Troubleshooting Customer Behaviour Against Merchants with Adaptive Multivariate Regression

Business intelligence can be said to be techniques and tools as acquisition, transforming raw data into meaningful and useful information for business analysis purposes. This study aims to build business intelligence in optimizing large-scale data based on e-metrics. E-metrics are data created from electronic-based customer behavior. As more and more large data sets become available, the challenge of analyzing data sets will get bigger and bigger. Therefore, business intelligence is currently facing new challenges, but also interesting opportunities, where can describe in real time the needs of the market share. Optimization is done using adaptive multivariate regression that can be address high-dimensional data and produce accurate predictions of response variables and produce continuous models in knots based on the smallest GCV value, where large and diverse data are simplified and then modeled based on the level of behavior similarity, basic measurements of distances, attributes, times, places, and transactions between social actors. Customer purchases will represent each preferred behaviour and a formula can be used to calculate the score for each customer using 7 input variables. Adaptive multivariate regression looks for customer behaviour so as to get the results of cutting the deviation which is the determining factor for performance on the data. The results show there are strategies and information needed for a sustainable business. Where merchants who sell fast food or food stalls are more in demand by customers.

Exploring the meteoric rise of online food ordering apps (OFOAs): the moderating role of visibility

PurposeOnline food ordering apps (OFOAs) have become increasingly popular, and consumers have widely recognized their benefits, particularly during pandemics or lockdowns. Despite the growing popularity of OFOAs, little is known about technology acceptance theories and their impact on purchase intention. The current study proposes the amalgamation of three theories to bridge this gap, better explaining customer behaviour toward OFOAs.Design/methodology/approachTo create a model, the researcher applied technology acceptance theories, visibility and purchase intention. To obtain results, the researcher used structural equation modelling (SEM) and moderation analysis on 432 responses obtained through a questionnaire.FindingsAccording to the research, all constructs were found to have a favourable and significant impact on the intention to use OFOAs, except compatibility. The moderating effect of visibility on intent to purchase is shown in the study. The study's findings add to the knowledge of consumer behaviour and practice.Research limitations/implicationsThis work sheds light on technology acceptance theories that ensure that OFOA platforms are used indefinitely. Furthermore, the significance of visibility as a mediator of purchase intention provides a deep insight into customer behaviour.Originality/valueThis model, which combines technology acceptance theories' original constructs with visibility to moderate purchase intention on OFOA platforms, is the first of its type in the current literature. Furthermore, this theoretical progress paves the way for future research.

The Effect of a Service Experience Cost on a Queueing System

In this work, the authors consider the effect of a service experience cost (SE cost) on customer behaviour in the M/M/1 queueing system. Based on customer individual equilibrium strategy, social welfare is also analyzed in unobservable and observable cases. The SE cost decreases the equilibrium joining probability and social welfare in an unobservable case. However, there might exist multiple individual equilibrium thresholds in an observable case. Furthermore, numerical results show that the SE cost can be used as a feasible policy to make an incentive for customers and regulate the system for improved social welfare in some scenarios.

Engaging consumers through firm-generated content on InstagramEngagement del consumidor a través del contenido generado por las empresas en instagram企业如何通过在instagram上发布内容来吸引消费者

Purpose The purpose of this paper is to analyse the impact of consumers’ perceptions of the enjoyment and originality of firm-generated content (FGC) posted on Instagram on affective customer engagement (CE). In addition, an examination is undertaken of affective CE as a driver of customer behaviour. Design/methodology/approach The paper takes a quantitative approach using a sample of 334 women followers of an eco-friendly restaurant Instagram account. After validation of the measurement scales, the hypotheses were tested through structural equation modelling. Drawing on the stimuli-organism-response framework the authors posit that consumers’ perceptions of the enjoyment and originality of Instagram posts generate affective CE, which, in turn, influences customer behaviour. Findings The results showed that the perceived enjoyment and perceived originality of Instagram posts generated by an eco-friendly restaurant have a positive influence on affective CE, which, in turn, affects consumers’ recommendation behaviours, intention to follow the restaurant’s advice on Instagram and intention to revisit the restaurant. Originality/value This research provides novel insights into how the perceived enjoyment and originality of FGC posted on Instagram increases women’s affective engagement and expands knowledge of how affective CE might increase positive electronic word-of-mouth, intention to follow the restaurant’s advice and repurchase intentions.