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Gamification is a strategic approach employed by practitioners to foster meaningful engagement and enhance the acceptance of recommendations. Gamification affordances (e.g. achievement, self-expression, interaction, and cooperation) catalyze significant psychological processes in consumers, leading to behavioral changes. Despite its application, a gap remains in understanding how these gamification affordances in e-commerce contexts impact customers' perceived values and drive recommendation acceptances.

Employing affordance theory and perceived value theory as our foundation, we have crafted a comprehensive framework that addresses the multifaceted nature of e-commerce gamification, thereby unifying the fragmented knowledge in this area. We implemented a quantitative research design to empirically test the proposed model.

The research reveals that the four principal affordances of gamification – achievement, self-expression, interaction, and cooperation – significantly enrich consumer values across hedonic, utilitarian, and social dimensions. This enrichment facilitates an increased propensity for accepting recommendations.

This study provides a novel lens through which to view the influence of gamification affordances on recommendation acceptance within gamified e-commerce settings. It delineates the effects of each affordance on consumers' perceived value and highlights the pivotal affordances that shape gamified e-commerce experiences. These insights yield actionable strategies for practitioners aiming to refine e-commerce gamification designs and cultivate more engaging consumer interactions.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

This paper aims to explore the impact of live streamer authenticity (LSA) on purchase intention in tourism e-commerce live streaming, with a focus on boundary conditions and underlying mechanisms.

The data collected from 451 participants were analyzed using structural equation modeling.

This paper found that four dimensions of LSA – sincerity, truthfulness endorsement, expertise and uniqueness – positively influenced purchase intention, while visibility did not. In addition, sincerity, truthfulness endorsement and uniqueness had an indirect influence on purchase intention through flow experience, while sincerity, truthfulness endorsement, expertise and uniqueness had an indirect effect through perceived trust. Furthermore, self-construal moderated the effect of sincerity and truthfulness endorsement on purchase intention, with the positive effect being stronger for the dependent self-construal.

To the best of the authors’ knowledge, it is the first study to examine LSA dimensions and their consequences. This paper not only provides a better and more detailed understanding of the complexity of LSA but also contributes to the development of authenticity theory by responding to individual authenticity studies.

本文旨在研究旅游直播电商中主播真实性对旅游购买意向的影响, 及其内在机制和边界条件。

本文采用问卷调查的方法收集451名参与者的数据。然后用结构方程模型（SEM）对收集的数据进行分析。

本文发现, 主播真实性的四个维度–真诚性、真实背书、专业性和独特性–对购买意愿有积极影响, 而主播的透明性没有影响。此外, 真诚性、真实背书和独特性通过心流体验对购买意愿产生间接影响, 此外真诚性、真实背书、专业性和独特性还通过感知信任产生间接影响。此外, 自我建构调节了真诚性和真实背书对购买意向的影响。对于依赖型自我建构, 真诚性和真实背书对购买意愿的影响更强。

本文是最早探讨主播真实性维度及其后果影响的研究之一。本文不仅对主播真实性的复杂性有了更好更详细的了解, 而且响应了对个体真实性研究的呼吁, 为真实性理论的发展做出了贡献。

Este artículo explora el impacto de la autenticidad del streamer en la intención de compra en la retransmisión en directo de comercio electrónico turístico, centrándose en las limitaciones y los procesos subyacentes.

Se analizaron los datos recopilados de 451 participantes utilizando un modelo de ecuaciones estructurales (SEM).

Este trabajo halló que cuatro dimensiones de la autenticidad de los streamers en directo -la sinceridad, el respaldo a la veracidad, la pericia y la singularidad- influían positivamente en la intención de compra, mientras que la visibilidad no lo hacía. Además, la sinceridad, el respaldo de veracidad y la singularidad influyeron indirectamente en la intención de compra a través de la experiencia de flujo, mientras que la sinceridad, el respaldo de veracidad, la pericia y la singularidad tuvieron un efecto indirecto a través de la confianza percibida. Además, la autoconstrucción moderó el efecto de la sinceridad y el respaldo de veracidad sobre la intención de compra, siendo el efecto positivo más fuerte para la autoconstrucción dependiente.

