Exploratory study on top 100 global brands’ mobile marketing effort through apps and mobile websites

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Exploratory study on top 100 global brands’ mobile marketing effort

through apps and mobile websites

Dominik Bohuslav (11373253) 23.6.2017 (Final)

MSc Business Administration – Digital Business Track University of Amsterdam

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List of tables

Table 1: Summary table of mobile website results section ... 28

Table 2: Mean and standard deviation of Overall Website Quality based on industry ... 30

Table 3: Mean and standard deviation of Functionality based on industry ... 31

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Table 5: Mean and standard deviation of Aesthetics based on industry ... 32

Table 6: Mean and standard deviation of Information based on industry ... 32

Table 7: Mean and standard deviation of Overall Website Quality based on orientation ... 33

Table 8: Mean and standard deviation of Engagement based on orientation ... 34

Table 9: Mean and standard deviation of Overall Website Quality based on revenue ... 34

Table 10: Mean and standard deviation of Overall Website Quality based on region... 35

Table 11: Mean and standard deviation of Engagement based on region ... 36

Table 12: Mean and standard deviation of Overall Website Quality based on rankings ... 36

Table 13: Correlations between factors with p-value ... 37

Table 14: Summary table of mobile website results section ... 38

Table 15: Mean and standard deviation of Overall App Quality based on industry... 39

Table 16: Mean and standard deviation of Overall App Quality based on orientation ... 39

Table 17: Mean and standard deviation of Aesthetics based on orientation ... 40

Table 18: Mean and standard deviation of Overall App Quality based on revenue ... 40

Table 19: Mean and standard deviation of Overall Website Quality based on region... 41

Table 20: Mean and standard deviation of Information based on region ... 42

Table 21: Mean and standard deviation of Overall App Quality based on rankings ... 42

Table 22: Correlation between factors with p-value ... 43

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List of figures

Figure 1: Number of companies based on Industries ... 29

Figure 2: Number of companies based on Orientation ... 33

Figure 3: Number of companies based on Revenue ... 35

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Statement of originality

This document is written by Dominik Bohuslav who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Abstract

The purpose of this study is to expand literature on app and mobile website usage by leading companies in the world, by examining both apps and mobile websites of each company with two rating scales: MARS-1 for apps and MARS-2 for mobile websites. Analyses of these data provide overview of companies’ performances in terms of apps and mobile websites and which of the four factors (engagement, functionality, aesthetics, and information) is

underperforming/overperforming compared to others. Results were analysed based on ranking, region, revenue, industry type, and orientation. This study showed that the higher the ranking the better chance of higher score, B2C companies score higher than B2B, the Americas lead in website quality, whilst Europe leads in app quality, higher revenue does not mean better score, and that factors are highly correlated. Managers and companies can employ the findings of the study to improve their strategies in developing and providing apps and mobile websites to customers, as trends and areas to improve are going to be highlighted throughout the study. This study is unique and contributes to literature as it follows and expands previous academic literature about websites or social media in a field that was not researched before.

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1. INTRODUCTION

Mobile marketing is a field that has been on the rise in recent years. Shankar &

Balasubramanian (2009) define mobile marketing as “the two-way or multi-way communication and promotion of an offer between a firm and its customers using a mobile medium, device, or technology”. Especially in a past couple of years M-commerce risen rapidly and there are expectations that this rise will continue in the future at this pace. In USA, as an example, it is estimated the M-commerce sales to reach $123.13 billion in 2016, which is 39.1% higher than in 2015 and remarkably more than double the amount recorded in 2014 (EMarketer, 2016). The growth in M-commerce is driven by the shift of mobile usage. A report from 2013 states that 26% of mobile research by customers start on branded apps, 33% on branded websites, and 48% start on search engines (possibly moving to branded website/app from there) (Jacob Nielsen, 2013). Currently, more people spend time on mobile than on desktop, and out of the whole time spent on mobile, 86% is spent in apps (Barakat, 2014). Regarding apps in particular, in 2009 there were 2.52 billion apps downloads, a number that is expected to skyrocket to 268.69 billion in 2017 (Statista, 2016). These numbers show that establishing native branded app or mobile friendly website is critical part of mobile marketing, especially in the light of significant growth in developing countries which means these numbers will inevitably rise in the future. People use mobile devices to search information about products, compare prices, and especially to buy products online. A forecast suggests that while in 2011, there were around 20% of mobile retail buyers as a percentage of mobile internet users, in 2017 this number will more than double to around 50% (Forrester, 2012).

