Big Data and Use of Mobile Apps in Changing Consumers Behaviour - Literature review Example

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Big Data in Business

With the development of Information Technology, firms and organizations have gained access to the automated software-based data collection, management, and analysis (Klauser and Albrechtslund, 2014). A common term used to define this continuously evolving development is ‘Big Data’ (Klauser and Albrechtslund, 2014). Big Data integrates different functions of digital data management through code and allows to process huge amounts of different types of information in automated way (Klauser and Albrechtslund, 2014).

Big data is recognized to be an important strategic asset in the era of knowledge-based economy as it offers great opportunities to “interconnect different data sources situated on multiple geographical scales, and to process and analyse the hence generated data in increasingly automated ways” (Klauser and Albrechtslund, 2014:273; Hollands, 2008). Beyond improved approaches to information and data management, big data allows companies and organizations to get answers they could not answer before as well as to make more efficient, focused, and tailored business decisions (Schmarzo, 2013).

The Value of App Data

The data generated through mobile apps on smartphones plays especially an important analytical role and represents one of the valuable shares of Big Data (Buck et al., 2014). Data generated through mobile apps is integrated and generated from many different apps. Each app is designed to meet specific needs of its users and therefore, collects limited in scope but very detailed personal data and information (Buck et al., 2014). Buck et al. (2014) define four different tiers of data aggregation and personalization. The first tier includes the basic data required for gaining access to the system (e-mail, device ID, phone number, banking information, etc.). The second tier includes information on general usage data via Operating system such as primary interests, purchase history, etc. The third tier includes specific usage data gained via the 3rd-party app publisher, which includes household spending, banking accesses, etc. Finally, the fourth tier includes the aggregated data gained via 4th-Party aggregator, which includes such details as householder spending tracked by time, location, shops, etc. (Buck et al., 2014).

Most of the mobile apps that are used nowadays work by the same principle. Stand-alone applications record important user’s data and communicate this data with other apps, aiming thus to solve a specific user’s problem/task each time (Paschou, 2013; Guesmi, 2014). Retrieved data/information is normally recorded and stored in the user’s cloud based private storage. These records are kept on the smartphone devices that are secured by a personal password and can be sent to the consulting specialists (physician/nutriologist for health-related cases) for future recommendations/actions to be taken (Paschou, 2013; Thrift and French, 2002).

Most of the self-monitoring apps collect several key aspects of user's personal health data and consumption behaviour (Lupton, 2014). The data retrieved and aggregated from the mobile apps includes not only insights into consumers digital habits, behaviours and lifestyles of consumers but also information about their real lives, such as physical data, location data, purchase/consumption data, interests, needs, etc. (Buck et al., 2014). Apps enable its developers to combine, integrate and aggregate different pieces of data and use it for developing more effective solutions (Buck et al., 2014). Below is presented the visual representation of the process of apps data aggregation.

Figure 1: App Data as a Personalized Part of Big Data (Source: Buck et al., 2014: 27).

Thus, by aggregating the user’s data from different apps, companies are able to develop a highly personalised approach. Due to the nature of mobile apps, companies-owners or developers of the apps have access to a huge pool of valuable and updated data and highly personal information about apps’ users. Mobile apps allow its developers to retrieve this information automatically without significant manual efforts (Buck et al., 2014).

There exist many different opportunities that allow using effectively this data for different purposes. Thus, for example, user’s personal data can be used in order to predict traffic jams or the outbreaks of flu (Buck et al., 2014). For instance, Google collects and analyses local data on flu search and generates the forecasts for a specific geography. This simple example illustrates that app data can be used for many different purposes, including such as purposes as change of consumption patterns/habits and healthcare information management (Lupton, 2014).

Apps and habit formation

Development of mobile apps that support users’ change of behaviour has become an important issue within the Human-Computer Interaction (HCI) research (Klasnja, et al., 2011; Stawarz et al., 2015). Consumers are increasingly concerned about their health and appearance. They use technology in order to improve their well-being and to take greater control over their lives. Mobile apps are widely used by individual users who want to track themselves and change their habits (Klauser and Albrechtslund, 2014). Numerous studies together with the popularity of mobile apps among users reinforce the importance and usefulness of apps for the process of adapting and developing new behaviour and habits (Direito et al., 2015). Some of the most popular apps used for habit/changing behaviour purposes are based on self-tracking, self-monitoring and self-governing functions. The range of potential applications of mobile apps in this area are extensive: from diet/sport tracking to supporting medication-taking habits (Stawarz et al., 2015). In fact, health and medical apps have gained particular interest and popularity among mobile users as digital technologies offer them great opportunities to take control over their health and consumption behaviour “via contributing to and harnessing online information and engaging in self-monitoring and self-care practices” (Lupton, 2014: 608).

