Search Results (697)

The increasing popularity of online job vacancies and machine learning methods has raised questions about their combination to enhance our understanding of labour markets and algorithms. However, the lack of comparable studies necessitates further investigation. This research aims to explore the effectiveness of Random Forest Regressor (RFR) and Support Vector Regressor (SVR) machine learning models in predicting online job vacancies compared to the auto-regressive ARIMA method. To answer this question, detailed sub-questions are posed in relation to the sub-samples of the main data provided by Birch Consultants, an external partner originally obtained by Jobdigger. Drawing upon previous research on time-series accuracy, this study combines various approaches to benefit society and the external partner. Using the walk-forward validation method, with a 91-day expanding window, it provides precise answers to the sub-questions. Findings suggest that RFR is suitable for forecasting larger samples, while SVR is preferred due to its capability to predict small series despite relatively small scoring benefits and computational costs. Both machine learning models outperform the baseline ARIMA model in capturing complex time-series. Further research should focus on exploring advanced auto-regressive, deep learning, and hybrid models for future investigations. Full article

The Lightning Network (LN), a second-layer protocol built on top of the Bitcoin blockchain, is an innovative digital payment solution that offers increased convenience, speed, and cost-effectiveness to consumers and businesses alike. However, there is limited literature available on the characteristics of this nascent technology, the depth and breadth of the various business LN-related applications as well as relevant adoption/implementation challenges. This study aims to contribute to the understanding of the LN’s characteristics, its potential in enhancing business operations and its applicability across different sectors, while taking into account adoption and implementation challenges. We apply a narrative review methodology using a semi-systematic approach to examine new and emerging business models empowered by the LN and its characteristics, topology, performance, privacy and security. We analyze the data to identify key themes and trends in the literature, offering a critical analysis of the strengths and weaknesses of the existing literature. Based on the findings, we provide several clusters of fruitful areas for future research directions. This study not only provides crucial insights for businesses contemplating the adoption of LN to improve their operations and customer experience, but it also represents a substantial academic contribution, offering valuable knowledge and fostering further research in the fields of blockchain technology, FinTech and cryptocurrencies. Full article

In light of the development of online travel agencies (OTAs), the rules of the entire tourism industry have changed. Due to the ease of finding information and comparing products, consumers can choose a hotel not only by room type, but also by rate, according to their preferences. The purpose of this study was to explore the effect of price discrimination on the fairness perception toward reservation intentions. The interaction effects of the brand familiarity and the type of consumers on the fairness perception were also examined. The study used an experimental design, with 2 price discriminations × 2 brand familiarities × 2 regulatory focuses, on a total of 320 valid subjects. The results showed that advantaged-price discriminations had higher fairness perceptions than equal-price discriminations, and that higher fairness perceptions had higher reservation intentions. The interaction effect of brand familiarity showed no significant impact on the fairness perceptions, while the regulatory focus had a mitigating effect on the price discrimination and on the fairness perceptions. This study provides insights into the relationship between online price discrimination and tourism, and it contributes to the literature on hospitality. It also provides the managerial implications for online hotels in developing pricing strategies. Full article

Digital content marketing (DCM) complements traditional marketing communication approaches and is a major focus of research. Uses and gratifications research posits that DCM only unfolds positive effects if it provides valuable content to consumers. However, there is limited evidence on what constitutes gratifying digital corporate content on company websites. This study aimed to elicit consumers’ preferences for key characteristics of digital corporate content on company websites and whether preferences differ among consumer subgroups. Best–worst scaling (BWS) was used to reveal preferences. To obtain BWS data, a cross-sectional survey was employed. The study sample comprised 1527 consumers from Germany, Switzerland, and Austria. Data were analyzed using counting analysis and conditional logit modeling. Subgroup comparisons were performed with t-tests and one-way ANOVA. The results consistently show that consumers prioritize information value as the most important content characteristic, followed by value in use, entertainment value, process value, and social value. Subgroup comparisons revealed generally similar priorities among consumers, with the greatest heterogeneity being found in assessments of the importance of social value. The study also suggests that consumers prioritize digital corporate content characteristics on company websites differently than they do on social media. These findings contribute to the evolving literature on DCM and provide insights that could help set evidence-based priorities in DCM practice. Full article

The COVID-19 pandemic has altered consumer behavior, making social commerce a viable alternative throughout the world. Europe is trailing the US and China in adopting this technology, but the prognosis is encouraging. Our goal is to contribute to this process by offering a literature review on social commerce in Europe for researchers, practitioners, and policymakers. We analyzed 4.764 articles published during the 2015–2023 period on the topic of social commerce in Europe utilizing the PRISMA flow diagram. After scrutinizing this large body of literature with various instruments including artificial intelligence (AI), we identified a final list of 45 articles that are most pertinent to our research questions. The emerging themes were that social media is shaping behavior and triggering buying intentions, that trust is paramount in buying impulses and behavior, and that success in social commerce is predicated upon relationships and engagement. Full article

