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This study aims to examine the impact of Covid19 on service ecosystem self-adjustment (SES_SA) and additionally to explore the mediating role of SES_SA on the relationship between the Covid19 pandemic and the development of digital service capability (DD_SC).

Data were drawn from 384 business people with the help of a survey questionnaire. The interrelation of the model was examined with the help of structural equation modeling (SEM) using bootstrapping measures in Smart-partial least square (PLS). Three constructs (Covid19, DD_SC and SES_SA) were found with the help of exploratory factor analysis (EFA). Convergent and discriminant validity were obtained through confirmatory factor analysis (CFA) using statistical package for the social sciences-analysis of a moment structures (SPSS-AMOS)-V.23.

There is a substantial impact of Covid19 on SES_SA and DD_SC. The investigation also discovered that SES_SA significantly impact DD_SC, whereas, Covid19 impact DD_SC indirectly through SES_SA. Age has a significant favorable influence on fear of Covid19.

There is scant literature linking SES_SA and the DD_SC.

The study promotes understanding of the contribution of Covid19 and SES_SA in the DD_SC among business people to enhance value co-creation. Capitalizing on DD_SC will enhance customer experience, assist customers in decision-making, and foster digital economic growth.

It enlightens on the digital capabilities needed for creating and co-creating value. Most studies in this area are qualitative/conceptually based and have not studied this kind of interrelation. Hence, it’s the only quantitative study that has examined the inter-relations among Covid19, SES_SA and DD_SC using SEM. This study also offers comprehension of all theories used in this context by relating Covid19 effects to DD_SC.

This paper examines in detail how the use of storytelling with parallax technology can influence the user experience (UX) in online shops as well as brand- and behavior-relevant variables. Furthermore, this study analyzes the causal relationships between UX, brand attitudes and brand-related behavioral intentions in terms of purchase intention and price premiums. Explicit and implicit paths of human information processing are considered.

A sample of 266 respondents completed a web-based experiment under two conditions (text-based vs parallax storytelling online shop). An existing and operational online shop was used. The causal relationships were assessed by using partial least squares structural equation modeling (PLS-SEM). To measure implicit information processing, a single category implicit association test was applied.

By applying the storytelling technique with parallax scrolling, the online shop increased visitors' UX on explicit and implicit information processing levels and increased the online shop's overall perceived attractiveness. Storytelling with parallax motion enables an efficient transmission of brand-related associations to consumers' minds, enhances their explicit and implicit brand attitudes and increases their willingness to pay a higher price. Moreover, this study provides empirical evidence on the effects of UX on brand-related measures by applying PLS-SEM and thus reveals a causal chain of effects from UX on online shop attractiveness, brand attitude and behavioral intentions. Again, explicit and implicit perceptions were considered.

Science and practice are increasingly emphasizing that storytelling emotionalizes content, which facilitates effective communication and builds strong relationships with customers. Little evidence exists about its efficient implementation in an online shopping context and in fulfilling hedonic and pragmatic needs throughout the online journey. This study provides novel insights into managing online shoppers' UX, brand-related perceptions and behavioral intentions with the optimal use of techniques to implement storytelling. Furthermore, this is one of the first studies to holistically consider the human perception of online shops by drawing on theories and methods of psychology, marketing, consumer behavior, brand research and consumer neuroscience and considering explicit and implicit information processing in terms of hedonic and pragmatic UX and brand-related measures.

This study aims to investigate the safety and liability of autonomous vehicles (AVs), and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs.

The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations. The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs, respectively, as well as accommodating the heterogeneity issue simultaneously.

The findings show that day, location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability.

The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.

This study aims to investigate the response of green investments of emerging countries to own-market uncertainty, oil-market uncertainty and COVID-19 effect/geo-political risks (GPRs), using the tail risks of corresponding markets as measures of uncertainty.

This study employs Westerlund and Narayan (2015) (WN)-type distributed lag model that simultaneously accounts for persistence, endogeneity and conditional heteroscedasticity, within a single model framework. The tail risks are obtained using conditional standard deviation of the residuals from an asymmetric autoregressive moving average – ARMA(1,1) – generalized autoregressive conditional heteroscedasticity – GARCH(1,1) model framework with Gaussian innovation. For out-of-sample forecast evaluation, the study employs root mean square error (RMSE), and Clark and West (2007) (CW) test for pairwise comparison of nested models, under three forecast horizons; providing statistical justification for incorporating oil tail risks and COVID-19 effects or GPRs in the predictive model.

