The Impact of Impulsive Consumption on Supply Chain in the Live-Streaming Economy

Abstract

Live e-commerce is an emerging business model in one form of which online influencers sell goods through live-streaming platforms. In this paper, we develop a live e-commerce supply chain pricing model with online influencers as retailers, with which we investigate the effects of consumers' impulsive consumption behavior, satisfaction. This paper analyzes the impact of consumers' impulse purchases on both manufacturers and retailers, and separately, on supply chains. We also assess consumers' purchase decisions when they watch online influencers promoting products via live-streaming. The results reveal that the impact of impulse spending on supply chain agents is non-monotonic. Rational pricing decisions and ensuring high-value products are the keys to profitability and impulse spending that lead to consumer satisfaction and can increase competition in the market. Therefore, supply chain decision makers should increase customers' satisfaction by offering products and enhancement following impulse consumption, rather than simply encouraging further impulse consumption behavior, which can often result in buyer's remorse and returned merchandize.

The Impact of Self-Efficacy and Perceived Value on Customer Engagement under Live Streaming Commerce Environment

“…Live streaming commerce refreshed the consumer's shopping context and promoted the consumer's shopping experience (Jiang and Cai, 2021) [8]. Conceptually, there are two typical definitions of live streaming commerce.…”

“…Scholars have conducted empirical research on the influencing factors of customer participation behavior in the e-commerce and social business environment from different perspectives, showing that customer participation is a combination of internal and external factors. Inner driving factors mainly refer to the internal characteristics of customers, including cognition [5,41], motivation [8,39], trust [39,42], values [43], emotion [5,41], self-efficacy [18], and flow experience [44]; external driving factors are mainly composed of technical features and environmental factors, including perceived control [44], perceived value [6,42], relational establishment [37], technical characteristics [45], social support and community identification [46], relational bonds [1], and social interaction [44]. It can be known from the literature that scholars have begun to pay attention to customer engagement behavior in the live streaming commerce environment, confirming the influence of relationship bonding and emotional commitment [1], consumption motivation [7], and trust [42] on customer participation.…”

“…e model path coefficient and research hypothesis test were carried out, and the results showed the following: (1) the path coefficient of perceived usefulness and perceived value was 0.507 (T � 7.991, P < 0.001), and the research hypothesis H1 passed the statistical test; (2) the path coefficient of perceived entertainment and perceived value is 0.225 (T � 4.440, P < 0.001), and the research hypothesis H2 passes the statistical test; (3) general self-efficacy has no significant effect on perceived value, assuming that H3 fails the statistical test; (4) the path coefficient of general selfefficacy and perceived usefulness is 0.853 (T � 7.115, P < 0.001), and the research assumes H4 passes statistical test; (5) the path coefficient of general self-efficacy and perceived entertainment is 0.785 (T � 7.289, P < 0.001), and the research hypothesis H5 passes the statistical test; (6) the path of general self-efficacy and live business self-efficacy coefficient is 0.785 (T � 7.032, P < 0.001), and the research assumes that H6 passes the statistical test; (7) the path coefficient of live broadcast business self-efficacy and perceived value is 0.210 (T � 3.922, P < 0.001), and the research assumes H7 passes statistical test; (8) the path coefficient of live broadcast business self-efficacy and customer participation is 0.380 (T � 6.817, P < 0.001), and the research hypothesis H8 passes the statistical test; (9) the path coefficient of perceived value and customer participation is 0.511 (T � 8.790, P < 0.001), and the research hypothesis H9 passes the statistical test. (See Figure 2 for the path diagram of the live business customer participation model based on VAM and self-efficacy and Table 4 for model path coefficients and tests).…”

Evolutionary Game of Multi-Subjects in Live Streaming and Governance Strategies Based on Social Preference Theory during the COVID-19 Pandemic

“…Their study introduced a new perception of measuring customer engagement in live-streaming digital marketing and called for further research into this new paradigm of social commerce to promote business and service provisions even with the restrictions of COVID-19. Jiang et al [12] developed a live e-commerce supply chain pricing model with online influencers as retailers by which they investigated the effects of consumers' impulsive consumption behavior and satisfaction. Their study analyzed the impact of consumers' impulse purchases on both manufacturers and retailers, and separately, on supply chains.…”

“…Under this evolutionary equilibrium strategy, the platform strategy is standardized operation, i.e., the platform considers that a good live streaming environment is good for social and economic development, so the platform does not hold egoistic preferences. In the case of δ 2 = 0, the results are shown in Figures 11-13 Figures [11][12][13] illustrate that the final evolutionary equilibrium strategy of the government and live streaming platform is (supervision, standardized operation). In this case, the government strengthens its own altruistic preference, and the platform correspondingly weakens its own altruistic preference.…”