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This study aims to comprehensively assess how digital maturity degree (DMD) impacts sustainable supply chain management (SSCM) performance through the mediating role of SSCM practices in businesses in Vietnam.

The hypotheses were performed using partial least square-structural equation modeling (PLS-SEM) with data collected from a survey of over 234 managers having responsibility in the supply chain field in Vietnam. Qualitative data were collected through semistructured interviews with 6 experts to deepen understanding of the relationship between DMD and SSCM.

The results show the mix-results in the relationship between SSCM practices and SSCM performance dimensions while DMD strongly impacts SSCM practices. Also, this study finds the mediating role of SSCM practices on the relationship between DMD and SSCM performance.

This is the first study to investigate the role of DMD on SSCM practices and SSCM performance, using empirical evidence. Moreover, the authors integrate both qualitative and quantitative for understanding complex SSCM phenomena. The present study also helps businesses improve their SSCM performance by leveraging SSCM practices and developing their digital technologies in the long-term view.

This paper aims to investigate the effect of the economic policy uncertainty (EPU), geopolitical risk (GPR) and climate policy uncertainty (CPU) of USA on Bitcoin volatility from August 2010 to August 2022.

In this paper, the authors have adopted the empirical strategy of Yen and Cheng (2021), who modified volatility model of Wang and Yen (2019), and the authors use an OLS regression with Newey-West error term.

The results using OLS regression with Newey–West error term suggest that the cryptocurrency market could have hedge or safe-haven properties against EPU and geopolitical uncertainty. While the authors find that the CPU has a negative impact on the volatility of the bitcoin market. Hence, the authors expect climate and environmental changes, as well as indiscriminate energy consumption, to play a more important role in increasing Bitcoin price volatility, in the future.

This study has two implications. First, to the best of the authors’ knowledge, the study is the first to extend the discussion on the effect of dimensions of uncertainty on the volatility of Bitcoin. Second, in contrast to previous studies, this study can be considered as the first to examine the role of climate change in predicting the volatility of bitcoin. This paper contributes to the literature on volatility forecasting of cryptocurrency in two ways. First, the authors discuss volatility forecasting of Bitcoin using the effects of three dimensions of uncertainty of USA (EPU, GPR and CPU). Second, based on the empirical results, the authors show that cryptocurrency can be a good hedging tool against EPU and GPR risk. But the cryptocurrency cannot be a hedging tool against CPU risk, especially with the high risks and climatic changes that threaten the environment.

In this “Info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activity and e-commerce. The purpose of this paper is to model users' preference evolution to recommend potential items which users may be interested in.

A novel recommendation system, namely evolution-learning recommendation (ELR), is developed to precisely predict user interest for making recommendations. Differing from prior related methods, the authors integrate the matrix factorization (MF) and recurrent neural network (RNN) to effectively describe the variation of user preferences over time.

A novel cumulative factorization technique is proposed to efficiently decompose a rating matrix for discovering latent user preferences. Compared to traditional MF-based methods, the cumulative MF could reduce the utilization of computation resources. Furthermore, the authors depict the significance of long- and short-term effects in the memory cell of RNN for evolution patterns. With the context awareness, a learning model, V-LSTM, is developed to dynamically capture the evolution pattern of user interests. By using a well-trained learning model, the authors predict future user preferences and recommend related items.

Based on the relations among users and items for recommendation, the authors introduce a novel concept, virtual communication, to effectively learn and estimate the correlation among users and items. By incorporating the discovered latent features of users and items in an evolved manner, the proposed ELR model could promote “right” things to “right” users at the “right” time. In addition, several extensive experiments are performed on real datasets and are discussed. Empirical results show that ELR significantly outperforms the prior recommendation models. The proposed ELR exhibits great generalization and robustness in real datasets, including e-commerce, industrial retail and streaming service, with all discussed metrics.

The priority of this study is to contribute to the literature by examining herding behavior at different periods of the COVID-19 pandemic. Furthermore, this study aims to investigate the herding behavior conditioned on market liquidity and information demand.

This study investigates herding behavior in Vietnam's stock exchanges (Ha Noi Stock Exchange and Ho Chi Minh Stock Exchange) on a sample of daily stock closing prices of 425 firms from 2018 to the first half of 2022.

The research confirms the existence of herding behavior not only for the whole but also during and post-COVID periods. These results are robust in both bull and bear markets, further confirming the influence of COVID-19 on herding in Vietnamese background. Moreover, when the authors condition exogenous factors for each period, the herding tendency is more evident at the medium market liquidity level than at high and low levels. Besides, the pandemic causes herding behavior of investors with low and medium information demand.

These findings imply some recommendations that facilitate investors, policymakers and researchers in the context of the COVID-19 crisis.

The study contributes to the herding literature by examining herd behavior during the post-COVID period, suggesting the long-term impact of the health crisis. Furthermore, the research provides new evidence of herding behavior conditioned on market liquidity and information demand during different COVID sub-periods.

The main purpose of this paper is to examine the adoption of big data analytics (BDA) in the Tanzania banking industry by investigating the influence of technological, environmental and organizational (TOE) factors while exploring the moderating role of perceived risk (PR).

The study employed a qualitative research design, and the research instrument was developed using per-defined measurement items adopted from prior studies; the items were slightly adjusted to fit the current context. The questionnaires were distributed to top and middle managers in selected banks in Tanzania using the snowball sampling technique. Out of 360 received responses, 302 were considered complete and valid for data analysis. The study employed partial least squares structural equation modeling (PLS-SEM) to examine the developed conceptual framework.

