Influencing Factors And Interventions Of Positive And Negative Customer Engagement Behaviors In Online Brand Community

The competitive and dynamic business environment makes quality the key element of an organization’s success,and quality management has also become an important part of the organization’s operation process.Customer focus is the first of the seven basic principles of quality management,and customer relationship is also an important research direction in the field of quality management.The development of mobile Internet has provided convenience for customers to publish their experiences about products or services anytime and anywhere.The negative emotions,cognition and behaviors expressed by customers are also easier to spread.The damage caused by Negative Customer Engagement Behaviors(NCEBs)to the company’s brand value and reputation has been gradually expanded.Therefore,the rationality of NCEBs should receive the same attention as Positive Customer Engagement Behaviors(PCEBs).Based on this,this study reexamines the influencing factors,differences and connections,evolution and intervention mechanism of customer engagement(CE)from a positive and negative perspective.Based on the current research situation of CE,this paper mainly explores the following four aspects.First,mining the influencing factors of PCEBs and NCEBs in Online Brand Communities(OBCs).Combining existing research and the research topic of this article,Xiaomi Community was selected as the data source platform,and users comments of 11 Ultra circles were crawled using Python programming language.After preprocessing and emotional polarity classification,856 positive and 1,737 negative emotional polarity comments were obtained.Based on the Grounded Theory perspective,text data encoding was conducted for positive and negative emotional polarity reviews,and a theoretical saturation test was conducted to obtain the conceptual model of the factors influencing PCEBs and NCEBs in OBCs.Second,analysis of the influencing factors of PCEBs and NCEBs in OBCs.Based on Expectancy Dis-confirmation Theory,Critical Incidents Technology,Cognitive Appraisals and Coping Theory and Justice Theory,combined with the conceptual model constructed in the previous study and the content of the review text,in the form of manual coding,supplemented by machine learning,positive emotional polarity reviews are coded and classified into six dimensions:customer satisfaction,personal needs met,users brand relationship characteristics,cognition,emotion,and behavior,negative emotional polarity reviews are coded and classified into five dimensions:negative critical incidents,user brand relationship characteristics,cognition,emotion,and behavior.Among them,PCEBs include loyalty expression behavior,feedback behavior,sharing behavior,and providing improvement suggestions behavior.NCEBs include exit behavior,supporting competitors behavior and harming the company’s reputation behavior.A binary logistic regression was performed on the classified data to obtain the impact of various factors on PCEBs and NCEBs and their differences.Third,research on the difference,connection and transformation between the two type of CE.According to the results of research content 1 and 2,research content 3 explores the differences and connections between positive customer engagement(PCE)and negative customer engagement(NCE)in three dimensions of cognitive,affective,and behavioral from the perspective of influencing factors.At the same time,using the research context of OBCs in this article,this research explored the dynamics of PCE and NCE,as well as the differences and connections between the dynamics of the two types of CE.Based on the collation of the content of the review text in research content 1,this article discovered and explored the transformation mechanism of two types of CEBs,identified the key triggers for the transition from PCEBs to NCEBs and from NCEBs to PCEBs,laying a theoretical foundation for the study of research content 4.Fourth,research on the evolution and intervention of two types of CEBs.Based on the influencing factors,differences and connections,and transformation mechanisms of the two types of CEBs,the fourth part of the study constructs a mathematical model of the multi-stage evolution of CEBs,and carries out numerical simulation through Python program code.At the same time,the intervention effects of four key factors,namely price,product quality,service quality and personal needs met level on PCEBs and NCEBs were explored,and corresponding management enlightenment were obtained.The results show that:(1)Emotion and cognition are no longer the necessary factors to produce PCEBs,and users with different emotions and cognition also show differences in PCEBs.Users with pride and appreciate tend to show their loyalty to the company.Users who feel justice will experience joyful emotions and display feedback behavior."Fans",a user brand relationship characteristic only affects loyalty expression behavior.