scholarly journals Analysing customer behaviour in mobile app usage

2017 ◽  
Vol 117 (2) ◽  
pp. 425-438 ◽  
Author(s):  
Qianling Chen ◽  
Min Zhang ◽  
Xiande Zhao
Keyword(s):  
Big Data ◽  
Mobile Apps ◽  
Rule Learning ◽  
Mobile App ◽  
Content Type ◽  
Customer Behaviour ◽  
Monetary Analysis ◽  
Marketing Information ◽  
And Function

Purpose Big data produced by mobile apps contains valuable knowledge about customers and markets and have been viewed as productive resources. The purpose of this paper is to propose a multiple methods approach to elicit intelligence and value from big data by analysing the customer behaviour in mobile app usage. Design/methodology/approach The big data analytical approach is developed using three data mining techniques: RFM(recency, frequency, monetary) analysis, link analysis, and association rule learning. The authors then conduct a case study to apply this approach to analyse the transaction data extracted from a mobile app. Findings This approach can identify high value and mass customers, and understand their patterns and preferences in using the functions of the mobile app. Such knowledge enables the developer to capture the behaviour of large pools of customers and to improve products and services by mixing and matching the functions and offering personalised promotions and marketing information. Originality/value The approach used in this study balances complexity with usability, thus facilitating corporate use of big data in making product improvement and customisation decisions. The approach allows developers to gain insights into customer behaviour and function usage preferences by analysing big data. The identified associations between functions can also help developers improve existing, and design new, products and services to satisfy customers’ unfulfilled requirements.

Developing mobile services

2016 ◽  
Vol 28 (12) ◽  
pp. 2721-2747 ◽  
Author(s):  
Manuel Rivera ◽  
Robertico Croes ◽  
YunYing Zhong
Keyword(s):  
Product Development ◽  
Conjoint Analysis ◽  
Mobile Applications ◽  
Mobile Apps ◽  
Mobile App ◽  
Small Island ◽  
Content Type ◽  
Study Approach ◽  
First Time

Purpose This paper aims to examine and identify important attributes for mobile applications (apps) that might dictate tourist preferences for the apps on a small island destination. Guided by the Task Technology Fit (TTF) theory, the study considers the tasks performed, technology characteristics and individuals’ characteristics in determining the mobile apps attribute set. Design/methodology/approach This study uses a conjoint methodology within a case study approach framework. The conjoint analysis allows for assessing preferences from different consumers regarding the objective characteristics of products or services that facilitate the optimal design of product development. Optimal product development is a challenge for destinations, as they strive to achieve and sustain optimal market positions. Mobile apps may empower destinations in this endeavor. The case study approach imparts a context-dependent knowledge that facilitates a more nuanced understanding of consumer preference of use. Findings The results of the conjoint analysis suggest a strategic mapping of the most important attributes including type of content information, coupons and location awareness in defining apps product development. Within each attribute, the study also identifies the significant characteristics of a mobile application that are preferred by tourists. This ranking exists irrespective of familiarity with the destination (first-time and repeat visitors). Research limitations/implications The implication is that revealed preferences anchored in conjoint analysis provide a powerful approach to optimize product development in a small island destination. From a practical perspective, the findings suggest that the developments of a mobile app for a destination must concentrate on fostering spending and consider the app as a new marketing channel. From a theoretical point of view, the current study highlights the usefulness of using the conjoint analysis and the TTF theory as an overarching framework in mapping a multi-attribute decision-making space that influences tourist judgment and preference of use. The conjoint method applied in the study enables researchers to clearly identify a combination of various mobile app attributes that are most influential on tourists’ choice and preference of use. The guiding framework, TTF theory, allows the conjoint product designs to go beyond the technology characteristics to include tasks performed by tourists and their individual characteristics. Originality/value This study is the first to apply a conjoint analysis within the TTF theoretical framework in the context of a small island destination when assessing tourists’ use preferences toward mobile applications, while at the same time investigating whether any differences exist between first-time and repeat visitors. The study demonstrates that complementing the nature of the task (traveling) with context-specific interface and interactive features is an important area of inquiry that can benefit from adopting conjoint analysis.

