the knowledge body (practitioners and researchers) by providing the list of significant covid impacts and insight into their interrelations for further deep analysis of the pandemic effects. This will also help the authorities and stakeholders in developing policies and strategies to minimize or avoid these effects and avoid future consequences due to any pandemic like covid. © 2022, Emerald Publishing Limited.
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Purpose>This paper aims to examine the determinants that drive digital library use by university students in Indonesia and Malaysia during COVID-19. A proposed model based on the unified theory of acceptance and use of technology model-2 was implemented.Design/methodology/approach>A pilot study was conducted prior to the main data collection to examine the reliability of the instrument. For the structural model assessment, 1,168 responses were analysed through partial least square structural equation modelling (PLS-SEM) and importance and performance analysis (IPMA). A t-test was also conducted to support the main findings.Findings>Most hypotheses are confirmed by the significant results reported through the PLS-SEM. The strongest determinant of behavioural intention is habits. Digital library use is most significantly predicted by behavioural intention. Through IPMA, habits are reported to be the most important determinant for behavioural intention. Performance expectancy obtains the highest performance in the IPMA procedure. Behavioural intention and digital library use are significantly different based on the respondents’ location.Originality/value>This study addresses the research gap (comparative studies between two countries) by evaluating digital library use among students in two universities in Indonesia and Malaysia during COVID-19. Findings could facilitate policymakers with insights into digital library use determinants and initiate effective and efficient policies to improve digital library use, especially during school closures due to certain situations or conditions.
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Analyzing the effect of the variables Eating Habits, Emotional Condition and Physical Activity (PA) Agency on Diet Perception and PA Time, in Colombian university students under COVID-19 confinement conditions. Preliminary correlational research was conducted through a comparative survey with both exploratory and explanatory scope. It was applied to 389 students who voluntarily completed the instrument on a Google Form. The structural model explains respectively 38% and 53% of the variability of the students’ diet perception and PA time. The model shows both statistical (χ² = 84 [47 gl p = 0,09]) and practical (IBBAN = 96;IBBANN = 99;IAC = 0,99 and RMSEA = 0,02 [0,00, 0,04]) goodness of fit. Hence, it can be stated that this inclusive model has the same explanatory power as the saturated one, which relates all variables to each other. Eating habits and intention were found to have a direct effect on the university students’ diet perception. Just as well, eating habits, intention and diet perception were observed to increase PA time.Alternate :Analizar el efecto de hábitos alimentarios, situación emocional y agencia personal de la actividad física sobre la percepción de dieta y tiempo de actividad física, de universitarios colombianos en condiciones de confinamiento por la COVID-19. Investigación exploratoria de tipo correlacional con alcance explicativo en su nivel de conocimiento. Se aplicó de forma voluntaria un cuestionario en plataforma Google a 389 estudiantes. El modelo estructural explica el 38% de la variabilidad de la percepción de dieta de los estudiantes y 53% de la variabilidad del tiempo dedicado a la AF. Posee bondad de ajuste tanto estadístico, χ² = 84 (47 gl), p = 0,09, como práctico, IBBAN = 96, IBBANN = 99, IAC = 0,99 y RMSEA = 0,02 (0,00, 0,04), por lo que se puede afirmar que este modelo inclusivo tiene el mismo poder de explicación que el modelo saturado, que relaciona todas las variables entre sí. Se evidencia un efecto directo entre las variables hábitos alimenticios, intencionalidad y la percepción que los estudiantes universitarios tienen de su dieta. Así mismo, se observa que, las variables antes mencionadas contribuyen a que aumente el tiempo en minutos de AF.
