Importance-performance map analysis to enhance the performance of attitude towards mobile wallet adoption among Indian consumer segments
Authors: Chawla Deepak , Joshi Himanshu 
Published: September 2021

PurposeIndia has the second highest percentage of mobile wallet adoption driven by availability of affordable smartphones and Internet. Despite a general interest, studies on its adoption have been scarce. This research assumes that user segments exist, each with their own level of maturity, and addresses the question “Are there segments which can be profiled?” Thus, the objectives of the study are to propose a model that explains the attitude of user segments towards its adoption; identify probable user segments and profile them; examine the importance and performance of constructs which influence attitude within each cluster and recommend ways to improve performance.Design/methodology/approachThis paper employs the constructs from two popular theories on technology adoption, i.e. technology acceptance model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT). A synthesis of review of literature on these models, besides two focus group discussions (FGDs), was used to design a pilot instrument. A nationwide survey was conducted, and 744 responses were obtained. Convenience sampling was used to select the respondents. The average scores of various constructs were computed and subjected to hierarchical clustering. Further, k-means clustering was carried out. The demographic profiling of each cluster was done through cross-tabulation and differences related to attitude and intention between clusters were tracked by one-way Analysis of Variance (ANOVA). To determine the relative importance and performance of constructs within each cluster, Importance-Performance Map Analysis (IPMA) using Smart Partial Least Squares (PLS) was carried out.FindingsThe hierarchical clustering resulted in three clusters. The result of k-means clustering was used to label the clusters as Technology Enthusiasts (TE), Technology Sceptics (TS) and Technology Pragmatists (TP). The obtained clusters were found to differ in terms of perception, attitude, intention, behavior, marital status, education, occupation and income levels. With respect to each cluster, it was seen that the top three important constructs are Perceived Usefulness (PU), Security (SEC) and Lifestyle Compatibility (LC) as indicated by the IPMA. The findings indicate that mobile wallet providers should focus on all six constructs, with special focus on PU, SEC and LC. The findings of this study will help mobile wallet providers in customizing their offerings to enhance adoption attitude in all three clusters.Research limitations/implicationsThis study examines the perception of students and working professional towards mobile wallet adoption and uses this data for segmentation. However, there could be underlying differences between these two groups, as the motive behind adopting a technology may be different. Thus, treating them as homogenous user segments could be a limitation. Therefore, exploring segments and profiles for each type of user may be an area for future research. Mobile wallet providers should also give utmost importance to perceived usefulness, security and lifestyle compatibility while designing their services. This will not only enhance user trust and compatibility with mobile wallet but also improve the outcomes associated with its usage.Practical implicationsThis study will help mobile wallet providers understand the user segments and customize their service offerings.Originality/valueThis study provides a comparison of the respondent profiles of three obtained segments of mobile wallet users. While prior studies have identified segments associated with adoption of technologies like ATM banking, SMS banking, online banking, Internet banking, mobile banking etc., not much has been reported on mobile wallet adoption. To the best of the authors' knowledge, this is a novel study in India, aimed at identifying user clusters among adopters of mobile wallets and developing cluster profiles based on demographic, attitude and intention.