Stawarz et al. (2015) have carried out a study aiming to analyse how using of smartphone apps could be applied for changing consumer habits and supporting new habit formations. The study was based on a 4-week long experiment, whereas researchers reviewed the functionality of 115 habit formation apps (Stawarz et al., 2015). While the study has shown that mobile apps were useful instruments for changing consumers’ behaviour, the researchers concluded that the sole use of self-tracking apps might not be sufficient enough to change the individuals’ behaviour as they lack of habit support (Cowan et al., 2013). In fact, many self-tracking apps fail to use behaviour change techniques as they primarily focus on motivation support and lack such important features as implementation intentions and support for trigger events (Stawarz et al., 2015).

Mobile apps and health system

Wireless technologies, body sensor devices, fitness trackers, and other useful innovations have significant impact not only on the users’ quality of life but also on the national healthcare systems (El-Amrawy & Nounou, 2015). The mobile apps synchronised with the body sensors and other smart trackers generate large amount of useful data that can be used in healthcare systems (El-Amrawy & Nounou, 2015; Yilmaz, 2010; Jeon and Park, 2015). While most of the modern apps offer tracking of fitness activities, calorie consumption and calorie burning rate, pulse, heart rate, weight, sleep, calculation of disease risks, etc. they also might be used for other health-related issues (Paschou, 2013).

At the national healthcare system level, mobile technologies can be used as cost-effective and efficient means for communicating health-behaviour risks such as alcohol consumption, smoking, sugar consumption, excessive coffee drinking, etc. and for accelerating the behavioural change (Cohn, 2013). Thus, for example, Cohn et al. (2013) carried out an empirical study aiming to assess the efficiency of available apps that aimed to change users’ consumption patterns of alcohol use. These apps offered a service of tracking alcohol consumption and providing advices on alcohol treatment. While the authors believe that more detailed and extensive research is required in order to examine the efficacy of mobile technology in alcohol consumption intervention and management, it is possible to claim that mobile apps are tools with extremely high potential. Taking into consideration the evidence based on preliminary literature review it is possible to suggest that coffee consumption also can be regulated, influenced and managed with a help of mobile apps technology and effective aggregation of big data. By integrating user’s health data based on key individual’s variables such as age, weight, blood pressure, etc. with recorded data of coffee consumption, there can be developed effective ways of changing consumer’s habits of coffee consumption.

Privacy issues

While the idea of using personal data and information of consumers is promising and offering opportunities to change consumer’s patterns of behavior such as coffee consumption, there is a critical limitation of the data usage associated with privacy issues (Albrechtslund and Lauritsem 2013). As Lupton (2014) claims, app developers are not allowed to disclose personal data of users to a third party without having confirmed this decision with every individual user. Moreover, app developers are not permitted to sell user’s data to third parties unless they are providing a health service to the users (Lupton, 2014). In relation to the medical and health apps there arise even more ethical, regulatory and legal issues, which also might pose some challenges for app developers (Ozdalga et al., 2012; Wallace and Dhingra, 2014; Fuchs et al., 2011; McCartney, M. 2013; Visvanathan et al., 2012).

Theoretical Approach

There has been an increase in the usage of mobile applications (apps), and this has led to vital change in the manner consumers are using technology in regard to how they associate with companies and brands. Mobile devices are characterised as cultural objects that consumers readily use for the purposes of transactions, socialising, information search and management of daily schedules (Shanker et al., 2010). The manner in which mobile apps are made, is to influence customers’ experience in an easier and intuitive manner that in turn influences behaviour. The theoretical framework that can be used to explain big data and use of mobile apps in changing consumers’ behaviour is the theory of planned behaviour and consumer decision making model.

The theory of planned behaviour hypothesises that, for a specific behaviour to take place, the intention to perform that behaviour should exist. The main importance of attention is to glimpse the motivating factors that influence a behaviour (Ajzen, 1991). The theory identified three components that when blended they form intention, and they include attitude towards behaviour, the perceived behavioural control and the subjective norm.

Figure 2: Theory of planned behaviour (Azjen, 2005 p.118)

This theory can be used to explain and assess user’s use of mobile app and the manner in which it influence the behaviour of that user. The other theory is the consumer decision model that can be linked to the mobile app, and how the consumers use the app to make a decision that to purchase a good or service. The two theoretical models can be used to explain how big data and mobile apps can be used by companies to influence consumers’ behaviour. They are very vital models in understanding the aspect that big data or mobile app can use to influence consumer’s behaviour.