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The business context has been affected by the action of Covid 19, and companies must react to this new reality. The research analyzes people, processes and technology and their effect on the development of the respective business strategy in small companies in Huancayo, Peru. The study design was non-probabilistic, cross-sectional, qualitative-quantitative and explanatory in a sample of 99 business units. The analytical technique used was structural equation modeling based on PLS. The results indicate that there is a positive relationship between the processes and technology used and the implementation of actions to achieve their objectives. No significant relationship was found with people, given that in the face of the health emergency, economic paralysis, closure of markets and sectors, the companies had to make decisions and postpone concern for human resources. The coefficient of determination (R2) was 79.3%, which shows that the model presented allows predicting future results. © 2022, Growing Science. All rights reserved.
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Purpose>This paper aims to focus on developing a theoretical framework for the analysis of factors influencing additive manufacturing (AM) in the health-care domain.Design/methodology/approach>A total of 18 factors are considered through extensive literature review and the relationship between each factor is studied using total interpretive structural modeling (TISM) and the model is logically developed. TISM model is developed using appropriate expert inputs. In addition, cross-impact matrix multiplication applied to classification (MICMAC) analysis is conducted to group the factors.Findings>It was found that “ease of design” and “research and development” are the two most important factors with the highest driving power and dependencies. Through MICMAC analysis, the significance of factors is studied.Practical implications>The study has been done based on inputs from academic experts and industry practitioners. The inferences from the study have practical relevance.Originality/value>The development of a structural model for the analysis of factors influencing AM in the health-care domain is the original contribution of the authors.
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Purpose>Although graphic-based emoticons in mobile instant messenger (MIM) services became an important revenue source for their service provider, empirical research investigating factors influencing graphic-based emoticon purchase from the consumer's perspective is insufficient. The authors explore how user's achieved belongingness (acceptance or rejection) affects graphic based emoticon usage motivations and its purchase intentions.Design/methodology/approach>A structural model is used to examine the relationship among individual's overall achieved belongingness, motivation factors of graphic-based emoticon usage in MIM such as perceived usefulness, perceived enjoyment, perceived enjoyment for others, social norm and emoticon purchase intentions. The authors collected and analyzed survey data of 279 Korean KakaoTalk users.Findings>The analysis shows that perceived acceptance/inclusion positively impacts perceived usefulness, enjoyment and enjoyment of others in graphic-based emoticon usage. Meanwhile, perceived rejection/exclusion positively impacts perceived enjoyment and enjoyment of others but negatively influences perceived social norms. Moreover, social norms and perceived enjoyment directly affect graphic-based emoticon purchase intentions. The authors also find that perceived enjoyment of others and perceived social norms in a serial causal order mediate the relationship between perceived acceptance/inclusion (and rejection/exclusion) and emoticon purchase intentions.Research limitations/implications>Additional research including users from other demographic groups, such as other age groups, is required to generalize our findings and to increase external validity.Originality/value>Unique implications related to the role of user's achieved belongingness and perceived enjoyment of others in graphic-based emoticon usage in purchase intentions are found.Peer review>The peer review history for this article is available at: https://publons.com/publon/ 10.1108/OIR-02-2020-0036
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Because of the growing number of hospitals in the country like the United Arab Emirates, huge medical wastes are generated in the hospitals, and managing this medical waste is considered a big challenge. In recent days, COVID 19 pandemic has paved the way for the generation of relatively huge amounts of infectious and hazardous waste in healthcare hospitals, and proper disposal of this heterogeneous mixture of medical waste is the biggest challenge. Improper waste management developed in health care units causes a direct impact on the workers, waste handlers, patients, caregivers, and the community. Also, it is important to manage the medical waste properly so that the environment will not get affected. In order to overcome this problem, both the manufacturer and the medical practitioner should take utmost care in managing the medical waste properly in all stages, starting from collection to the final disposal. The main aim of this research is to understand the different types of medical waste in the hospital and identify the barriers that impede the effective management of medical waste. For analyzing the interactions among the barriers, Interpretive Structural Modelling (ISM) approach is proposed as a solution methodology in this research work. By analysing the interaction among the barriers using the ISM model, we may extract the most influencing barrier that challenges both hospital management and government in managing medical waste safely and effectively. © 2021 IEEE.