A Decision Analysis Model for the Brand Experience of Branded Apps Using Consistency Fuzzy Linguistic Preference Relations

Abstract

Branded apps are not only an important platform for enterprises and customers to have real-time interactions and communicate marketing messages, but also a new business model that encourages value co-creation between the two. In order to explore the impact of branded apps on customers, this study constructs a fuzzy multi-criteria decision making (FMCDM) analysis model, and it uses consistent fuzzy linguistic preference relations (CFLPR) to set up a symmetric pairwise comparison matrix, which greatly reduces the complexity and error rate of calculations. Empirical research findings show that brand experience attributes and the influence of brand experience on customer loyalty and satisfaction can be more accurately measured. As a consequence of this study, we show that, among the brand experience facets of two retail chain branded apps, behavioral experience is the most favored, while affective experience is the least favored. Furthermore, brand attachment and active participation should be strengthened to enhance customer loyalty. Through the analytical model employed in this study, enterprises can regularly monitor changes in the brand experience preferences of branded app users and evaluate app performance to flexibly adjust mobile device-based marketing campaigns and strategies. It can also aid enterprises in using mobile devices effectively to improve customer loyalty and address the issue of diminishing brand loyalty.

Developing Universally Applicable Service Quality Assessment Model Based on the Theory of Consumption Values, and Using Fuzzy Linguistic Preference Relations to Empirically Test Three Industries

“…This method was, thus, shown to be more efficient and correct than other FMCDM methods, which also requires consistency testing. Because Fuzzy LinPreRa can avoid the problem of inconsistent evaluation results [51,118], and because it uses triangular fuzzy numbers, so it can completely retain the uncertainty information [111]. Therefore, its research results are easier to have higher reliability and validity than AHP, and easier to have higher reliability than FAHP.…”

“…Therefore, its research results are easier to have higher reliability and validity than AHP, and easier to have higher reliability than FAHP. The pairwise comparison matrix of Fuzzy LinPreRa only needs to be compared n − 1 times [51,118], and no fine-tuning step is required after analysis [118], which is more efficient than AHP and FAHP. Because of these characteristics and advantages, this study uses Fuzzy LinPreRa in the CV-SQ framework to verify the most important key factors for the service quality of the three industries, which confirmed its scientific results.…”

“…The Fuzzy LinPreRa method is an improved simplified practical method derived from the fuzzy analytical hierarchy process (FAHP). Hsu, Chen [51] (p. 4) pointed out that "In FAHP, for a pairwise comparison matrix that includes n alternatives, as many as n(n − 1)/2 comparisons are requested. Keeping aside the complexity of the entire process, the evaluation results produced by different experts could also be inconsistent.…”

“…Compared with the FAHP, Fuzzy LinPreRa is simpler, for a pairwise comparison matrix that includes n alternatives only needs to be compared n − 1 times, and can avoid the problem of inconsistent evaluation outcomes". Triangular fuzzy numbers are most widely used among commonly seen linguistic variables [51,52]. The crisp analytical hierarchy process (AHP) cannot reflect the thinking style in capturing the expert's knowledge; the triangular fuzzy importance scale can cover the uncertainty of assigning crisp concepts in related decision-making topics [53].…”