Using UTAUT 2 model to predict mobile app based shopping: evidences from India

2017 ◽  
Vol 9 (3) ◽  
pp. 248-264 ◽  
Author(s):  
Preeti Tak ◽  
Savita Panwar
Keyword(s):  
Behavioral Intention ◽  
Mobile Apps ◽  
Smart Phone ◽  
Mobile App ◽  
Content Type ◽  
Use Of Technology ◽  
Usage Behavior ◽  
Managerial Implications ◽  
Facilitating Conditions ◽  
The Impact

Purpose The purpose of this paper is to understand antecedents of app-based shopping in an Indian context. The paper has used unified theory of acceptance and use of technology (UTAUT) 2 model for examining the impact of various constructs on behavioral intention and usage behavior of smart phone users toward the mobile shopping apps. Design/methodology/approach The constructs were tested and validated by means of a structured questionnaire which was administered on a sample of 350 mobile app shoppers in Delhi. AMOS 20 was used to analyze the collected data. Findings The study revealed that hedonic and habit are the strongest predictors of users’ behavioral intention to use mobile apps for shopping. Respondents are also influenced by the deals that are being offered by the marketers. The research also suggests that facilitating conditions help in usage of mobile apps for shopping. Research limitations/implications Managerial implications simplifying the interface which would encourage the less technologically advanced individuals to use mobile apps. Hedonic element of shopping through mobile apps should also be enhanced. Originality/value This study contributes to the research on intentions and usage behavior of consumer technologies by adopting UTAUT 2 model to explain the intentions and usage behavior toward mobile apps for shopping. The paper also measured the role of deals in influencing the consumers.

An automated mobile app labeling framework based on primary motivations for smartphone use

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anteneh Ayanso ◽  
Mingshan Han ◽  
Morteza Zihayat
Keyword(s):  
Classification Scheme ◽  
Mobile Apps ◽  
Mobile App ◽  
Machine Learning Algorithms ◽  
User Needs ◽  
Context Information ◽  
Web Based ◽  
Content Type ◽  
Classification Framework ◽  
Textual Data

Purpose This paper aims to propose an automated mobile app labeling framework based on a novel app classification scheme that is aligned with users’ primary motivations for using smartphones. The study addresses the gaps in incorporating the needs of users and other context information in app classification as well as recommendation systems. Design/methodology/approach Based on a corpus of mobile app descriptions collected from Google Play store, this study applies extensive text analytics and topic modeling procedures to profile mobile apps within the categories of the classification scheme. Sufficient number of representative and labeled app descriptions are then used to train a classifier using machine learning algorithms, such as rule-based, decision tree and artificial neural network. Findings Experimental results of the classifiers show high accuracy in automatically labeling new apps based on their descriptions. The accuracy of the classification results suggests a feasible direction in facilitating app searching and retrieval in different Web-based usage environments. Research limitations/implications As a common challenge in textual data projects, the problem of data size and data quality issues exists throughout the multiple phases of experiments. Future research will extend the data collection scope in many aspects to address the issues that constrained the current experiments. Practical implications These empirical experiments demonstrate the feasibility of textual data analysis in profiling apps and user context information. This study also benefits app developers by improving app descriptions through a better understanding of user needs and context information. Finally, the classification framework can also guide practitioners in customizing products and services beyond mobile apps where context information and user needs play an important role. Social implications Given the widespread usage and applications of smartphones today, the proposed app classification framework will have broader implications to different Web-based application environments. Originality/value While there have been other classification approaches in the literature, to the best of the authors’ knowledge, this framework is the first study on building an automated app labeling framework based on primary motivations of smartphone usage.

The effect of political neuromarketing 2.0 on election outcomes

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Islam Mohamed Hegazy
Keyword(s):  
Big Data ◽  
Political Campaign ◽  
Quality Of Democracy ◽  
Content Type ◽  
Interdisciplinary Field ◽  
The Usa ◽  
Negative Impacts ◽  
Field Of Inquiry

Purpose The purpose of this paper is the better understanding of the increasing relation between big data 2.0 and neuromarketing, particularly to influence election outcomes, along with a special aim to discuss some raised doubts about Trump’s presidential campaign 2016 and its ability to hijack American political consumers’ minds, and to direct their votes. Design/methodology/approach This paper combines deductive/inductive methodology to define the term of political neuromarketing 2.0 through a brief literature review of related concepts of big data 2.0, virtual identity and neuromarketing. It then applies a single qualitative case study by presenting the history and causes of online voter microtargeting in the USA, and analyzing the political neuromarketing 2.0 mechanisms adopted by Trump’s political campaign team in the 2016 presidential election. Findings Based on Trump’s political marketing mechanisms analysis, the paper believes that big data 2.0 and neuromarketing techniques played an unusual role in reading political consumers’ minds and helping the controversial candidate to meet one of the most unexpected victories in the presidential elections. Nevertheless, this paper argues that the ethics of using political neuromarketing 2.0 to sell candidates and its negative impacts on the quality of democracy are and will continue to be a subject of ongoing debates. Originality/value The marriage of big data 2.0 and political neuromarketing is a new interdisciplinary field of inquiry. This paper provides a useful introduction and further explanations for why and how Trump’s campaign defied initial loss predictions and attained victory during this election.