Mobile apps data and consumer behavior

Most of the existing literature on mobile usage for marketing reasons focuses on consumer behavior to try and comprehend why people will consider using an app for the purpose of purchasing decisions. Wozniak (2013) indicated that mobile apps that are used for the purpose of marketing can be designed or customized such that they affect the different stages of consumer decision-making process, and this include need recognition, search for information, seeking for alternatives, to make a purchase decision, and the post-purchase behavior. Consumers have found that usage of apps is very vital in their shopping process, and they have influenced their purchase decision (Wozniak, 2013). Other studies have indicated that mobile apps are very crucial since they are attractive and they give marketers an opportunity to present specific information that is relevant to consumers based on demographics, social trends, communication and geographical patterns (Friedrich et al., 2009).

Kalnikaite, Bird and Rogers (2013), in their study on consumers using mobile apps on grocery shopping, found that consumer are very burdened in using mobile apps when they are purchasing basic commodities. However, the study also found that high end purchases are very concerned with the star rating, and they are willing to take time and invest in products that have a good rating. It was also found in other study that select personality and high usage of technology in relation to app usage lead to greater purchases (Kumar and Mukherjee, 2013). Taylor and Levin (2015), also found that there is a strong correlation between the retailer offering a mobile app and the intent to purchase, and that customers that have used to apps successfully are likely to influence others to use the app. Consumer behavior and the mobile technology use are much associated and marketers should develop apps that are more captivating to consumer such that they can influence consumer behavior.

Big data for customer- based marketing and customer behaviour

The enormous and quick growth of technology have prompted a substantial increase in the memory speed and the manner that information is processed (Ervasti, 2013). Additionally, there is cheap storage that enable users to store and collect data in an astonishing manner. Using big data can be very helpful is ascertain the trend of consumer behaviour. Some study indicate that quantitative can be used to find valuable patterns in data, and this data can be very helpful to company in predicting the behaviour of customers (Siegel and Davenporrt, 2013). Big data as well as analytics are progressively being accumulated by companies, however there are innovative ways that have been deployed to enhance businesses and their consumers’ experience (Nair et al., 2013). Big data is gathered by companies from different sources, and they analyse this data for the purposes of marketing performance. There are predictive tools that are incorporated in the big data and analytics are very useful in enhancing the consumer experience. For instance, it was found that marketing response rate cane be increased by using predictive analytics and this was fundamental in targeting high value customers (IBM, 2013). Using of big data is very vital in analyses and to position a company to devise ways to use the data analysed to influence consumer behaviour and marketing decisions. Cisco (2013) indicate that in 2012 mobile data traffic was 12 times the size of the entire global internet in the year 2000. Most of the IT practitioners have indicated that the main role of Big Data is to enhance customer centricity and operational optimisation (eMarketer, 2012a). When the business is consumer centred they are likely to influence consumer experience that will in turn influence consumer behaviour.

The consumer decision-making process

In order to evaluate how mobile apps can influence the changes in consumer behaviour, it is prudent to explore the concept of consumer behaviour. This research is important in understanding how the mobile app data and the mobile environment influences the decision of a consumer in favour of an organisation offering the app. This helps understand the consumer decision making process as exemplified in Kotler and Armstrong (2012).

Figure 3: Model of Consumer Behavior - Kotler and Armstrong, pg.152, 2012

The process of consumer decision making begins with the identification of the need, which may include interest in buying a new car, need for clothing or hunger (Ervasti, 2013). Some of the most popular areas of needs in terms of mobile apps include the health and wellness needs, which then prompts an individual to search for information specifically on the wellness and health apps or any other area of need. In this case, the individual “…may store the need in memory or undertake an information search related to the need.” (Kotler and Armstrong, 2012, p.153)

The third stage is the evaluation of alternatives, when the individuals undertakes to “…use careful calculations and logical thinking. At other times, the same consumers do little or no evaluating; instead they buy on impulse and rely on intuition.”(Kotler and Armstrong, 2012, p.153). After careful evaluation of the alternatives, the last stage of the decision-making process involves making the final decision.

Understanding the stages of consumer decision making process is prudent in the study of how mobile apps influence consumer behaviour as it can help mobile app developers and marketers using the mobile app data to understand the mobile app properties that are essential in supporting the decision making process. According to a study conducted by Teo and Yeoung (2003) conducted to investigate the external information search during the decision making process had an effect on the overall decision, the results of the study indicated that there was a “…significant positive relationship between consumers’ overall evaluation of the deal and their willingness to purchase.”(Teo & Yeoung, 2003, p.360).