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10 For the purposes of quality assessment, two rating scales (frameworks) will be introduced, one for the apps (MARS-1) and one for the mobile websites (MARS-2). Frameworks are divided into four sections, each representing the assessment of a particular factor. These factors are

engagement, functionality, aesthetics, and information. These four factors together form an average which provides overall app/website quality mean score.

The core contribution of this study will be an overview of the field and companies will be divided into different sections containing industry type, size, facing (B2C or B2B) and evaluated in their mobile marketing efforts. Academic literature so far lacks research about companies’ use of mobile websites and apps. In the past many new ‘trends in technology’ such as websites, corporate blogs, or social media were researched by academics to provide valuable findings for literature. As the app and mobile website fields are relatively new, they provide an affluent area for further research, as well as a possibility to follow up and expand previous studies in different areas. Therefore, this study is intended as a status report and fills a gap in knowledge of leading companies’ app and website usage. Furthermore, it provides practical implications for managers and companies, which can apply the findings of this study to strengthen their future

development and provision of apps and mobile websites to accommodate users with higher quality service.

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2. LITERATURE REVIEW

2.1 Trend on researching new technology in the past

There are several examples from the past about conducting research when new technology, such as mobiles, came in play. In 1995, when use and application of websites was rising remarkably, Liu et al. (1997) underwent a study to get an idea how many Fortune 500

companies are using websites, what kind of information they present on these websites, or if there are any differences in revenues of companies that use websites compared to those that do not. The research was an initial attempt to examine competitiveness among Fortune 500 companies via data gathering from home page visits. The study also provided valuable insights into to future trends of usage of websites by large business organizations.

Similarly to Liu et al. (1997), in 2005, emerging marketing element among companies called corporate blogging was a focus of interest of Lee et al. (2006). The purpose of that study was to investigate a new phenomenon of corporate blogging and its objective. The study focused on Fortune 500 companies and how they maintain control and support employees’ autonomy in corporate blogging. Lee et al. (2006) found that many companies have a high control because of their top-down blogging strategies, while only few have bottom-up strategies that promotes employees’ autonomy. Those companies focusing on bottom-up blogging tend to aim for customer service and product development content strategies. Top-down blogging companies, on the other side, tend to focus on promotional or leadership content strategies. The main implication of this study was that developing a truthful relationship with customers is crucial for customer relationship of the companies that adopt corporate blogs. These findings were

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12 important foundations for future research and showed the importance of research conducted on new technology that are companies employing. Similarly, this thesis will provide information on field that was not thoroughly studied before.

Another example is the study by Yu et al. (2013) who studied the impact of social media like Facebook, Twitter, and conventional media on firm equity value. Social media is a huge emerging field, which is gaining more followers each day and thus have great importance for firms in marketing practice as well. Yu et al. (2013) found that overall social media had a strong relationship with firm stock performance than conventional media. Additionally, social and conventional media have a strong interaction effect on stock performance. The research, moreover, also found that different types of social media have different impacts. It was one of the first studies to examine the effect multiple sources of social media together with the effect of conventional media, while also examining their importance and interrelatedness. Yu et al. (2013) found that differentiation and leveraging the specific impact of numerous sources of media outlets is important during the implementation of their social media marketing strategies.

These study examples show that often, when there is a new element in marketing, several papers try to address effect of these elements on firms’ performance or make an overview of their use. However, academic field nowadays lacks deeper insight about information on apps and mobile websites. This study will fill a gap in knowledge of leading companies’ app and mobile website usage and provide grounds for future research. On top of that it will provide valuable information and room for improvement for managers and companies and show how

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13 leading companies use apps and mobile websites. Leading companies are often followed and act as an example for followers therefore it is important to highlight shortcomings and strengths that occurs nowadays to fulfil each company’s potential.

2.2 Mobile app factors

Academic scholars’ opinions on apps and mobile websites are going to be discussed in following paragraphs. First, past studies on mobile apps are going to be introduced.