How the Internet of Things can help knowledge management: a case study from the automotive domain

2017 ◽  
Vol 21 (1) ◽  
pp. 57-70 ◽  
Author(s):  
Lorna Uden ◽  
Wu He
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Internet Of Things ◽  
Current Knowledge ◽  
Time Data ◽  
Content Type ◽  
Useful Knowledge ◽  
Iot Devices ◽  
The Internet Of Things

Purpose Current knowledge management (KM) systems cannot be used effectively for decision-making because of the lack of real-time data. This study aims to discuss how KM can benefit by embedding Internet of Things (IoT). Design/methodology/approach The paper discusses how IoT can help KM to capture data and convert data into knowledge to improve the parking service in transportation using a case study. Findings This case study related to intelligent parking service supported by IoT devices of vehicles shows that KM can play a role in turning the incoming big data collected from IoT devices into useful knowledge more quickly and effectively. Originality/value The literature review shows that there are few papers discussing how KM can benefit by embedding IoT and processing incoming big data collected from IoT devices. The case study developed in this study provides evidence to explain how IoT can help KM to capture big data and convert big data into knowledge to improve the parking service in transportation.

Big data and Internet of Things (IoT) technologies in Omani banks: a case study

foresight ◽  
2017 ◽  
Vol 19 (4) ◽  
pp. 409-420 ◽  
Author(s):  
Stuti Saxena ◽  
Tariq Ali Said Mansour Al-Tamimi
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Banking Sector ◽  
Big Data Analytics ◽  
It Infrastructure ◽  
Major Step ◽  
Content Type ◽  
External Data ◽  
Security Infrastructure

Purpose The purpose of this paper is to underline the significance of invoking Big Data and Internet of Things (IoT) technologies in Omani Banks. Opportunities and challenges are also being discussed in the case study. Design/methodology/approach Four Omani banks representative of local, international, Islamic and specialized banks are being studied in terms of their social networking presence on Facebook and their e-banking facilities. Also, impetus is laid upon the aggregation of internal data and vast amounts of semi-structured external data from public sources, including social media. Findings The case study shows that Big Data analytics and IoT technologies may be utilized by the Omani banks for facilitating them in “forecasting” and “nowcasting”. Besides, customers may be better managed with better and efficient services. However, there are challenges in tapping these technologies such as security, infrastructure, regulatory norms, etc. Practical implications Banks in Oman need to appreciate the utility of Big Data and IoT technologies, and for this, a robust IT infrastructure should be institutionalized. Originality/value The case study is a major step in integrating Big Data and IoT technologies in Omani banks across four variants of national, international, Islamic and specialized banks. This is the first study where such integration has been emphasized in the Omani banking sector.

The moderating role of customer engagement experiences in customer satisfaction–loyalty relationship

2019 ◽  
Vol 53 (7) ◽  
pp. 1278-1310 ◽  
Author(s):  
Rakhi Thakur
Keyword(s):  
Mobile Apps ◽  
Business Environment ◽  
Mobile App ◽  
Customer Engagement ◽  
Content Type ◽  
Digital Platforms ◽  
Quantitative Studies ◽  
Non Linear ◽  
Moderating Role

Purpose This study aims to examine the moderating role of customer engagement experiences in satisfaction–loyalty relationship in the digital business environment. This paper looks at mobile apps for shopping and travel planning to understand these relationships. Design/methodology/approach This paper includes the conceptualization and validation of the proposed relationship through multiple studies. An exploratory qualitative study was conducted to identify the relevant engagement experiences. Subsequently, multiple quantitative studies were conducted to examine the proposed relationships. Findings The effect of satisfaction on continuance intention is stronger among customers with higher levels of engagement. Further, the propensity to provide electronic word of mouth is non-linear in customers with higher levels of engagement and may not vary directly with satisfaction levels. Research limitations/implications The findings of this study contribute to the emerging literature on customer engagement and mobile app-usage domains. Future studies may examine such a relationship in different businesses and on varied digital platforms. Practical implications The findings of this paper may provide actionable insights to marketers, giving them a mechanism to segment customers based on engagement levels and using discretion while focusing on satisfaction levels among different segments. Originality/value This study validates the proposed moderating role of customer engagement in the satisfaction–loyalty relationship. The non-linear relationship between satisfaction and loyalty is also demonstrated.