Influence of mobile apps visual design

For an app to have an effective influence over consumer behaviour, there should be a considerable aspect of usability and the consumer must find the app user friendly for purposes of enhancing consumption propensity (Wendel, 2013). In a study that investigated the difference in the consumption activity between free and paid cholesterol tracking mobile apps, it was found that language was a major influencer of the use of a specific apps regardless of the price. The investigation involved 34 apps, 15 free and 19 paid mobile apps ranked based on the Nielsen-Schneiderman Heuristic framework. The report indicated that both the free and paid apps ranked the same across the categories, but for the price, where consumers seemed to favour the paid mobile apps better due to their stronger score in assistance and clarity of instructions. Amberson, (2014, p.33) notes that

“If an app is harder to use, the consumer can put information in wrong sections, inaccurately track their cholesterol results, or even be misled about their medication. These mistakes can severely decrease the consumer’s health status.” It is therefore prudent to note that the design and visual elements of the app are important to mobile app developers as they enable them to develop the app in a way that it can positively influence the consumer behaviour through the decision making process.

Mobile apps should also possess the desired aesthetics to attract the consumers and achieve brand loyalty (Wang et al., 2013). This can be illustrated by the results of a study conducted on 60 Canadian mobile users. The study sought to investigate the link between loyalty and design aesthetics in the mobile space. The study found that a strong link between technology adoption and elements such as usefulness, enjoyment and ease of use (Cyr, Head, & Ivanov, 2006, p.957). This study is further supported by the results conducted by Adhami (2013) on neuromarketing. The study examined mobile app data from 25 iPhone users and how they used their mobile apps in searching and making purchases. Using the electroencephalography headsets to monitor their brain activity, the results indicated that “…participants were more responsive to images than text, and were often emotionally and attentionally engaged when presented with high fidelity imagery” (p.98).

Behaviour change

The field of consumer behaviour change is an area of growing research when it comes to application of mobile app data in business and how mobile app developers can use the information to design more productive apps to suit their objectives (Taylor and Levin, 2014). The use of mobile app data is specifically very popular in the areas of health and wellness as apps in these areas are widely used to provide solutions to emerging health and wellness issues. It is worth noting that this area is in need of further exploration as there is a gap in the inclusion of behavioural theory in development of health apps as well as social support. The design of the health and wellness apps should incorporate both functionality and fine-tune design to promote ease of usability, enjoyment and usefulness (Pagoto and Bennett, 2013). Mobile app data from the analysis of 127 health apps in the iTunes Health & Fitness category indicates that most of the apps under investigation did not integrate the health-behavior change theories into the development of the apps, although such an incorporation could be effective in enhancing a change in consumer behavior (Cowan et al., 2012). In this case, it would be prudent to note that the changes in health behavior of the consumers requires inclusion of essential theoretical constructs, which when implemented during app development, can lead to more functionality of the app. Some of these important elements include social support (Shareef et al., 2014).

This analysis shows that select app features of the health and wellness apps, as well as other types of apps used in business can help support positive behaviour change in the users of the apps (Verkasalo et al., 2010). However, there should be sufficient mobile app data to ensure that the app development meets the needs of the users and the app is capable of driving the consumers through all the stages of the decision making process (Stawarz et al., 2015). As such, more attention needs to be given to the analysis of the app content. As the advancement of technology is increasingly changing the society, so is mobile app data useful in business and marketers as it is in the heart of mobile consumer behaviour (Sultan et al., 2009).

Key conclusions based on the literature review

Based on the above presented literature review it is possible to outline several major conclusions.

• Importance of big data and mobile apps

Mobile app is an important and absolute leader in generating and aggregating Big Data. By utilising very specific, detailed, reliable and representative personal information/data of users, businesses, non-government organizations and government structures may use it for numerous purposes, underpinned by either commercial or social initiatives. Increased popularity of self-monitoring and self-tracking apps allows collecting highly personalized data and using it for addressing various health issues.

• Raising individual awareness

More specifically, mobile apps enabling tracking consumers’ consumption of coffee might help to raise the individuals’ awareness and to stimulate them for adopting a more balanced coffee consumption approach. Apps enable users to track the cups of coffee drunk, to calculate the calories consumed with it and the amount of caffeine consumed per day. Moreover, apps might provide consulting service to coffee drinkers, setting thus for them recommended norms of daily coffee consumption based on their individuals characteristics, lifestyle, etc.

• Changing consumer habits of coffee consumption

The mobile apps and effective management of individuals’ information might be very helpful in changing consumer habits of coffee consumption. Increased awareness followed by various stimulating and encouraging activities might be very helpful for developing new consumer habits (for instance, replacing coffee by other healthier alternatives: green tea, water, vegetable juice, etc.). However, as research shows, the sole use of self-tracking apps might not be sufficient to change the individuals’ behavior. Therefore, it might be important to add such features as implementation intentions and support for trigger events (Stawarz et al., 2015).

• Big Data use at national level

Big Data aggregated from mobile apps can be used at the national level and can be very helpful for the national healthcare regulators such as NHS for addressing the problem of coffee consumption among the UK population. The government might use Big Data to research overall patterns of consumptions by gender, household size, age, location, etc. Based on this information, there could be developed various campaigns and promotions targeting specific needs of different consumers.