(2)Only negative critical incidents based on price can trigger all NCEBs,while negative critical incidents related to services can only promote the behavior of harming company’s reputation.Anger is no longer a necessary factor for all NCEBs,anger and disgust will lead to the behavior of harming company’s reputation,while customers with sadness will choose to exit.Distribution injustice will trigger customers’ support for competitors,and interaction injustice will not lead to customers’ exit and support for competitors’ behavior,while procedural injustice will not play a role in the generation of the three NCEBs."Fans" are more likely to turn to support competitors after being hurt,while "anti-fans" are more likely to make actions that harm company’s reputation.(3)When users encounter negative critical incidents related to price,they will inevitably experience NCEBs,but users who believe that price is reasonable will not exhibit PCEBs.Emotion and cognition are no longer the necessary trigger conditions for the generation of NCEBs and PCEBs.(4)Compared with users with PCEBs,users with NCEBs are more likely to have continuous and constantly changing emotional fluctuations."Anti-fans" users often do not show PCEBs,but users who call themselves "fans" will show NCEBs that is unfavorable to the company at a certain point in time.Loyal customers exhibit PCEBs even if the experience negative experience,or even generate negative cognition and emotion.For users who exhibit both PCEBs and NCEBs(at different time nodes),their changes in cognitive,affective and behavioral dimensions are consistent.(5)The key antecedents of PCEBs conversion to NCEBs and the key trigger antecedents of NCEBs exhibit differences.Negative critical incidents based on price can directly lead to the NCEBs,and when users with PCEBs experience a poor product and service experience,their behavior will also change.The key antecedents of NCEBs conversion to PCEBs are similar to the key trigger antecedents of the PCEBs.When users are satisfied with the service,or generate needs for helping others,solving problems and social,even if they have committed behaviors that harmed the company’s reputation,they ultimately demonstrate encouragement and support for the organization.(6)The total number of potential CE users and the total number of users in the two types of CEBs showed the same trend of change,and the trend of the increase in the number of two types of potential CE users is consistent.The increase in the number of potential NCE users is significantly higher than the increase in the number of potential PCE users.The number of users in NCE state is greater than the number of users in a PCE state.The number of PCE and NCE users shows the same trend in the third and fourth stages,but unlike the third stage,the number of two types of CE users is closer at the end of the fourth stage,and there is an alternating increase/decrease phenomenon.(7)Lower product pricing can generate relatively fewer NCE and more PCE users,while higher product pricing can significantly widen the number gap between the two types of engagement users,and the higher the price,the more difficult it is for NCE users to convert into PCE users,resulting in a growing gap between the two types of users.Reducing the initial product quality level will lead to more NCE users and fewer PCE users,while improving the initial product quality level will be the opposite.Reducing or improving the initial service quality level has no significant impact on the total number of NCE and PCE users at the end of the fourth stage,but the change in post service quality level can significantly affect the number of two types of CE users in the community.A reduction of the initial personal needs met level will lead to more NCEBs in a short time,while the improvement of the user’s initial and post personal needs met level will not have a significant impact on the number of users with two types of CE in a short time.However,over time,an improvement of initial personal needs met level can significantly promote the transformation of NCE users to PCE status.The above analysis results have certain management implications for enterprises:(1)Enterprises should carefully determine the initial launch price and promotion price of the products.Low cost performance ratio of products and the high gap between the initial launch and promotion prices are one of the key antecedents of NCEBs.(2)Enterprises should always put the improvement of product and service quality in the first place.Dissatisfaction with product quality will lead to NCEBs,while satisfaction with service quality can lead to PCEBs,and the improvement of service quality is the key to promote the transformation of customers from NCEBs to PCEBs.(3)The OBC provides a platform for company to engage with customers.Managers should pay attention to how to transmit and implement the company’s product and service concepts on the platform and their impact on the customer’s emotional state,so as to manage the CEBs through the community platform.(4)Company should focus on the use and operation of OBC,establish an effective mechanism for communicating with customers through the community platform,and provide a good environment for customers and non-customer groups to communicate with each other,so as to guide users to generate PCEBs.