Es el primer estudio que examina las dimensiones de la autenticidad de los retransmisores en directo y sus consecuencias. Este trabajo no sólo proporciona una comprensión mejor y más detallada de la complejidad de la autenticidad de los streamers en directo, sino que también contribuye al desarrollo de la teoría de la autenticidad al responder a los estudios sobre autenticidad individual.

The paper aims to examine the digital economy's influence on China's regional innovation and development. It focuses on direct effects and spatial spillover across regions, and the mediating role of human capital. This analysis is vital for policy and strategic planning in the digital era.

This study uses panel data from 30 Chinese provinces (2004–2019) and uses the entropy method to quantify the digital economy's development. It investigates its impact on regional innovation using a dynamic spatial Durbin model (SDM) and mediation effect model, assessing direct effects, spatial spillover and human capital's mediating role. Various control variables are included for comprehensive analysis.

Findings show the digital economy significantly boosts regional innovation, acting as a growth driver. However, impacts vary regionally, with the central region gaining more than the eastern and western areas. Spatial spillover effects are mixed, showing negative short-term and positive long-term impacts under different weight matrices. Human capital is crucial for fostering innovation through the digital economy.

The paper offers unique insights into the spatial dynamics of the digital economy's impact on regional innovation in China. It advances understanding of the digital economy's role in regional development using innovative methods like the entropy method and dynamic SDM. Highlighting human capital as a key mediating factor enriches discussions on digital economy strategies for regional innovation.

Understanding the effect of assortment composition on attentional levels is an essential topic for academic researchers and practitioners. This work has important implications when analyzing the influence of shopping frame time and search effort on the relationship between the reaction to assortment composition and visual attention to stock-keeping units (SKUs) pricing.

Two experimental studies through gauze behavior analysis technology (using eye-tracking equipment) analyze the variable's large assortment, visual attention to SKU pricing, search effort and shopping frame time.

The results suggest that, although it increases the search effort, a large assortment decreases the visual attention to SKU pricing. Further, our results indicate a moderating effect associated with mitigating the negative effect by medium-low levels of search effort and a moderating impact of time in this relation.

Marketing professionals can carefully optimize the in-store experience by managing the assortment and variety and by influencing consumers' visual attention to SKU pricing along the journey as part of the experience. Assortment and SKU pricing strategies need to be aligned with consumer journey design.

Our findings contribute to assortment theory and management by detailing the relationship between consumers' reactions to assortment perception and visual attention to SKU pricing in time flow. We reinforce the importance of considering assortment strategies from the consumer perspective and giving reliable information about in-store behavior.

Although various booking platforms have been contributing to the dramatic growth of hotel industry, little research has been conducted to understand consumer psychological processes and behaviors in online hotel booking. To fill this gap, the current study examines the effect of switching barriers (switching cost and alternative attractiveness) on consumers' decision postponement and repurchase intention. Additionally, the moderating effect of time pressure in different phases of booking decision is investigated.

A total of 352 samples was collected through an online platform. Data analysis was conducted via Amos 23 (structural equation modeling) and SPSS 24 (descriptive analysis and PROCESS macro).

Results show that switching cost and alternative attractiveness are two significant drivers of decision postponement and repurchase intention. Meanwhile, time pressure only has a significant moderating effect on the relationship between switching cost and decision postponement.

The findings of this research reveal that hotel operations need to implement strategies to prevent customers' delayed booking decisions and overcome the influence of time pressure on customer decision-making.

These findings stress the importance of consumer perceptions of switching barriers and time span when making hotel reservations online. Hotel practitioners are encouraged to provide multiple human–computer interaction applications to attract novice consumers and increase their familiarity with booking process.