User-generated content (UGC) from e-commerce platforms and third-party platforms can impact customer-perceived risk and influence product sales in online stores. However, the understanding of UGC from which platform type yields a stronger effect on product sales and how the effects interact across the platforms remains limited. This limitation arises from the complexity of consumer purchasing behavior and information processing, as well as the heterogeneity of UGC features across different platforms and the uncertainty surrounding causal relationships. This study constructs a novel cross-platform framework using the elaboration likelihood model (ELM) to investigate the underlying mechanism of how cross-platform UGC affects online sales of experiential products. Additionally, it examines the mediating effect of purchase intention in the relationship between cross-platform UGC and product sales, as well as the moderating effect of product price. Taking the e-commerce platform Tmall and third-party platform Bilibili as a cross-platform example, we analyzed customer reviews on Tmall and vlogger reviews on Bilibili for 300 cosmetic products, using text sentiment analysis and multiple regression. Results show that the number of product evaluations from third-party platforms positively impacts sales, but this impact is weaker compared to the influence of UGC originating from e-commerce platforms on sales. The underlying mechanism refers to the process by which UGC on an e-commerce platform directly impacts sales and also influences sales through purchase intention. In contrast, UGC on third-party platforms only influences sales through purchase intention. Furthermore, the product price has no significant moderating effect on the positive relationship between review length and sales. This study provides a cross-platform UGC research framework that can guide effective cross-platform marketing management by shedding light on the role of UGC in reducing customer-perceived risk and its impact on online sales of experiential products. Full article

Although the market for Head-Mounted Display Virtual Reality (HMD VR) devices has been growing along with the metaverse trend, the product has not been as widespread as initially expected. As each user has different purposes for use and prefers different features, various factors are expected to influence customer evaluations. Therefore, the present study aims to: (1) analyze customer reviews of hands-on HMD VR devices, provided with new user experience (UX), using text mining, and artificial neural network techniques; (2) comprehensively examine variables that affect user evaluations of VR devices; and (3) suggest major implications for the future development of VR devices. The research procedure consisted of four steps. First, customer reviews on HMD VR devices were collected from Amazon.com. Second, candidate variables were selected based on a literature review, and sentiment scores were extracted. Third, variables were determined through topic modeling, in-depth interviews, and a review of previous studies. Fourth, an artificial neural network analysis was performed by setting customer evaluation as a dependent variable, and the influence of each variable was checked through feature importance. The results indicate that feature importance can be derived from variables, and actionable implications can be identified, unlike in general sentiment analysis. Full article

Conversational robots powered by artificial intelligence (AI) are intensively implemented in the insurance industry. This paper aims to determine the current level of acceptance among consumers regarding the use of conversational robots for interacting with insurers and seeks to identify the factors that influence individuals’ behavioral intention to engage with chatbots. To explain behavioral intention, we tested a structural equation model based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. It was supposed that behavioral intention is influenced by performance expectancy, effort expectancy, social influence, and trust, and by the moderating effect of insurance literacy on performance expectancy and effort expectancy. The study reveals a significant overall rejection of robotic technology among respondents. The technology acceptance model tested demonstrates a strong ability to fit the data, explaining nearly 70% of the variance in behavioral intention. Social influence emerges as the most influential variable in explaining the intention to use conversational robots. Furthermore, effort expectancy and trust significantly impact behavioral intention in a positive manner. For chatbots to gain acceptance as a technology, it is crucial to enhance their usability, establish trust, and increase social acceptance among users. Full article

Streamers play a critical role in fostering consumer stickiness in live streaming sales. Thus, it is necessary to make clear the mechanism of how streamers influence consumer stickiness. Based upon the theories of social support, social identification and consumer stickiness, this study investigates the effects of consumers’ perceived emotional support, informational support, financial support, affectionate support and social network support from streamers on consumer–streamer identification, which in turn affects consumer–streamer stickiness and consumer–brand stickiness in live streaming sales settings. Based on the structural equation modeling analysis of 280 online questionnaires, using the software of Smart PLS 3.0, the results demonstrate that perceived emotional support, perceived informational support, perceived financial support and perceived affectionate support enhance consumer–streamer identification, thereby enhancing consumer–streamer stickiness and consumer–brand stickiness, and thus, consumer–streamer stickiness also enhances consumer–brand stickiness. This study not only extends the theories of live streaming sales, but also provides practical implications for enterprises’ improving consumer–streamer stickiness and consumer–brand stickiness in live streaming sales. Full article

