The impact of influencers' multi-SNS use on followers’ behavioral intentions: An integration of cue consistency theory and social identity theory

The considerable marketing potential brought by the rapid growth of social media has been extensively examined in the literature, particularly in terms of the impact of influencers' recommendations on their followers' decisions. However, little attention has been given to how the individual influencers' use of multiple social networking sites affects their followers' behavioral intentions. Hence, this academic gap warrants further exploration. Drawing on cue consistency theory, social identity theory, and stimulus-organism-response framework, this research proposes three research questions and constructs an integrated research model to explore how the influencers' cue consistency affects the social identification of their followers, subsequently influencing their behavioral intentions. A structural equation modeling technique is performed to analyze the data of 510 valid respondents. The findings show that influencers' consistent cues (i.e., information and image consistency) across social media have positive impacts on followers' cognitive and affective identification toward the influencers. Furthermore, the determinants for followers' purchase intention and electronic word-of-mouth intention are affirmed to serve as both cognitive and affective identification. Especially, the results indicate that the relationships between followers’ social identifications and behavioral intentions are moderated by the type of influencer (i.e., lifestyle vs. review). Accordingly, the results indicate how the use of multiple social networking sites by influencers impacts the behavioral intentions of their followers. The findings offer new insights into influencer marketing and provide important lessons for marketers.     

Evaluating discounts as a dimension of customer behavior analysis

Today, increased competition between organizations has led them to seek a better understanding of customer behavior through identifying valuable customers. Customers’ expectations about the price and quality of products and services play an important role in their selection process. In online businesses, competition and price differences between suppliers is high, so discounts will attract different customers. As a result, discounts and the frequency and amount of purchases can lead to better understanding of customer behavior. Customer segmentation and analysis is essential for identifying groups of customers. Hence, this study uses a model based on RFM called RdFdMd, in which d is the level of discount used to analyze customer purchase behavior and the importance of discounts on customers’ purchasing behavior and organizational profitability. The CRISP-DM and k-mean algorithm were used for clustering. The results indicate that using the R_dF_dM_d model achieves better customer clustering and valuation, and discounts were identified as an important criterion for customer purchases.     

Plausible versus implausible tensile price claim: Selective accessibility model approach

Retailers frequently use exaggerated price discount advertisements with a tensile price claim (TPC; e.g., “Save up to 70%”) to attract consumers because they expect that once consumers enter a store, they will purchase low‐ or medium‐discounted products. Drawing on the selective accessibility model, this study investigated the way in which an implausibly high maximum level of savings stated in a TPC influences consumers’ expected price discount (EPD) and perceptions of actual price discounts across different types of TPCs (i.e., TPC stating a maximum level and TPC stating a range of savings). This study also investigated two situations in which consumers have previous knowledge of a product’s price discount versus when they have less or no knowledge of the discount. For both conditions, a single‐anchor TPC (i.e., “Save up to Y%”) that stated an implausible maximum level of savings led to a higher EPD and lower perceptions of the deal (i.e., perceived savings, price fairness, and perceived value) with respect to the actual price discount than did a TPC with a plausible maximum level of savings. In contrast, when the TPC stated two anchors (i.e., “Save X–Y%”) and consumers had knowledge of the price discount, their EPDs assimilated only toward the plausible anchor (X), and ignored the implausibly high maximum price discount (Y), resulting in a lower EPD and higher perceptions of the deal of the actual price discounts than a TPC that stated a plausibly high maximum level of savings. In contrast, when consumers had no knowledge of the price discount, their EPDs only adjusted toward the more plausible anchor (X), regardless of whether they perceived the maximum anchor as plausible or implausible. Thus, there was no difference in consumers’ perceptions of “Save X–Y%” between implausibly and plausibly high Y%.     

Industry risk transmission channels and the spillover effects of specific determinants in China’s stock market: A spatial econometrics approach

This paper uses a spatial econometrics approach to study the industry risks in China’s stock market. We comprehensively consider the real linkage and information risk transmission channels and analyze the risk spillovers of specific determinants. Our empirical results show the following: 1) The real linkage channel and information channel are both effective transmission channels for driving spillover effects, and the information channel is of the utmost importance. 2) The spillover effects of specific determinants exist and are persistent. The superposition of spillover effects may lead to extreme risk. 3) The transmission channels and spillover effects are asymmetric in different regimes.     