There have been many studies done in understanding the design of mobile application. Many of them addressed several issues found with previous studies. For example, the main aspects of applications and mobile websites is that people use them on the move. Remarkably, this is something that developers often forget about or do not put emphasis on. For this reason Harrison et al. (2013) came with their study that addressed this problem as they focused on usability of mobile applications in their paper. They conducted a literature review of articles about mobile usability models and identified several issues including the one mentioned above. The final model was mostly inspired by Nielsen’s Model of Usability (J. Nielsen, 1994). They addressed these issues with new PACMAD (People At the Centre of Mobile Application Development) model that put together attributes from other models in a way to address the issues. PACMAD model consists of these attributes: effectiveness – user’s ability to complete a task in specified context, efficiency – user’s ability to complete task with speed and efficiency, satisfaction – pleasantness and comfort afforded to user, learnability – time by which user is able to understand important factors of the application, memorability – extent to which user can remember the aspects of application after some time, errors – how many errors do users do

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14 while using the application, cognitive load – addressing circumstances in which is an application used, and three factors of use: user, task, and context of use. An app that is considered to be of good usability should score high in these aspects. However, as clear from the brief description of the factors, although the model from Harrison et al. (2013) as well as from other scholars is very extensive and detailed it is also hard to use and interpret.

Therefore, Stoyanov et al. (2015) took this further and introduced a complete mobile app rating scale (MARS) which aim is to provide easy to use rating scale to be used by anyone interested in it. MARS was developed with the aim to provide objective rating of apps as for example the star-ratings system used by app stores lack objectivity. The app reviewers are by nature subjective and the reviews can come from suspicious sources, therefore MARS’ purpose is to provide a reliable and objective multidimensional measure of the quality of the apps. MARS focuses on Health mobile apps but its general quality criteria let it be used on other app categories as well with slight adjustments as stated by authors. Stoyanov et al. (2015) built on previous research, including Harrison et al. (2013) and presented four objective quality scales: engagement, functionality, aesthetics, and information quality. On top of that there is fifth section that is concerned with subjective quality scale. These five sections are further divided into 23 questions that form the MARS. Together they form a reliable, multidimensional measure to not just rate but also trial and classify mobile apps. It is important to score high in each factor, preferably in each factor evenly (Stoyanov et al., 2015). It is essential to have a

‘balanced’ app in terms of factors for user’s better satisfaction. MARS rating scale provides high consistency and reliability even by a single reviewer which is extremely important especially for

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15 the purposes of this thesis. The original scale has a high reliability as internal-consistency alpha was 0.92 and inter-rater reliability Intra-Class Correlation was 0.85 (Stoyanov et al., 2015).

2.3 Mobile website factors

Mobile websites are the second focus of this thesis. Academic research on website design and factors that are occurring the most are going to be discussed in the following paragraphs. Unlike apps that are ‘individual entities’ mobile websites are usually just main website displayed differently on mobile devices so it is easier and more intuitive for user to use it. Therefore, research on mobile websites is really intertwined with the research on websites in general. Consequently, this section will focus on scholars’ view on both websites and mobile websites. Purpose of this section is to introduce mobile website factors occurring in literature in the past few years, which can be used in creation of ranking scale.

Research by Rosen & Purinton (2004) about website design can be easily interpreted to mobile website design as well. The urge for studying website design came from a knowledge that right web design contributes to repeated visits which makes it critical. Therefore, they developed a Website Preference Scale (WPS) that was based on work in environmental psychology of Rachel Kaplan and Stephen Kaplan. Rosen & Purinton (2004) argued that Kaplan & Kaplan's (1982) Preference Framework, which was developed to provide guidance in designing physical landscapes, is easily applicable to internet as it too is highly cognitive and information driven. Study suggests that simplicity, minimalistic design, eye-catching but appropriate graphics and categories that are appealing to users appears to be effective. Further, distinction of the

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16 website from others, memorability, fast loading and logical information all seem to be

important to web design.

Another important study, which is later incorporated into the rating scale MARS-2, is the one by Gehrke & Turban (1999), and even though it is of an older date, it is still relevant, as most of the factors introduced are occurring in literature until this day. They identified page loading,

business content, navigation efficiency, security, and marketing/consumer focus as main areas for companies to focus on during building of a website. For example, page loading speed and business content factor, which consists of clear and concise text, simple background colours, or useful information, were mentioned in Rosen & Purinton (2004) as well. Similarly, navigation efficiency is crucial part of any good web design as mentioned by many relevant academic articles on this matter (Gehrke & Turban, 1999; Rosen & Purinton, 2004; Stoyanov et al., 2015). Others suggested that easy to understand website and smooth user behaviour are core to website quality (Tan & Wei, 2006). Tan & Wei (2006) created illustration of the flow of cognitive processes during browsing. The research provided valuable information about the right

structure and navigation within websites that were used in many subsequent studies. Moustakis et al. (2004) conducted a research on website quality assessment criteria. Their research presents hierarchical framework that divides assessment criteria into five categories: content, navigation, design and structure, appearance and multimedia, and uniqueness (Moustakis et al., 2004). The categorization is more or less in accordance with other scholars that created frameworks on website quality assessment which confirms the general agreement on main assessment factors in this field (Stoyanov et al., 2015).