Top management support and financial resources emerged as influential organizational factors, as did competition intensity for the environmental factors. Notably, bank size and perceived trends showed no significant impacts on BDA adoption. The study's novelty lies in revealing PR as a moderating factor, weakening the link between technological readiness, perceived usefulness and the intent to adopt BDA.

This study extends literature by extending the TOE model, through examining the moderating roles of PR on technological factors. Furthermore, the study provides useful managerial support for the adoption of BDA in banking in emerging economies.

The paper aims to examine the moderating role of both the perceived value and perceived trust in the relationship between the ease of use, perceived risk, perceived usefulness and quality of website and the Palestinian millennials’ behavioral intention toward online shopping.

A survey questionnaire was carried out targeting 357 Palestinian millennials. In total, 311 valid responses were processed and analyzed using PLS-SEM model fitting approach.

The findings demonstrate that the impact of ease of use, perceived usefulness and website quality on online shopping behavioral intention are reinforced by perceived value and perceived trust, while the effect of perceived risk is weakened. This implies that improving convenience and lowering non-monetary costs such as time and effort will enhance the customers’ perceived value and, as a result, their shopping behavioral intention. In addition, one of the most significant barriers to online shopping is a lack of trust. Therefore, once trust is established, shopping willingness rises, and vice versa.

As this study focused on consumers from a developing country, it neglected the cross-cultural issue; thus, future studies may have to make a comparison of other samples from various nations to gain a deeper understanding. Furthermore, this research focused on the ease of use, perceived risk, perceived usefulness and perceived quality of the website to predict the customer’s behavioral intention toward online shopping, which shows significant results, indicating that more research is needed to look at these independent variables as predictors of customers purchasing decision.

Online shopping is a critical topic that has significant impact on the business world. Investigating the moderating role of perceived trust and perceived value on Palestinian millennials’ online shopping behavioral intention during COVID-19 might give useful information for organizations developing policies and strategies to create direct sales using social media platforms. Furthermore, the findings might also be valuable for management studies and academics to better understand consumers’ intentions when it comes to commercial internet buying.

The empirical nature of this paper gives a thorough explanation of the phenomena of online shopping from a developing country. To the best of the authors’ knowledge, this is the first empirical study that examines the moderating effect of perceived trust and perceived value on Palestinian millennials’ online shopping behavioral intention during COVID-19.

Amidst the Industry 4.0 landscape, organizations are increasingly harnessing cutting-edge technologies and embracing digital change to fuel innovation. However, the full potential of innovation cannot be realized without organizational readiness. This study explores how various dimensions of organizational readiness and innovation influence digital change and firm performance.

Data were gathered via a survey of 303 managers from both state-owned and private-owned enterprises in Ho Chi Minh City, Vietnam, and analyzed utilizing partial least squares–structural equation modeling (PLS–SEM).

Organizational readiness positively influences both digital change and innovation. Additionally, innovation positively impacts digital change and mediates the relationship between organizational readiness and digital change. Furthermore, both innovation and digital change positively affect firm performance, whereas the control variable of ownership type has no significant correlation with firm performance. Finally, digital change mediates the relationship between innovation and firm performance.

This study contributes to the digital innovation literature by empirically testing the influence of organizational readiness on digital change and innovation within the context of Vietnam. By investigating whether innovation and digital change enhance firm performance, this study also addresses inconsistencies in the literature.

Prior research examining cultural effects on customer service expectations has primarily used more generic Western cultural theory on an aggregate scale or with only a single variable to draw conclusions on a customer’s underlying reasoning for buying a service. This study aims to focus on culturally distinct clusters within non-Western nations, specifically exploring within-cluster differences in service expectations within the Confucian Asia cluster.

This study developed a measurement model of Chinese cultural values and service expectations, consisting of a three and five-factor structure, respectively. Data from a sample of 351 diners were analysed using SmartPLS software. The data was compared with similar studies within the Confucian Asia cluster to understand the culture effect on service expectations and within-cluster variations.

The findings underscore the varying importance of cultural values in shaping customer service expectations, emphasizing their relative, rather than equal, significance. The study provides insights into potential within-group differences in customer service expectations within the same cultural cluster – without losing sight of the fundamental cultural heterogeneity of the Confucian culture.

Managers should leverage the distinct cultural values of their operating country to gain insights into diverse customer groups, predict their behaviours and meet their needs and expectations.

This study offers valuable insights to both service management scholars and practitioners by focusing on culturally distinct clusters of non-Western nations and exploring their effects on variation in service expectations within these clusters.

Given the importance of the growing segmentation of ageing consumers and their increasing interaction with the Internet, digital marketing scholars are becoming more interested in this market. Prior research needs to pay more attention to this market in many contexts of digital marketing. This study aims to provide insights into ageing consumers’ content usage, content typology choices, and online brand advocacy (OBA).

Semi-structured interviews were applied, and 16 consumers from Southern Europe aged 55+ were included. The interviews were transcribed and examined following the principles of content analysis.

According to the research, older consumers display their usage and concerns regarding online content. They have different decision-making processes depending on whether they are purchasing products or services. Likewise, their choices of content typology vary based on the utilitarian or hedonic product category.

This study contributes to the literature by providing insights into this growing segmentation and proposing an OBA framework for older consumers related to content marketing. Finally, the study suggests that older consumers are passive online and active offline brand advocates.