Multigroup validation of the service quality,customer satisfaction and performance links in higher education

The notion of ‘students as customers’ continues to be prominent, yet is insufficiently explored, especially from the standpoint of various stakeholders. To address this inadequacy, the present study employs a multigroup analysis of the service profit chain (SPC) model in higher education (HE). Its purpose is to examine the complete SPC model regarding stakeholder perceptions in order to inform its validation and implementation. A cross-sectional survey was employed in order to enable multigroup comparison of a comprehensive research model on subsamples of employees and students by means of structural equation modeling (SEM). Results provide support for the proposed SPC model within the sample of vocational colleges. Besides strongly linked constructs (quality–satisfaction–loyalty), some notable weaknesses (cracked ‘satisfaction mirror’) are found. Multigroup analysis also indicates some important differences between employees and students. Implications are provided for strategic service management in HE, which should acknowledge the differences among stakeholder perceptions.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

The mechanism of credit risk contagion among internet P2P lending platforms based on a SEIR model with time-lag

The credit risk contagion of Internet peer-to-peer (P2P) lending platforms is an important part of Internet financial risk management and supervision. This study analyzes the contagion path of credit risk in Internet P2P lending. Based on complex network theory and the theory of infectious disease dynamics, the characteristics of Internet P2P lending development are combined to construct a SEIR model of credit risk transmission among Internet P2P lending platforms with time lag, and the robustness of the model is analyzed and proven. The influence of platform correlations, the susceptible immune rate, the platform elimination rate, contagion latency, the saturation coefficient, and the susceptibility input rate on credit risk contagion behavior among Internet P2P lending platforms is analyzed, using the equilibrium point and threshold value. The impact of each variable is analyzed by simulation. Corresponding countermeasures and suggestions are proposed to prevent and control credit risk contagion among these platforms.     

中国零售业自营与联营问题的流通经济学分析

零售业自营与联营之争由来已久，在当前供给侧结构性改革、完善促进消费体制机制以及零售业寻求创新发展的背景下，有必要对这一问题展开进一步的理论探讨。文章结合马克思流通经济理论指出，自营和联营的本质区别在于是商业资本还是产业资本承担商品流通职能，自营和联营并不是非此即彼的互斥关系，但如果零售业完全倚重其中一种模式并在全社会推向极端，则需要反思商业资本和产业资本是否各得其所和高效分工。在中国流通体制变革中，虽然联营模式在特定历史时期帮助众多零售企业渡过生存危机并推进内资零售快速扩张，但发展至今，已呈现零售业普遍联营的情况，使零售业整体面临着制约创新发展的新问题。数字经济为零售业回归自营提供了有利契机，零售企业应充分利用新技术摆脱联营制的路径依赖，依托数字化开展深度自营并全面提升流通效率。     

Evolution of indigenous tourism among the Lacandon of Chiapas: an application of Weaver’s model

ABSTRACT

In this paper, Weaver’s six-stage indigenous tourism model is applied to the Lacandon Maya (Hach Winik) of Chiapas, Mexico. Based on a comprehensive review of the anthropological and historical literature on this indigenous group, combined with longitudinal ethnographic and collaborative research performed with tourism entrepreneurs, the Lacandon tourism experience is assessed from the pre-European period until present. By analysing a case study of indigenous tourism in Mexico, a developing country in another geographical region and with a different colonial past, this work supplements Weavers’ perspective. The results show that the fourth and fifth stages of Weaver’s model coincide in this case study, while the sixth stage is still incomplete. Although the Lacandon case has its peculiarities and bearing in mind that several different factors should be considered in the Latin American context, the model proves to be an interesting tool for indigenous tourism analysis in developing countries.     

A hybrid information approach to predict corporate credit risk

This study proposes a hybrid information approach to predict corporate credit risk. In contrast to the previous literature that debates which credit risk model is the best, we pool information from a diverse set of structural and reduced‐form models to produce a model combination based on credit risk prediction. Compared with each single model, the pooled strategies yield consistently lower average risk prediction errors over time. We also find that while the reduced‐form models contribute more in the pooled strategies for speculative‐grade names and longer maturities, the structural models have higher weights for shorter maturities and investment grade names.