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Abstract
With the informatization of versatile platforms, e-commerce has become the most popular promotional channel. As the key element of the marketing mix, price strategy plays an almost decisive role in a consumers willingness to pay and consumer satisfaction. Thus, this study will explore the impact of bundling strategy on consumer psychology in big data e-commerce. The result shows that the bundling strategy can take advantage of big data to maximize product value perception and consumer satisfaction. Through the previous literature, the article finds many complex psychological aspects of consumers in bundle pricing under big data e-commerce. However, there are few studies on the specific elements and degree of impact on consumer psychology. Therefore, future studies can focus on using qualitative analysis to explore the influence of bundling strategy on consumer psychology in big data E-commerce and focus on one of the e-commerce platforms.
... It shows that bundling can maximize product value perception and consumer satisfaction. Future research can focus on the specific elements and degree of impact on consumer psychology [22]. ...
The happiness of their customers is the primary focus of quality improvement initiatives, and the happiness index is the primary benchmarking metric used across industries and businesses. The Chinese real-time e-commerce platform Taobao has expanded rapidly. Still, rising tensions among its stakeholders, consumers, retailers, and platform operators pose a significant threat to the industry’s future. Consequently, it is essential to investigate the customer satisfaction model and identify the most influential facets of online shoppers’ experiences. To analyze the evaluation system of consumer happiness in live e-commerce, we use their level of satisfaction during the transaction as our total evaluation index and combine two single assignment methods, the AHP method and the entropy method, via a combined assignment model. A model is developed to assess consumer happiness. In addition, using two standard satisfaction indices in the evaluation, this paper identifies the primary factors influencing the happiness of online consumers. Then, it proposes actionable strategies for platform operators to increase their happiness and retailers to improve customer satisfaction.
This research aims to determine different levels of loss aversion in the context of price responsiveness and service bundling. Considering that nonlinearities in price responses may exist in a bundling strategy, this research tests the existence of different degrees of loss aversion, depending on whether an individual books one service independently of another (e.g., an airline ticket independently of accommodation) or as part of a bundle (e.g., a package that includes an airline ticket plus accommodation). We estimate a random parameter logit model. Empirical application shows that people who book a flight independently of accommodation are more loss averse than those who book a package that includes flight and accommodation. To explain this result, we propose the one-click effect so that people who find a price higher than expected (loss aversion) are more willing to accept it if the product is included in a bundle.
Inaction inertia is the phenomenon in which people are less likely to accept a less attractive opportunity after having missed a relatively more attractive one. Previous studies have mainly explored the inaction inertia effect on single products or services, whereas this study explored how the promotional frames of sales packages influence inaction inertia toward the individual items within the sales packages and the inaction inertia effect of the target product under different price strategies. On the basis of the cost assignments of mental accounting and comparisons of the current inferior promotion with missed superior promotion, this study found evidence that when consumers encounter a freebie (bundle) condition, they assign a higher cost to the focal (supplementary) product and a lower cost to the supplementary (focal) product. Therefore, consumers who have missed a freebie (bundle) promotion exhibit lower (higher) inaction inertia toward the focal product, but higher (lower) inaction inertia toward the supplementary product. Applying a similar internal mechanism to pricing strategies, the findings also show that when consumers encounter a two‐component sales package with a surcharge promotion using a partitioned price (vs. a price discount promotion using an all‐inclusive price), they assign a higher cost to the base product and a lower cost to the surcharge. So, consumers who have missed the surcharge (vs. price discount) promotion show higher inaction inertia when the surcharge of the current inferior opportunity is salient, but show less inaction inertia when it is not salient. Moreover, the percent of a surcharge as a part of the total package value moderates the impact of promotional price strategy frames on inaction inertia. These findings have significant implications for both theoreticians and practitioners interested in inaction inertia, promotional frames, and price strategies.
Retailers often use the promotion strategy of offering supplementary products (e.g., free gift, bundle) to attract consumers and increase sales. Despite the growing literature on the promotions that are differently framed but offer economically identical values, little research has examined the link between promotion framing and consumer product returns. The current article sheds light on this relationship, hypothesizing that a free gift promotion would be superior to a bundle promotion in reducing consumer product returns. The findings suggest that a gift‐framed promotion leads to a lower product return intention than an economically equivalent bundle promotion, because consumers tend to perceive more loss from giving up the gift‐framed (vs. bundle‐framed) deal. Further, this study examines a moderating role of brand familiarity (familiar vs. unfamiliar) and shows that the merits of free gift framing on product return intention via perceived loss are amplified (attenuated) when the promoted brand is familiar (unfamiliar). Overall, the investigations of this study imply that it is better to frame a promotion as a “free gift” than a “bundle” to increase perceived loss in returning the purchase and thus to decrease consumer product returns. This strategic intervention works especially when the gift is offered by familiar brands.
Fast development of IT and ICT facilitate customers to post a large volume of their concerns and expectation online, which are widely accepted to be a valuable resource for product designers. However, it is found that only a small number of small and medium-sized enterprises (SMEs) have capabilities to leverage customer online insights for design innovation, which often demonstrate a significant share in national economies growth. To discover the beneath reasons regarding the barrier that prevent them to make effective utilization, in this study, as a concrete example, manufacturing SMEs in the South Wales and Greater Manchester industrial areas of the UK are focused and their potential motivations for using and knowledge of big data-based customer analytics are investigated. An exploratory survey was conducted in terms of the type of customer data they have, the storage approaches, the volume of customer data, etc. Next, a carefully devised exploratory study was undertaken to understand how SMEs perceive the relations between customer data and product design, how about their expectations from big customer data analytics and what really challenges SMEs to exploit the value of big customer data. Besides, a demonstration platform is developed to present SMEs an automatic process of analysing customer online reviews and the capacity on customer insights acquisition and strategic decision making. Finally, findings from two focus groups indicate the different managerial and technical considerations required for SMEs considering implementing big data and customer analytics. This study encourages SMEs to welcome big customer data and suggests that a cloud-based approach may be the most appropriate way of giving access to big data analytics techniques.
The rise of ‘Big Data’ had a big impact on marketing research and practice. In this article, we first highlight sources of useful consumer information that are now available at large scale and very little or no cost. We subsequently discuss how this information – with the help of new analytical techniques – can be translated into valuable insights on consumers’ psychological states and traits that can, in turn, be used to inform marketing strategy. Finally, we discuss opportunities and challenges related to the use of Big Data as a window into consumers’ psychology, and provide recommendations for how to implement related technologies in a way that benefits both businesses and consumers.
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