The impact of mobile app adoption on physical and online channels

Lim, Boram; Xie, Ying; Haruvy, Ernan

doi:10.1016/j.jretai.2021.10.001

Cited by 31 publications

(15 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mobile apps introduce people to online shopping. They can purchase clothes, book hotels and tickets, order food and grocery and do other necessary things (Lim et al ., 2021). Customers find mobile apps comfortable as it saves time and energy and they can access online shopping from anywhere at any time.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigation and prediction of users' sentiment toward food delivery apps applying machine learning approaches

Hossain

Begum

Rouf

et al. 2023

JCMARS

View full text Add to dashboard Cite

PurposeThe goal of the current research is to use different machine learning (ML) approaches to examine and predict customer reviews of food delivery apps (FDAs).Design/methodology/approachUsing Google Play Scraper, data from five food delivery service providers were collected from the Google Play store. Following cleaning the reviews, the filtered texts were classified as having negative, positive, or neutral sentiments, which were then scored using two unsupervised sentiment algorithms (AFINN and Valence Aware Dictionary for sentiment Reasoning (VADER)). Furthermore, the authors employed four ML approaches to categorize each review of FDAs into the respective sentiment class.FindingsAccording to the study's findings, the majority of customer reviews of FDAs were positive. This research also revealed that, while all of the methods (decision tree, linear support vector machine, random forest classifier and logistic regression) can appropriately classify the reviews into a sentiment category, support vector machines (SVM) beats the others in terms of model accuracy. The authors' study also showed that logistic regression provided the highest recall, F1 score and lowest Root Mean Square Error (RMSE) among the four ML models.Practical implicationsThe findings aid FDAs in determining customer review behavior. The study's findings could help food apps developers better understand how customers feel about the developers' products and services. The food apps developer can learn how to use ML techniques to better understand the users' behavior.Originality/valueThe current study uses ML methodologies to investigate and predict consumer attitude regarding FDAs.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Investigation and prediction of users' sentiment toward food delivery apps applying machine learning approaches

Hossain

Begum

Rouf

et al. 2023

JCMARS

View full text Add to dashboard Cite

show abstract

“…Beside customer age, socio-demographic factors identified in the channel choice literature include gender, income and education level, country and residence, household size and occupation. A customer’s country and residence influences channel choice in several ways, including the potential effect of cultural dimensions on customer needs and consequently on channel selection (Ali et al 2021 ; Hofstede et al 2010 ; Lu et al 2018a ; Park and Kim 2018 ), physical distance from offline channels (Lim et al 2021 ; Sousa et al 2015 ; Soysal and Krishnamurthi 2016 ), and availability or otherwise of channels due to infrastructural conditions such as internet coverage or broadband connectivity (Fornari et al 2016 ).…”

Section: Analysis and Findingsmentioning

confidence: 99%

“…Numerous studies highlight both positive (cross-channel synergies; see, for example, Fornari et al 2016 ; Kumar et al 2019 ; Verhagen and van Dolen 2009 ; Yang et al 2014 ) and negative (cross-channel competition or cannibalization; e.g. Bilgicer et al 2015 ; Li et al 2017 ; Lim et al 2021 ) cross-channel effects. A holistic understanding of these effects along the customer journey could be of significant benefit to channel management (Bilgicer et al 2015 ; Dorie and Loranger 2020 ; Trenz et al 2020 ; Yang et al 2014 ).…”

Section: Suggestions For Future Researchmentioning

confidence: 99%

Factors of customers’ channel choice in an omnichannel environment: a systematic literature review