Enabling integrated business planning through big data analytics: a case study on sales and operations planning

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alexander Schlegel ◽  
Hendrik Sebastian Birkel ◽  
Evi Hartmann
Keyword(s):  
Big Data ◽  
Information Processing ◽  
Data Analytics ◽  
Single Case ◽  
Big Data Analytics ◽  
Business Planning ◽  
Operations Planning ◽  
Content Type ◽  
The Impact

PurposeThe purpose of this study is to investigate how big data analytics capabilities (BDAC) enable the implementation of integrated business planning (IBP) – the advanced form of sales and operations planning (S&OP) – by counteracting the increasing information processing requirements.Design/methodology/approachThe research model is grounded in the organizational information processing theory (OIPT). An embedded single case study on a multinational agrochemical company with multiple geographically distinguished sub-units of analysis was conducted. Data were collected in workshops, semistructured interviews as well as direct observations and enriched by secondary data from internal company sources as well as publicly available sources.FindingsThe results show the relevancy of establishing BDAC within an organization to apply IBP by providing empirical evidence of BDA solutions in S&OP. The study highlights how BDAC increase an organization's information processing capacity and consequently enable efficient and effective S&OP. Practical guidance toward the development of tangible, human and intangible BDAC in a particular sequence is given.Originality/valueThis study is the first theoretically grounded, empirical investigation of S&OP implementation journeys under consideration of the impact of BDAC.

Explaining tourist behavioral loyalty toward mobile apps

2019 ◽  
Vol 10 (3) ◽  
pp. 415-430
Author(s):  
José-Alberto Castañeda ◽  
María-José Martínez-Heredia ◽  
Miguel-Ángel Rodríguez-Molina
Keyword(s):  
Design Methodology ◽  
Mobile Apps ◽  
Cost Benefit ◽  
Internet Access ◽  
Mobile App ◽  
Unified Theory ◽  
Content Type ◽  
Behavioral Loyalty ◽  
Use Of Technology ◽  
Continued Use

Purpose The overall tourist experience is changing because of the development of mobile devices, ubiquitous internet access and applications (apps) designed specifically for tourism. The aim of this study is to identify the determinants of continued use and recommendation of such apps (loyalty), framed within the unified theory of acceptance and use of technology (UTAUT2) model, considering innate user traits (innovativeness) and one of the principal attributes of mobile phones (portability). Design/methodology/approach The sample comprised 285 Spanish tourists who owned a smartphone and were using some kind of mobile app in connection with their stay. Findings The results show that the UTAUT2 model is effective in explaining loyalty toward tourism apps and that its variables mediate the effect of user and mobile device characteristics. Research limitations/implications To ensure continued use of a tourism app, it should possess the following core attributes: deliver a positive cost–benefit trade-off, be fun to use, provide up-to-date and useful information and generate a degree of dependency in the user. Originality/value The present research is particularly relevant because of its focus on the use of apps during the stay, given that most of the extant literature centers on previous stages (such as service booking).

Service mobile apps: a millennial generation perspective

2018 ◽  
Vol 118 (9) ◽  
pp. 1837-1860 ◽  
Author(s):  
Steven Leon
Keyword(s):  
Technology Acceptance ◽  
Self Efficacy ◽  
Millennial Generation ◽  
Information Quality ◽  
Mobile Apps ◽  
Mobile App ◽  
Ease Of Use ◽  
Perceived Ease Of Use ◽  
Model Framework ◽  
Content Type

Purpose This purpose of this paper is to evaluate Millennials’ intention to use service mobile apps and assess gender as a moderator. Design/methodology/approach An extended technology acceptance model framework that includes information quality and self-efficacy guides this research. PLS-SEM is used to evaluate the data and test the hypotheses. Findings The study reveals that information quality, self-efficacy, perceived ease of use and usefulness, and attitude influence Millennials’ intentions to use service mobile apps. Additionally, gender is found to partially moderate the results. Practical implications Service companies that rely on mobile apps to deliver services ought to consider the disparities among the Millennial generation, increasing the likelihood that Millennial customers will adopt service mobile apps and that they receive acceptable customer experiences. Originality/value This paper examines the factors influencing adoption and use of service mobile apps among Millennials and examines gender as a moderator. Additionally, guidelines for service mobile app design are included.

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