The effect of place attachment of geographical indication agricultural products on repurchase intention

[Objective] This study aims to investigate how place attachment and the quality of experience affect consumers' intention to repurchase Geographical Indication (GI) agricultural products. [Methodology] Based on the special functional value and symbolic meaning of GI agricultural products, we constructed a five-dimensional model of place attachment for GI agricultural products consumers. Expanding on the original two-dimensional place attachment model, we introduced three connection concepts: nature, society, and emotion. We also combined this model with the quality of experience model and consumer purchase intention model to form a research model of repurchase intention for GI agricultural products. A questionnaire survey was used to collect data (n = 340), and a partial least squares structural equation model was used to test the empirical evidence. [Research results] (1) Place attachment to Geographical Indication agricultural products significantly affects the quality of experience. (2) Quality of experience significantly affects consumer trust and repurchase intention. (3) Consumer trust significantly affects repurchase intention. (4) There is a mediating effect of experience quality between place attachment and repurchase intention; there is a mediating effect of trust in the relationship between quality of experience and repurchase intention. (5) There is a chain mediating effect of “place attachment→quality of experience→consumer trust→repurchase intention” in the model.     

Institutional investor horizon and stock price synchronicity: Do product market competition and analyst coverage matter?

This paper provides new insights into the relation between institutional investment horizon and stock price synchronicity and investigates whether this relationship depends on the intensity of product market competition and analyst coverage. Based on a sample of French listed companies, we find that long-term (short-term) institutional investors are associated with lower (higher) stock price synchronicity. The results also show that the negative effect of long-term institutional investors is more accentuated for firms in less competitive markets and with high analyst coverage. An additional analysis shows that the synchronicity reduction effect does not vary during the financial crisis. Overall, these findings suggest that unlike their short-term counterparts, long term investors reduce asymmetric information and help disseminate firm-specific information into stock prices.     

When push comes to shove: How coach versus student athlete misconduct affects event attendance intentions

Though onand off-the-field misconduct is common among U.S. college athletic programs, little is known regarding the ramifications that may result. Drawing on social learning theory, the current research suggests consumers intentions (e.g., likelihood of attending a game) differ depending on violator's team role. Across one qualitative and five experimental studies, we demonstrate that consumers' intentions are influenced by violator's team role, such that likelihood of attending a game is lower when a coach (vs. student athlete) misbehaves, an effect driven by evaluation of the academic institution. This effect is robust across both winning and losing records and moderated by perceived fairness of the university's actions toward the violator.     

On linking the perceived values of mobile shopping apps,customer well-being,and customer citizenship behavior: Moderating role of customer intimacy

Marketing studies that seek to determine the drivers of Customer Citizenship Behavior (CCB) are still relatively rare, especially for its multi-faceted relationship with perceived values in the context of online transactions. This study seeks to bridge this research gap by exploring the relationship between the perceived values of mobile shopping applications (MSApps) and CCB through perceived well-being based upon the social exchange theory. This study also makes another original contribution by investigating the moderating role of customer intimacy. A quantitative approach was adopted by collecting data through a web survey and then applying structural equation modelling, as well as cluster and multigroup analyses to test the proposed hypotheses. Perceived values were found to influence consumer well-being, thus predicting CCB, with consumer intimacy playing a significant moderating role in this relationship. This study is one of a few seeking to verify the drivers of CCB for MSApps, and it is the first of its kind to investigate consumer intimacy as a moderating factor. The findings will prove useful to providers of MSApps and retailers.     

Green consumption behavior prediction based on fan-shaped search mechanism fruit fly algorithm optimized neural network

Predicting consumption behavior is very important for adjusting supplier production plans and enterprise marketing activities. Conventional statistical methods are unable to accurately predict green consumption behavior because it is characterized by multivariate nonlinear interactions. The paper proposes an optimized fruit fly algorithm (FOA) and extreme learning machine (ELM) model for consumption behavior prediction. First, to address the problem of uneven search direction of FOA leading to insufficient search ability and low efficiency, the paper proposes a sector search mechanism instead of a random search mechanism to improve the global search ability and convergence speed of FOA. Second, to address the issue that the initial weights and hidden layer bias values of the ELM are randomly generated, which affects the learning efficiency and generalization of the ELM, the paper uses an improved FOA to optimize the weights and bias values of ELM for improving the prediction accuracy. Taking the green vegetable consumption behavior of Beijing residents as an example, the results show the optimization of the initial weight and threshold of ELM by the GA, PSO, FOA, and SFOA, the prediction accuracy of the GA-ELM, PSO-ELM, FOA-ELM, and SFOA-ELM models all surpass those of ELM. Compared with BPNN, GRNN, ELM, GA-ELM, PSO-ELM, and FOA-ELM models, the RMSE value of SFOA-ELM was decreased by 9.45%, 8.40%, 11.89%, 5.84%, 2.22%, and 2.69%, respectively. These findings demonstrate the effectiveness of the SFOA-ELM model in green consumption behavior prediction and provide new ideas for the accurate prediction of consumption behaviors of other green products with similar characteristics.     

Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities

Artificial intelligence (AI) has captured substantial interest from a wide array of marketing scholars in recent years. Our research contributes to this emerging domain by examining AI technologies in marketing via a global lens. Specifically, our lens focuses on three levels of analysis: country, company, and consumer. Our country-level analysis emphasizes the heterogeneity in economic inequality across countries due to the considerable economic resources necessary for AI adoption. Our company-level analysis focuses on glocalization because while the hardware that underlies these technologies may be global in nature, their application necessitates adaptation to local cultures. Our consumer-level analysis examines consumer ethics and privacy concerns, as AI technologies often collect, store and process a cornucopia of personal data across our globe. Through the prism of these three lenses, we focus on two important dimensions of AI technologies in marketing: (1) human–machine interaction and (2) automated analysis of text, audio, images, and video. We then explore the interaction between these two key dimensions of AI across our three-part global lens to develop a set of research questions for future marketing scholarship in this increasingly important domain.     

科学与技术耦合效果评价——科技互补效应和成本效应分析

考虑到当前创新环境日益复杂化，科学与技术耦合系统已成为一个受系统内外因共同影响而发生演化的开放系统。在为耦合系统构建状态空间模型的基础上，通过求解该模型，刻画科学技术化强度和技术科学化强度，据此分析科学与技术互动产生的互补效应和成本效应，评价29个省份的科学与技术耦合效果，并测度地区科技发展所属耦合路径类型。研究表明，不同省份推进科学技术化进程和技术科学化进程的差异性显著；科学与技术耦合效果最佳并不能说明地区科技发展速度最快；绝大多数省份的科技耦合路径表现为科技中立路径，占比最小的路径为技术强势路径。基于不同省份的耦合情况差异，提出针对性建议。     

中国高端装备制造业自主技术创新效率稳定性及影响因素研究

在全面实施创新驱动发展战略的时代背景下，揭示中国高端装备制造业自主技术创新效率稳定性，测度相关因素的影响强度与方向，对揭示中国高端装备制造业自主技术创新规律，制定其发展策略具有重要指导意义。聚焦中国高端装备制造业自主技术创新效率稳定性及影响因素，采用DEA-Malmquist指数方法和Tobit回归模型进行实证研究，结果表明，中国高端装备制造业自主技术创新效率存在一定波动性，且各细分行业技术创新效率内部稳定性较差，产业自主技术创新效率整体稳定性不高；自主技术创新投入因素和环境影响因素对产业自主技术创新效率的影响具有较高的异质性，其中创新资金投入和人力资源投入对自主技术创新综合效率、纯技术效率及规模效率具有正向影响，而新产品开发投入和企业规模对自主技术创新综合效率、纯技术效率和规模效率的影响具有异质性，政策因素与市场因素分别具有显著和非显著负向影响，外源性创新因素具有显著正向影响，且影响强度较大。在此基础上，提出调整政府对高端装备制造业创新支持模式、加强外源性创新与智力引进等策略建议。     

实现2035年发展目标的潜在增长率*

党的十九届五中全会提出了到2035年人均GDP达到中等发达国家水平的远景目标，因此测算和回答能否和如何如期实现该目标，对于我国实现第二个百年奋斗目标和坚持“四个自信”具有重要的意义。为此，本文根据跨越和陷入“中等收入陷阱”经济体的发展经验，对2021—2035年我国潜在增长率变化进行了测算。一是参照跨越和陷入“中等收入陷阱”经济体在我国相同发展阶段时各主要生产要素的变化，模拟设定我国未来各主要生产要素的增长率；二是通过运用附加人力资本的增长核算模型测算基准、乐观和悲观三种不同情境下未来我国经济的潜在增长率，验证我国2035年发展目标实现的可能性；三是依据主要要素对潜在增长率的贡献度，提出我国如期实现2035年发展目标的相应政策建议。     

银行业联合授信制度制约企业的非效率投资了吗*——基于沪深A股公司的分析

中国银保监会出台联合授信管理制度旨在抑制企业过度融资行为，提高资金使用效率。为检验该制度是否达到预期效果，采用2014—2018年沪深A股上市公司样本，运用非平衡面板数据回归实证考察联合授信机制对企业非效率投资的影响，发现联合授信对企业的非效率投资（包含投资不足和投资过度）呈现显著抑制效应，其中联合授信对投资过度的抑制效应最大。进一步研究发现，相较于国有、大规模、低负债、高盈利等异质属性的企业，联合授信对非国有、小规模、高负债、低盈利等属性企业的非效率投资（尤其是投资过度）抑制效应更为显著。