Digital Business最新文献

This study investigates how consumers make decisions when using online food delivery services in Thailand, with a particular focus on the price-related factors that influence their choices. The research adopts a mixed-method approach, combining quantitative and qualitative data collection and analysis. Quantitatively, data was gathered from 392 respondents through closed-ended questionnaires, and statistical analysis software was used, employing binary regression to explore the relationship between various independent variables (including gender, age, education, marital status, income, residence, experience, reasons, information sources, frequency, and expenses) and the price war strategy, which serves as the dependent variable. Qualitatively, in-depth interviews were conducted with ten purposively selected participants. The qualitative data were analyzed using content analysis and the NVivo software program. The study's results highlight the significant influence of income and information on the price-related decisions in Thailand's online food delivery market. Moreover, consumers in Thailand prioritize factors like price sensitivity, value perception, convenience, promotions, and cultural food preferences when selecting online food delivery services. This understanding is essential for businesses operating in this market to effectively meet consumer preferences and succeed in the competitive online food delivery industry.

The TikTok platform has become widely popular among younger generations and has influenced various industries and aspects of life. This systematic literature review, using bibliometric methods, aims to examine the evolution of TikTok research from its initial development to its current status. A total of 402 journal articles from 2019 to 2022 were systematically selected from the Web of Science Core Collection. Descriptive statistical analysis identified the most contributing academic institutions, nations, journals, scholars, and influential studies. Using CiteSpace, three visualization types were implemented: cluster views, timezone views, and timeline views. The study results indicate that several TikTok research clusters exist, which summarize important topics such as the platform's overall impact on society, politics, culture, as well as human-centric issues such as social attachment, functional tics, and their implications for public health. According to the co-word analysis, TikTok research revolves around body consciousness, social behavior, consumer behavior, and news media. The comprehensive mapping of TikTok research provides valuable insights into the current state of knowledge and identifies numerous relevant technical and social areas for future investigation. As the first bibliometric review of TikTok research, this study offers an important foundation for future scholarly inquiry in various areas.

The explosive growth of decentralized finance (DeFi) has revolutionized the accessibility and functionality of financial services. To gain valuable insights into this evolving ecosystem, analyzing transaction data using network analysis methodology proves to be highly effective. Network analysis allows us to uncover intricate relationships, patterns, and communities within the DeFi market by examining participant transactions. We model nodes group representing actors or wallet addresses, and edges represent transactions between wallet addresses. This study focuses on three prominent DeFi token-based Ethereum protocols: DAI, UNI, and WBTC. We analyzed 5,802,742 transaction data spanning from January 2022 to January 2023 of those three tokens. Using network topology metrics, we uncover market size, average transaction of each wallet address, market density, and reachability (diameter). We also detect the presence of transaction clusters and grouping quality using the modularity metric. At last, we employ centrality calculation to identify the most important wallet address and their role in the market. We gain insight by comparing those three tokens and reveal valuable insight into patterns and relationships, network dynamics, decentralized nature, and the presence of intermediaries in the token economy market. Our objective is to uncover hidden dynamics and trace asset flows, which can provide valuable information to market participants, regulators, and innovators seeking to optimize the DeFi infrastructure, ensure stability, and mitigate risks. Leveraging the power of network analysis offers the potential to shape the future of DeFi by enhancing efficiency, security, and accessibility, thereby fostering financial inclusion and empowering individuals and businesses globally.

Video-on-demand (VOD) service providers face the challenge of making strategic decisions regarding the allocation of movies to specific pricing plan libraries. This study delves into the cannibalization effect that occurs when a movie transitions from the transactional video-on-demand (TVOD) library to the subscription video-on-demand (SVOD) library. We conducted a comprehensive analysis using user purchasing and viewing data for 1541 movies offered by SK Broadband, South Korea's leading VOD service provider. Our analysis employed propensity score matching and difference-in-difference methodologies. The results of our initial analysis reveal that allocating movies to SVOD leads to an 192% increase in the number of views while simultaneously 52% decreasing TVOD sales. Additionally, we investigate the characteristics of movies that are particularly effective for SVOD servicing. To accomplish this, we scrutinized 236 movies available in SK Broadband's SVOD service using a cost-effectiveness analysis. Our second analysis demonstrates that certain categories of movies, including foreign films, non-G-rated movies, animations, and films not allowed as Download-To-Own (DTO), are more suitable for allocation to SVOD libraries. Furthermore, movies with lower box office viewership numbers exhibit greater success when allocated to SVOD. This study offers insights into the strategic considerations that VOD service providers should embrace, emphasizing a content feature-driven approach to managing pricing plan combinations.

Our research analyzes how the fear related to the Coronavirus disease 2019 (COVID-19) epidemic reinforces the engagement in the use of Internet of Things (IoT) devices for healthcare. While prior research has examined the adoption of IoT for healthcare, there is a notable absence of exploration regarding the influence of COVID-19-related factors, such as fear and desire for self-care, on user engagement with IoT devices and their determining factors. A quantitative study using the partial least squares algorithm was conducted to analyze this gap. The mediating role of the desire for self-care between fear and social influences, on the one hand, and engagement in using this technology, on the other, was confirmed by adopting the Macro Process on SPSS.¹ The study results make it possible to put forward several interesting recommendations that facilitate the long-term adoption of the Internet of Things for health. The mediating role of the desire for self-care in the relationship between fear and user engagement explains that IoT users who are afraid of catching COVID-19 will be more concerned about health monitoring, leading them to adopt this technology of IoT devices.

This research introduces an innovative multi-theoretical model that combines Expectation-Confirmation Theory, Theory of Consumption Values, and Theory of Planned Behavior.