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17 Overall, the ‘main’ factors occur on a very regular basis since the beginning of website research. Consequently, these will be discussed in more depth in the next section.

2.4 Division of the factors

Throughout the many years of research on both apps and mobile websites several factors and elements were occurring numerous times. Based on the studies, elements were grouped into four factors, just as Stoyanov et al. (2015) did in their MARS model, which will be used for throughout this thesis. The grouping is clear, simple, and suitable for full evaluation of both apps and mobile websites. The four factors are: engagement, functionality, aesthetics, and information. Factors for both app and website are the same only difference comes in particular elements within factors therefore the following subsections belong to both apps and websites.

2.4.1 Engagement

Engagement is about entertainment for user, interest, customization of app for user liking, interactivity and fact if the app content is appropriate for target audience (Stoyanov et al., 2015). Pagani & Mirabello (2011) defined engagement in websites as the state of being

involved, interested, retained, and occupied in something. Engagement can be also viewed as a series of behavioural and emotional activities including problem-solving, evaluation, decision-making processes or reasoning (Kim et al., 2013). Huizenga (2009) clearly showed on her

experiment that higher engagement within apps and websites has very positive effects on user’s experience. The main purpose of Engagement is to engage people, get their attention, through various elements such as fun or interactiveness. Engagement is applied in apps or websites through entertainment (fun), strategies to increase interest, customisability to fit each user’s

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18 preferences, or strategies to make users to repeat their visits to app or mobile website (Kim et al., 2013; Pagani & Mirabello, 2011; Rosen & Purinton, 2004; Stoyanov et al., 2015).

2.4.2 Functionality

Functionality consists of aspects like app performance, ease of use and learning, or ease of navigation inside the app. Aladwani & Palvia (2002) presented parts of functionality aspects in one of their four dimensions of perceived web quality under technical adequacy section.

Navigation and ‘Structure and Design’ were also two of five main factors identified by Moustakis et al. (2004) when evaluating website quality. They identified several subsections that formed foundations of MARS Functionality section in the future. These were, for instance, ‘Ease of use of navigation tools’, ‘Order of elements’, ‘Loading speed’, and ‘Convenience of navigation tools’. Functionality is applied in apps or websites through accuracy and speed, ability to learn the app or website quickly, or ease of navigation (Aladwani & Palvia, 2002; Moustakis et al., 2004; Stoyanov et al., 2015).

2.4.3 Aesthetics

This factor mostly deals with how the app looks like, what is the graphic design, layout, visual appeal, colour scheme, etc (Stoyanov et al., 2015). Aesthetics is important when evaluating app but certainly not all important. Tuch et al. (2012) found that aesthetics does not affect

perceived usability implicating that good app aesthetics must be accompanied with other factors such as Functionality one to have a positive impact on user. Lavie & Tractinsky (2004) stated that “aesthetics is a strong determinant of pleasure experienced by the user during the interaction” (p. 277). Aesthetics is also strongly present in already mentioned Moustakis et al.'s

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19 (2004) model. Moustakis (2004) among other things presented readability as a very important element of mobile websites that could be achieved through possibility to enhance (or diminish) letters within the app. Aesthetics is applied in apps or mobile websites through the quality of app/website layout, resolution of graphics, or visual appeal (Lavie & Tractinsky, 2004; Moustakis et al., 2004; Stoyanov et al., 2015; Tuch et al., 2012). Although the evaluation of this factor may seem subjective rating scales use strict criteria for evaluating questions about aesthetics. They usually use scale where each stage of rating has a description assigned. Furthermore, reliability and consistency tests are used to evaluate the rating scales. As an example MARS rating scale has both reliability and consistency on exceptionally high levels (Stoyanov et al., 2015).

2.4.4 Information

Information factor focuses on accuracy of app description, quality and quantity of information, or credibility (Stoyanov et al., 2015). Moustakis et al. (2004) divided content (information) section into 5 sub-criterion: ‘Utility of content’, ‘Completeness of information’, ‘Subject

specialization’, ‘Reliability of content’, and ‘Syntax of content’. It is clear that MARS authors got ‘inspired’ by Moustakis et al. (2015). Information content within app or mobile website is important as it “relates to the responsiveness of a website to satisfy a user inquiry and to the trustfulness about the information which in included in the site” (p. 62, Moustakis et al., 2004). Information factor could be found in apps or websites through accuracy of app description, quality and quantity of information, or explanation of graphs/pictures/tables. Rosen & Purinton (2004) stated that logical organization of information is also very important element of website or app design as it participates on user’s experience and satisfaction.