This paper investigates the buy online and pick up in-store cooperation (BOPSC) of online and offline retailers. Specifically, this study solves the following questions: (1) What is the impact of BOPSC on their optimal price and sales volume of products? (2) When should an online retailer and an offline retailer conduct the BOPSC strategy with each other?

The paper first establishes two game models to explore the equilibriums of online and offline retailers in non-BOPSC and BOPSC. Then the condition for online and offline retailers to implement BOPSC strategy are determined. Furthermore, the applicability of the BOPSC strategy is enhanced by incorporating numerical analysis.

The study’s findings reveal that BOPSC strategy will not always beneficial to online and offline retailers, which depends on the total cost of online shopping and the product valuation of consumers. BOPSC strategy leads to the increase of prices and online orders, and the demand of offline retailer is eroded. Moreover, BOPS cooperation between different retailers is easier to achieve than omni-channel integration strategy. When the convenience difference between offline shopping and BOPSC pick-up is moderate, the effectiveness of BOPSC strategy can be improved.

This study has the following two main contributions: Firstly, the authors investigate the effects of BOPSC strategy on the prices of online and offline retailers. The study results show that the BOPSC strategy alleviates price competition and promotes a win–win situation between online retailers and offline retailers. Secondly, this paper mainly studies the cooperative behavior between online and offline retailers and reveals the optimal conditions for online and offline retailers to adopt BOPSC strategy. It can help small- and medium-sized online and offline retailers to choose suitable products for BOPSC strategy, so as to achieve the purpose of increasing profit.

Consumers are assumed to engage in external information search only after exhausting their internal information sources. Guided by the accessibility/diagnosticity and ease-of-retrieval frameworks, and the elaboration likelihood model, the current study investigates this phenomenon.

To test the relationships between internal information accessibility/diagnosticity and the importance of external search, and the moderating role of involvement in these relationships, 308 responses were collected on Amazon MTurk. Then, structural equation modeling was employed to analyze the data.

The analyses showed that while accessibility and diagnosticity of internal information have an impact on external information search, involvement with the product class has a consequential moderating effect on these relationships. In particular, in the low-involvement group, only the diagnosticity of internal information had a negative effect on external information search. On the contrary, in the high-involvement group, only accessibility of internal information had a negative effect.

These findings highlight the possibility of drawing erroneous conclusions resulting from not incorporating involvement, in conjunction with information accessibility and diagnosticity, in the study of the consumer external information search behavior.

The findings also imply that if practitioners aim to prime consumers to engage in external information search, they need to take into account that the effects of internal information's accessibility and diagnosticity on consumers' external search behavior may be different depending on their levels of involvement.

This study's results showed that without considering the moderating effect of involvement, spurious conclusions may be made about the relationships between accessibility and diagnosticity of internal and external information importance. This finding may explain the discrepancy between the accessibility/diagnosticity and ease-of-retrieval frameworks, thus enriching the literature.

This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

The purpose of this study is to investigate the pricing strategies for knowledge payment products, taking into account the quality level of pirated knowledge products, in the context of platform copyright supervision.

This study abstracts the knowledge payment transaction process and aims to maximize producer's revenue by constructing a pricing model for knowledge payment products. It discusses pricing strategies for knowledge payment products under two scenarios: traditional supervision and blockchain supervision. The analysis explores the impact of pirated knowledge products quality level and blockchain technology on pricing strategies and consumer surplus, while providing threshold conditions for effective strategies.

Deploying blockchain technology in platform operations can significantly reduce costs and increase efficiency. In both scenarios, knowledge producer needs to balance factors such as the quality of pirated knowledge products, the supervision level of platform, and consumer surplus to dynamically adjust pricing strategies in order to maximize his own revenue.

This study enriches the literature on the pricing models of knowledge payment products and has practical significance in guiding knowledge producer to develop effective pricing strategies under copyright supervision.