The model sheds light on the intricate determinants influencing users' intentions to continue engaging with social media platforms as a post-adoptive behavior. Drawing upon data collected from 425 valid survey responses, this study affirms the effectiveness of this combined model in predicting users' intentions to persist in using social media and emphasizes the central importance of the Theory of Planned Behavior constructs. The theory of Planned Behavior emerges as the primary driver shaping users' intention for continued social media use, surpassing the significance of the Theory of Consumption Values and Expectation-Confirmation Theory constructs. Furthermore, this study enriches our comprehension of the profound importance of all types of consumption values in the social media context, unveiling a hierarchy of these values and their varying impact on users' intentions to maintain their social media engagement. By embracing users' perspectives across a diverse array of social media platforms, this research enhances the generalizability and applicability of its findings, ensuring their relevance in diverse social media contexts. Additionally, the study delves into the moderating role of social media experience, unveiling intriguing dynamics in user behavior and elucidating the distinctions between experienced and less experienced users in their social media usage patterns. The paper explores the theoretical, methodological, practical, and social implications of these findings.

The emergence of ChatGPT in the broader field of generative artificial intelligence (AI) has sparked scholarly discourse on its utilization in various disciplines. Yet, a significant void exists in our understanding of the dynamics of consumer engagement with content creators producing ChatGPT-related content. Therefore, the present study aims to delineate how ChatGPT-related content garners consumer engagement on YouTube. Data from 100 YouTube videos amassing an aggregate of 65 million views on ChatGPT were extracted using three application programming interfaces (APIs), namely, VidIQ, Tubebuddy, and SocialBlade. We subsequently contrasted this dataset with data from 200 other videos produced by the same creators. The data were analyzed using one-way ANOVA, multigroup SEM, and comparative line graphs. Employing the Uses and Gratifications (U&G) theoretical framework, our results indicate that innovative content such as ChatGPT-related videos garners more engagement than other content types from the same YouTube channels. Intriguingly, this study finds that ChatGPT-focused content exhibited diminished sensitivity to channel subscriber counts, with channels having fewer subscribers achieving higher viewership numbers. Furthermore, ChatGPT-related content induced a surge in new subscribers to the channel compared to the other content types. The present study pioneers the investigation of audience engagement with ChatGPT-related content by juxtaposing it with other content from the same YouTube channels. We also explicate the relationship between content sensitivity and extant subscriber counts. The present study provides vital insights and implications for a diverse audience, including content creators, developers of AI tools, advertisers, and content publishers.

The challenge of failure is a well-known issue for many founders of digital start-ups during market entry. Unfortunately, sub-Saharan Africa has experienced a particularly high rate of failure for digital start-ups during early-stage development. Between 2010 and 2018, an average of 54.20% of digital start-ups failed on the continent. Globally, the survival rate during early-stage development is only around 10% to 20%, which means that roughly 80% of digital start-ups fail during market entry. To address this issue, it's critical to investigate the factors that contribute to the few successes, especially in sub-Saharan Africa. This study focuses on the behaviour of Nigerian founders whose digital start-ups survived during market entry. The goal is to explore how these founders overcame the various complexities and uncertainties that come with entrepreneurial activities in the Nigerian market. The researcher conducted a qualitative case study research, using enactment theory and effectuation theory to investigate the underlying factors that drive the actions and practices of founders to the early-stage survival of digital start-ups in Nigeria.

In recent years, the concept of the metaverse has evolved significantly, with the aim of defining richer immersive and interactive environments that can support various types of virtual experiences and interactions among users. This evolution has given rise to several metaverse platforms that utilize blockchain technology and non-fungible tokens (NFTs) to establish ownership of metaverse elements and attach features and information to them. This article seeks to delve into the complexity and heterogeneity of the data involved in these metaverse platforms and highlight some of the dynamics and features that make them unique. Additionally, the paper introduces a metaverse analysis tool developed by the authors, which leverages machine learning techniques to collect and analyze daily data, including blockchain transactions, platform-specific metadata, and social media trends. The experimental results of our approach are presented with a use-case scenario focused on the trading of digital parcels, commonly referred to as metaverse real estate. This scenario allows us to demonstrate the effectiveness of our tool and showcase the potential of using machine learning techniques to analyze and gain insights into the metaverse ecosystem.

Small and medium-sized enterprises (SMEs) face obstacles in raising capital and accessing financial services due to information asymmetry, high transaction costs and lack of collateral. FinTech developments have made gathering and sharing information easier, changed how funds are mobilized and allocated, and increased capital-raising activities. This paper conducts a systematic literature review on FinTech and SME financing for the period 2008–2022. So far there are unstructured and separate publications on this topic. Therefore, there is a need to consolidate the empirical research and their findings on the effectiveness of FinTech in meeting SMEs' financing needs. The bibliometric findings show that few studies on FinTech and SME financing are empirical in nature. These empirical studies surged a decade later after FinTech 3.0 started in 2008, with the majority of them using quantitative methods based on data from surveys and FinTech platforms. Furthermore, emerging alternative digital financing to SMEs have attracted more empirical studies than those on FinTech and bank lending to SMEs. In terms of publications, China is dominating, followed by the United States. The content analysis shows that FinTech has increased the ability of financial and non-financial institutions to collect and process accurate information about SMEs, thus reducing information asymmetry and transaction costs. FinTech has also increased the speed and quality of the lending cycle, from establishing an SME pipeline, collecting and processing information, to loan screening, monitoring and repayment. Finally, the paper presents research gaps and areas for future studies, challenges and policy recommendations on this novel subject.