Wolf

Steul-Fischer

2022

Manag Rev Q

View full text Add to dashboard Cite

The proliferation of mobile devices and the continuous development of online technologies has led to an increasing variety of channels, leaving customers with a choice of channel alongside the choice of product, service, or retailer. Any attempt to optimize customer experience and engage in successful omnichannel management will require a complete, multifaceted understanding of the processes around channel choice of customers. To date, the many existing studies around multi- and omnichannel research have failed to yield an integrated, comprehensive synthesis of factors involved in customers´ channel choice. Our study conducted a systematic literature review to the end of identifying the factors involved in channel choice which appear in the scientific literature on this topic over the last two decades. We retrieved 128 papers from three bibliographic databases (EBSCO Host, Scopus, and Web of Science) and carried out descriptive analysis on them. Qualitative thematic analysis inductively extracted 66 different factors of channel choice, each assignable to five broader categories, from the studies included in the review. The findings indicate that perceived channel characteristics, customer needs and situational or contextual factors influence customers´ channel choice directly, and customer characteristics and characteristics of products or services influence it indirectly. Alongside its presentation of an integrated conceptual framework comprising these relationships, our study details a comprehensive research agenda with regards to theories, contexts, and methods and, in particular, with regards to factors influencing customers´ channel choice. Our findings advance the academic understanding of channel choice behavior and provide researchers and practitioners in this area with information on important implications for omnichannel management.

show abstract

“…Of the eight studies discussing the impact of the diffusion of digital technologies on grocery retail brick-and-mortar patronage, three studies discuss how using mobile apps affects brick-and-mortar shopping behaviour and retail patronage (Bies et al. , 2021; Lim et al. , 2022; Klabjan and Pei, 2011), two studies discuss how the introduction of online shopping affects retail patronage in online and offline shopping (Bauerová, 2019; Melis et al.…”

Section: Introductionmentioning

confidence: 99%

“…A Scopus search of the latest research trends on the factors shaping patronage of bricks-and-mortar grocery retailing under the context of widespread online shopping revealed that several empirical studies had been published in leading journals (See Appendix for details of the systematic review we conducted). Of the eight studies discussing the impact of the diffusion of digital technologies on grocery retail brick-and-mortar patronage, three studies discuss how using mobile apps affects brick-and-mortar shopping behaviour and retail patronage (Bies et al, 2021;Lim et al, 2022;Klabjan and Pei, 2011), two studies discuss how the introduction of online shopping affects retail patronage in online and offline shopping (Bauerov a, 2019;Melis et al, 2016); two studies examine the impact of in-store digital signage on retail patronage (Garaus et al, 2017;Garaus and Wagner, 2019) and one study discusses the formation of retail patronage in a click-and-collect (Vyt et al, 2022). Whilst existing studies have initiated new horizons in the retail literature, online grocery shopping has only been around for a short time, leaving many areas unexplored.…”

Section: Introductionmentioning

confidence: 99%

The impact of e-retail usage on relative retail patronage formation

Yokoyama

Azuma

Kim

2023

IJRDM

View full text Add to dashboard Cite

PurposeDespite retail digitisation and research efforts focussed on online and omnichannel shopping, there is insufficient knowledge regarding retail patronage formation in the grocery category, where in-store sales dominate. This study analyses the retail patronage formation in grocery in-store fill-in shopping.Design/methodology/approachThe authors designed a questionnaire to measure retail patronage behaviour, consumer satisfaction (CS), store attributes evaluation and e-retail usage. Then, the authors analysed the path structure for retail patronage behaviour formation using structural equation modelling. Additionally, they performed a mediation analysis using the bootstrap method and a moderation analysis based on a chi-square difference test.FindingsThis study provides three main findings. First, the authors' model has two ways to increase Share-of-Wallet (SOW). One is to increase Share-of-Visits (SOV) and another is to increase CS amongst non-users of e-retailing. Second, the results of the moderation analysis suggest the influence of customers' use or non-use of e-retailing on SOW formation. Third, service evaluation plays an interesting role in the overall model: the lower the assessment of service, the higher the SOV; the higher the evaluation of service, the greater the CS; the greater the CS, the higher the SOV.Originality/valueThe authors proposed the framework for the relative retail patronage formation in grocery fill-in shopping to examine the relationship between two relative patronage indicators (SOW and SOV) in the path structure and the mediating effect of CS and the moderating effect of e-retailing usage on retail patronage formation.

show abstract

The impact of mobile app adoption on physical and online channels

Cited by 31 publications

References 50 publications

Investigation and prediction of users' sentiment toward food delivery apps applying machine learning approaches

Investigation and prediction of users' sentiment toward food delivery apps applying machine learning approaches

Factors of customers’ channel choice in an omnichannel environment: a systematic literature review

The impact of e-retail usage on relative retail patronage formation

Contact Info

Product

Resources

About