Green returns responds significantly to own-market uncertainty (mostly positively), oil-market uncertainty (mostly positively) as well as the COVID-19 effect (mostly negatively), with some evidence of hedging potential against uncertainties that are external to the green investments market. Also, incorporating external uncertainties improves the in-sample predictability and out-of-sample forecasts, and yields some economic gains.

This study contributes originally to the green market-uncertainty literature in four ways. First, it generates daily tail risks (a more realistic measure of uncertainty) for emerging countries’ green returns and global oil prices. Second, it employs WN-type distributed lag model that is well suited to account for conditional heteroscedasticity, endogeneity and persistence effects; which characterizes financial series. Third, it presents both in-sample predictability and out-of-sample forecast performances. Fourth, it provides the economic gains of incorporating own-market, oil-market and COVID-19 uncertainty.

This paper aims to forecast inbound and outbound container throughput for Bangkok Port to 2041 and uses the results to inform the future planning and management of the port’s container terminal.

The data used cover a period of 16 years (192 months of observations). Data sources include the Bank of Thailand and the Energy Policy and Planning Office. Cause-and-effect forecasting is adopted for predicting future container throughput by using a vector error correction model (VECM).

Forecasting future container throughput in Bangkok Port will benefit port planning. Various economic factors affect the volume of both inbound and outbound containers through the port. Three cases (scenarios) of container terminal expansion are analyzed and assessed, on the basis of which an optimal scenario is identified.

The economic characteristics of Thailand differ from those of other countries/jurisdictions, such as the USA, the EU, Japan, China, Malaysia and Indonesia, and optimal terminal expansion scenarios may therefore differ from that identified in this study. In addition, six particular countries/jurisdictions are the dominant trading partners of Thailand, but these main trading partners may change in the future.

There are only two major projects that have forecast container throughput volumes for Bangkok Port. The first project, by the Japan International Cooperation Agency, applied both the trend of cargo volumes and the relationship of volumes with economic indices such as population and gross domestic product. The second project, by the Port Authority of Thailand, applied a moving average method to forecast the number of containers. Other authors have used time-series forecasting. Here, the authors apply a VECM to forecast the future container throughput of Bangkok Port.

This article aims to establish a dynamic Energy Performance Contract (EPC) risk allocation model for commercial buildings based on the theory of Incomplete Contract. The purpose is to fill the policy vacuum and allow stakeholders to manage risks in energy conservation management by EPCs to better adapt to climate change in the building sector.

The article chooses a qualitative research approach to depict the whole risk allocation picture of EPC projects and establish a dynamic EPC risk allocation model for commercial buildings in China. It starts with a comprehensive literature review on risks of EPCs. By modifying the theory of Incomplete Contract and adopting the so-called bow-tie model, a theoretical EPC risk allocation model is developed and verified by interview results. By discussing its application in the commercial building sector in China, an operational EPC three-stage risk allocation model is developed.

This study points out the contract incompleteness of the risk allocation for EPC projects and offered an operational method to guide practice. The reasonable risk allocation between building owners and Energy Service Companies can realize their bilateral targets on commercial building energy-saving benefits, which makes EPC more attractive for energy conservation.

Existing research focused mainly on static risk allocation. Less research was directed to the phased and dynamic risk allocation. This study developed a theoretical three-stage EPC risk allocation model, which provided the theoretical support for dynamic EPC risk allocation of EPC projects. By addressing the contract incompleteness of the risk allocation, an operational method is developed. This is a new approach to allocate risks for EPC projects in a dynamic and staged way.

The Indian pharmaceutical industry has contributed significantly to global healthcare by securing superior-quality, inexpensive and reachable medicines worldwide. However, supply chain management (SCM) has been challenging due to constantly shifting requirements for short lifecycles of products, the convergence of industry and changeable realities on the ground. This study aims to identify, assess and prioritize the strengths, weaknesses and opportunities of the pharmaceutical SCM environment in India.

The paper employs a Strength, Weakness, Opportunity, Threat (SWOT) analysis and recognizes strategies to utilize the advantages of the strengths and opportunities, rectify weaknesses and resolve threats.

A variety of strategies that could have a positive effect on the Indian pharmaceutical business are presented. Findings and suggested strategies can significantly advance knowledge, enhance understanding and contribute to the growth of a successful SCM for the Indian pharmaceutical sector.

This paper would act as a roadmap to greater comprehension of the market leaders and market leaders' operating climate. The findings from this study will offer academic scholars and business practitioners deeper insights into the environment of SCM.