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2.5 Literature gap and research focus

The previous section looked at the different factors that together form a measurement method for app and website quality. A good app and mobile website should score high in the mentioned factors. I presented every factor in more detail with additional information from other literature if applicable.

All scholars came up with their own versions of web or app measurement models, some very different, some very similar to others. Many of them tested out their models with experiments or surveys to find out if the factors chosen are consistent and relevant.

For the purposes of this thesis MARS (Mobile App Rating Scale) rating scale from Stoyanov et al. (2015) is going to be used for app evaluation. Slightly changed version of MARS is going to be used for mobile websites evaluation. Both of these are going to be presented in next sections. This thesis will explore how companies are doing in terms of mobile marketing effort through use of apps and mobile websites. Findings, that will have an overview per industry and size of companies, will be discussed and conclusion drawn out of them.

This study is unique and valuable to literature as it follows and expands previous academic literature about websites or social media in a field that was not researched before. Relatively new field of app and mobile website provides a rich area for further research as well as

possibility to follow up and expand previous studies in different areas. This study is intended as a status report and fills a gap in knowledge of leading companies’ app and website usage.

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2.6 Study’s expected outcomes

It is expected that there will be differences in marketing effort between companies from different classifications (industry, size, location). It is expected that the larger the company (in this case, in terms of revenue) the better the mobile website and app score. This is a finding by Liu et al.'s (1997) found that bigger companies tend to invest in websites. As larger companies tend to have more resources and manpower it can be expected that they will also have higher quality apps and mobile websites (Liu et al., 1997).

Similarly, it is expected that B2C companies are going to score higher in mobile website and app quality compared to their counterparts from B2B sector. B2C companies are aiming at masses who they need to engage through high quality service, which can be achieved through

information technology such as mobile websites and apps (Liu et al., 1997). Furthermore, it is expected in this study that technology, retail, and financial services companies are going to score higher in app and mobile websites evaluation, which is based on findings by Lie et al. (1997) during their research on corporate website usage.

The leading companies from various top lists, such as Fortune 500, have long been considered as leaders in the use of information technology. This study should show the high standard of mobile websites and apps presented by these companies with overall numbers for most of them scoring well above the score of 3, which is considered to be an average score (Robbins &

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3. METHODOLOGY

To explore the use of app and mobile website this study used two sets of database collections. One for mobile websites and one for mobile apps. This section addresses how the data was collected, why particular companies were chosen, and how the data was analysed.

3.1 Research design

This study used Interbrand’s best global brands 2016 rankings of top 100 brands as foundation for list of companies that are going to be researched (list of companies in Appendix D). Apps and mobile websites of those hundred companies were then examined (selection criteria listed in 3.4 Data Analysis). This ranking shows the best (most valuable) brands in the world. Interbrand has a track record of valuating brands for many years and is also acknowledged in its field of brand consultancy, specialization in brand strategy, brand analytics, naming, and corporate design. In addition, Interbrand was also the first company to have its methodology certified to be in compliance with requirements of ISO for monetary brand valuation (Interbrand, 2016). There were strict criteria considered by Interbrand while developing this ranking: 1. at least 30% revenue must come outside of the brand’s home region; 2. brand must have significant

presence across continents including Asia, Europe, and North America as well as being present in emerging markets; 3. there must be financial performance data of the brand available publicly; 4. there has to be positive economic profit expected in the longer term (return higher than cost of capital); 5. brand has to have awareness and publicly known across major

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23 On top of that, Interbrand uses three key components when evaluating the brands. These are: 1. financial analysis that measures overall economic profit or financial return to investors of the company; 2. role of brand that measures portion of purchases that could be attributed to the brand; 3. brand strength that measures ability of the brand to create loyalty which leads into profit and demand in the future (Interbrand, 2016).

These criteria ensure highest quality, trustworthiness and validity of the Best Global brands. On top of that Interbrand’s global brand rankings is a very diverse one and thus suits well to this study that aims to explore various industries, companies of various sizes and from various regions (Interbrand, 2016). It was also chosen because leading companies from similar rankings, such as Fortune 500, represent leadership in the use of various information technology and are often followed by other companies in their fields. Therefore, making the chosen companies likely the best there are (Li et al., 1993).

