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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 aim of this study was to investigate the role of immersive gaming on brand coolness and brand equity, with particular emphasis on fashion gaming collaborations. We used game theory as a theoretical framework to analyse immersive branding strategies and gain a deeper understanding of fashion consumers’ decision-making process in gaming environments. Gender, as a significant factor affecting gamers, has been studied as a moderator that impacts the overall proposed framework.

A quantitative method was used to assess the significance of relationships within the proposed model empirically. The partial least squares structural equation modelling technique was implemented to assess the relationships of the framework with a sample size of 160 active Malaysian gamers.

The findings indicate that brand equity is positively associated with perceived brand coolness. Furthermore, of the three core values of online games, perceived enjoyment is most strongly associated with perceived brand coolness, ahead of the values of self-expression and perceived emotional challenge. The results of the multigroup analysis further suggest that in the fashion industry, building brand equity through online games is strongly related to perceived brand coolness among female respondents, the role of perceived brand coolness being weaker among male respondents.

The study contributes to the existing literature by providing a deeper understanding of the impact of immersive gaming branding practices on the overall equity of the fashion brand. The results provide insight for fashion brand managers into the significant effect on consumer behaviour outcomes of fashion-gaming collaborations.

本研究擬探討身歷其境的遊戲體驗在品牌酷感和品牌資產上所扮演的角色; 研究特別強調裝扮遊戲的合作。研究人員以博弈論作為研究的理論框架,來分析沉浸品牌策略、和對時裝消費者在遊戲的環境中如何作出決策取得更深入的瞭解。研究人員探討了性別作為影響遊戲參與者的重要因素,這因素被視為為整個被提出的框架帶來調節的影響和作用。

研究人員採用定量方法進行研究,目的為於被提出的模型內之各種關聯的意義進行以經驗為依據的評估。研究人員採用了基於偏最小平方法的結構方程模型研究法,來對一個包含160名活躍的馬來西亞遊戲參與者的樣本進行框架的各個聯繫的評估研究。

研究結果顯示,品牌資產與品牌酷感成正相關; 而且,在網絡遊戲的三個核心價值中,感知享受與品牌酷感之間的聯繫最為強烈和密切,超過自我表現和感知情感挑戰兩者的價值; 多群組的分析結果更暗示了在時裝產業裡,透過網絡遊戲去建立品牌資產在女性回應者中是與品牌酷感有強烈密切的關係; 而在男性回應者中,品牌酷感所扮演的角色則較弱。

本研究使我們能更深入認識沉浸遊戲品牌的慣常做法如何影響時裝品牌的整體資產。研究結果為時裝品牌經理提供了啟示,使他們明瞭時裝遊戲的合作會給消費者行為產生重要的影響。

Automatically extracting stance information from natural language texts is a significant research problem with various applications, particularly after the recent explosion of data on the internet via platforms like social media sites. Stance detection system helps determine whether the author agree, against or has a neutral opinion with the given target. Most of the research in stance detection focuses on the English language, while few research was conducted on the Arabic language.

This paper aimed to address stance detection on Arabic tweets by building and comparing different stance detection models using four transformers, namely: Araelectra, MARBERT, AraBERT and Qarib. Using different weights for these transformers, the authors performed extensive experiments fine-tuning the task of stance detection Arabic tweets with the four different transformers.

The results showed that the AraBERT model learned better than the other three models with a 70% F1 score followed by the Qarib model with a 68% F1 score.

A limitation of this study is the imbalanced dataset and the limited availability of annotated datasets of SD in Arabic.

Provide comprehensive overview of the current resources for stance detection in the literature, including datasets and machine learning methods used. Therefore, the authors examined the models to analyze and comprehend the obtained findings in order to make recommendations for the best performance models for the stance detection task.

In order to solve the problem of inaccurate calculation of index weights, subjectivity and uncertainty of index assessment in the risk assessment process, this study aims to propose a scientific and reasonable centralized traffic control (CTC) system risk assessment method.

First, system-theoretic process analysis (STPA) is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis. Then, to enhance the accuracy of weight calculation, the fuzzy analytical hierarchy process (FAHP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and entropy weight method are employed to calculate the subjective weight, relative weight and objective weight of each index. These three types of weights are combined using game theory to obtain the combined weight for each index. To reduce subjectivity and uncertainty in the assessment process, the backward cloud generator method is utilized to obtain the numerical character (NC) of the cloud model for each index. The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system. This cloud model is used to obtain the CTC system's comprehensive risk assessment. The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud. Finally, this process yields the risk assessment results for the CTC system.