The current sales model combining online and offline channels meets the diverse requirements of consumers. However, consumers’ inter-channel switching behavior and strategic behavior also pose significant challenges to pricing decisions in the hybrid channel. Using game theory and consumer utility theory, a retailer-driven pricing model is developed to study the optimal pricing problem for each channel in a mixed-channel supply chain considering the characteristics of channel competition and the waiting behavior of strategic consumers. Study results show there is a negative correlation between the proportion of strategic consumers and the optimal pricing and profit of each channel, and as the proportion of strategic consumers rises, the optimal pricing and profit of manufacturers and retailers all trend downward. Incorporating strategic consumers into the pricing model will assist the supply chain in elucidating the behavior of consumer heterogeneity during various decision-making periods and in making reasonable pricing decisions. Effective guiding strategies, such as pre-discount and purchase restrictions, can reduce the profit loss caused by strategic consumer behavior. The optimal combination of pre-announcement discount and strategic consumer ratio can generate the greatest profit for retailers and the supply chain. Full article

Condominiums and similar properties use a stratum to manage daily operations, and owners fund it through strata fees. While existing strata fee management systems may be able to handle such funds, such systems could be more inherently transparent. It is possible to leverage the digital ledger from blockchain networks and smart contracts to build a fully transparent strata fee management system. This paper proposes designing a strata fee management system based on a smart contract in the Ethereum network. Both strata corporations and homeowners can interact with the smart contract to execute common procedures such as paying strata fees and handling expenses. Using smart contracts for strata fee management, it is believed that the chance of fraud by strata corporations is lowered compared to other systems. Full article

This study analyzes the fear of COVID-19 effect on European stock market returns. For this purpose, the search volumes (SV) collected by Google Trends (GT) and Wikipedia were used as proxies of fear of COVID-19. In a sample from 13 European stock markets, fear of COVID-19 was found to be associated with negative European stock returns. Our research employed this observation to propose an algorithmic trading system based on fear of COVID-19. Back-testing results show the possibility of extraordinary returns based on this system. These findings have important implications for political authorities, the mass media, and investors. Full article

Live-streaming e-commerce is the future development direction of the retail industry. When retailers choose a live-streaming e-commerce platform, they face the test of various risks of the platform, such as insecure control of capital flow, insufficient support of public domain traffic, etc. Therefore, it is necessary to evaluate the risks of the platform to help retailers identify the platform with the lowest risk. Considering the complexity of the risks of live-streaming e-commerce platforms and the ambiguity of the decision-makers thinking, the current method for multi-criteria group decision-making (MCGDM) method in a fuzzy environment rarely discusses the decision-makers weight for the criterion. This paper proposes interval-valued intuitionistic fuzzy multi-criteria group decision-making based on the decision-makers’ professionalism to evaluate the platform’s risks. This method determines the decision-maker’s weight for the criterion based on the professionalism of the decision-maker and uses the technique for order preference by similarity to an ideal solution (TOPSIS) method to rank the alternative platforms. Finally, a risk assessment of the agricultural product live-streaming e-commerce platforms is used as a case study to demonstrate the feasibility and effectiveness of the proposed method. This research will not only provide practical guidance for retailers to choose the live-streaming e-commerce platform with the lowest comprehensive risk but also provide ideas for the research of live-streaming e-commerce from the perspective of risk assessment. Full article

Mobile streaming is increasingly viewed as a major advancement in the wireless industry, as it enables users to consume content without any time or space restrictions. Mobile TV serves as an excellent example of streaming, providing services for watching TV content on mobile devices. While previous studies have explored video-on-demand (VOD) purchasing factors in mobile TV, it is rare to find research examining differences based on users’ mobile TV usage types, such as subscribers and free users. Consequently, we investigated VOD purchase factors for 310 subscribers and 311 free mobile TV users. In other words, using 621 survey responses, we analyze the influence of personality traits, intrinsic and extrinsic motivations, and mobile-related factors on users’ VOD purchase intentions and behavior. Our findings indicate that mobile TV utilization, hedonic needs, and subjective norms are positively related to VOD purchases, and that neuroticism, extraversion, openness, and conscientiousness positively impact mobile TV utilization. We also examine the relationships between the constructs within two sub-groups to highlight the differing perceptions and behavioral patterns of these groups regarding mobile TV utilization and VOD purchases. Theoretical and practical implications are discussed as well. Full article

Artificial intelligence-powered recommendation systems have gained popularity as a tool to enhance user experience and boost sales. Platforms often need to make decisions about which seller to recommend and the strength of the recommendation when conducting recommendations. Therefore, it is necessary to explore the recommendation strategy of the platform in the case of duopoly competition. We develop a game model where two competing manufacturers sell products through an agency contract on a common platform, and they can decide whether or not to provide recommendations to the manufacturers. Our highlight lies in the endogenous recommendation strength of the platform. The findings suggest that it is optimal for the platform to offer recommendation services when the commission rate is high. The platform also prefers to only recommend one manufacturer in the market with low or high competition, but it prefers to recommend both manufacturers in moderately competitive markets. From the view of manufacturers, they can benefit from the recommendation service as long as the commission rate is not too low. Moreover, recommending only one manufacturer consistently yields stronger recommendations compared to recommending multiple manufacturers. However, the impact of recommendation on prices is influenced by the commission rate and product substitutability. These results have significant implications for platform decision making and provide valuable insights into the trade-offs involved in the development of recommendation systems. Full article