The cloud model can handle the subjectivity and fuzziness in the risk assessment process well. The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.

This study provides a cloud model-based method for risk assessment of CTC systems, which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment, achieving effective risk assessment of CTC systems. It can provide a reference and theoretical basis for risk management of the CTC system.

In 2022, the International Network for Quality Assurance (QA) Agencies in Higher Education (INQAAHE) published the new guidelines by adding three QA modules in response to the changing higher education landscape. The paper aims to investigate the transformative focus of quality assurance in higher education globally as well as Asian response to three new QA modules according to the INQAAHE ISGs.

The research conducted a quantitative approach for data collection. An on-line survey was conducted to perceive QA practices, perceptions toward new emerging QA modules and challenges encountered. In total, there were 26 responses from 18 territories with 22 QA agencies. A total of 13 out of them have a national qualifications framework in place.

Three are three major findings in the study. First, national policy and criteria and standards in distance education have been developed in the majority of Asian nations. Second, non-signatories of the Tokyo Convention had a higher proportion of having related policies, regulations and criteria in CBHE and distance education. Third, national policies and regulations; and lack of professional staff are two common challenges implementing QA in new types of providers.

The findings are of value for policymakers, QA agencies and universities to advocate the new QA model as a systematic approach in response to changing higher education landscape in the post pandemic era.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

This study applies the target similarity model to examine the effects of servant leadership on supervisor commitment and supervisor citizenship behavior. The mediating role of supervisory commitment is explored to determine the relationship between servant leadership and supervisor citizenship behavior. The difference in supervisor gender is examined in the linkage between servant leadership and supervisory commitment.

The data were collected through a survey of 478 salespeople in the retail industry. Structural equation modeling (SEM) techniques were used to verify the hypotheses of this study.

The findings showed significant support for the direct and indirect effects of servant leadership on supervisor commitment and supervisor citizenship behavior. Furthermore, the positive relationship between servant leadership and supervisory commitment was stronger among female supervisors than male supervisors.

Due to the scarcity of studies conducted on the linkages of servant leadership, supervisory commitment and supervisory citizenship behavior, this study theoretically and empirically contributes to the leadership literature as it is the first study to investigate these direct and indirect relationships. Similarly, this study examined gender differences in servant leadership to fill the gap in the research field.

Digital inclusive finance has a positive promotion effect on the development of the national economy, but little research exists on how digital inclusive finance affects high-quality consumption in economically developed regions. Therefore, to fill the gap, this paper aims to study the impact of digital inclusive finance on high-quality consumption development using the economically developed regions of Jiangsu, Zhejiang and Shanghai as examples.

Firstly, the entropy method is used to construct the index of high-quality consumption among residents. Then, the municipal-level data of Jiangsu, Zhejiang and Shanghai from 2011 to 2020 are used to test the impact. Subsequently, the mechanism of action test and heterogeneity analysis are conducted.

The results show that digital inclusive finance has a positive role in promoting the high-quality consumption of residents in Jiangsu, Zhejiang and Shanghai. At the same time, digital inclusive finance can promote high-quality consumption through its own digital payment and internet insurance channels. There is regional heterogeneity in the impact.

To the best of the authors’ knowledge, this study is the first to examine whether and how digital inclusive finance affects high-quality consumption. The authors consider multiple dimensions, such as consumption level, consumption structure, consumption ability, consumption environment and consumption mode, to measure high-quality consumption. The findings provide valuable insights for policymakers, investors and regulators in planning regulations.

Collaborating with consumers during new product development can provide companies with significant benefits and competitive advantages. Although several studies have been conducted on the design of co-innovation platforms, there is still a need for a more comprehensive understanding of the co-innovation phenomenon. To address this gap, this research aims to identify the critical success factors of co-innovation platforms and provide an extensive analysis of the variables that determine their effectiveness.

This study presents a systematic literature review of co-innovation platforms based on an analysis of 89 articles published in 50 scholarly journals in the disciplines of information systems, marketing and business, covering the years from 2006 to 2022.

The review synthesizes the current state of scientific knowledge and groups prior studies thematically as critical success factors of co-innovation platforms. As a result, eight success factors have been identified in terms of quantity and quality of contributions. These factors include product involvement, perceived fairness, sense of community, interactive environment, employee involvement, participant diversity, assessment structure and task design.

The study consolidates existing research about the critical success of co-innovation platforms. It also provides a research framework that incorporates a diverse set of variables that can be used to assess co-innovation performance in future studies.

In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.