3.2 Rating scales

In my research I will use two rating scales that are both ‘inspired’ by MARS. MARS-1 will be used for apps and MARS-2 for mobile websites.

3.2.1 MARS-1

MARS-1 (Appendix A) has four main sections each focusing of one factor. These are:

Engagement, Functionality, Aesthetics, and Information. Together, they form a complete picture about the app and correspond with researches presented in literature review. MARS-1 is slightly modified version of original MARS to fit the kind of apps that this research will look at. Like already mentioned it consists of 4 sections which scores are averaged and form Overall App

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24 Mean Score. In total it consists of 15 questions each having a rating scale of 1-5 (1 being

worst/weakest and 5 being strong/best).

3.2.2 MARS-2

The second rating scale (Appendix B) evaluates mobile websites. It also has the same 4 sections to correspond with MARS-1 (and MARS) as much as possible. However, as the nature of website quality evaluation undergoes different criteria, as highlighted in literature review, several questions from MARS-1 were omitted and on the other side some number of them added into the scale. Care was put into organizing the question and sorting them into the section so they fit seamlessly within the scale. It consists in total of 16 questions each having a rating scale of 1-5. Aspects that were added into the scale based on literature research are: has the mobile website enough content to be interesting to repeat visitors? (Question 4), is the website unique? (5), is the website readable? (12), and is the organization of information logical? (16).

3.3 Quality ratings

As mentioned before MARS-1 and MARS-2 uses five point rating scales. For further understanding the quality ratings correspond to these levels: 1. Inadequate, 2. Poor, 3.

Acceptable, 4. Good, 5. Excellent. Acceptable referrers to medium level as the common ground

for most apps and mobile websites that are working without problem. Going down towards

Poor and Inadequate means that some important components are missing, in terms of

Inadequate to an extent that an app or mobile website is hardly usable. Going up towards Good

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25 use of the app or mobile website and promote overall better user experience (Stoyanov et al., 2015).

3.4 Data collection

Data were collected and written into an excel spreadsheet (example in appendix C) so the values can be easily analysed later on. Rating was performed on mobile device Samsung A5 A500F (2015) which uses Android operation system. Samsung A5 is considered as a good quality mobile phone with average performance therefore making it a good fit for research as

apps/mobile websites should be optimized for both high performance (high-end) smartphones as well as for the ones that do not have such a power (Samsung, 2015).

Mobile websites were accessed through Chrome browser which is the most used mobile browser in the world, making it a most likely browser In which the users open companies’ websites (StatCounter, 2017). After that the website was being looked at and examined for at least 10 minutes before the rating scale was used to determine its quality.

Apps were accessed and downloaded from Google Play Store, official app store for Android smartphones, and similarly being looked at and examined for at least 10 minutes before evaluating it with rating scale.

Data were collected in Microsoft’s Excel program and coded based on following guidelines:

Ranking consists of ranking provided in Interband’s top 100 global brands (Interbrand, 2016).

Rating goes from 1 (being highest ranking company – having most valuable brand) to 100 (least valuable brand on the list).

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Industry type division is also taken from Interband’s website and consists of 17 industry types

which are: Alcohol, Apparel, Automotive, Beverages, Business Services, Diversifies, Electronics,

Energy, Financial Services, FMCG, Logistics, Luxury, Media, Restaurants, Retail, Sporting Goods,

and Technology (Interbrand, 2016).

Revenue was divided into 5 categories: 1 - < $10bn. 2 - > $10bn and < $30bn, 3 - > $30bn and <

$50bn, 4 - > $50bn and < $100bn, 5 - > $100bn. Revenue of companies were taken from Fortune 500 or from companies’ financial report from year 2016 (Fortune 500, 2016).

Orientation consists of two option. If company makes most of its revenue from B2B it is

classified as B2B company in the dataset. On the other hand if it makes higher revenue from B2C sales it is classified as B2C (B2B Marketing, 2016).

Region is coded based on data provided in the Interbrand’s list. Region refers to the country

where the company originated.

3.5 Data analysis

There was one company missing a mobile website (Kellogg’s) and, therefore, was omitted from the analysis.

Because of various reasons app analysis was missing 29 data entries. For example, the app was not accessible as a bank account in the company was required, car from the company was required to login to app and start using it, etc. Some companies did not have apps at all or only provided apps that have very few downloads. Those with less than 500 downloads were omitted from the research as these apps are without any promotion from a company and there is clearly

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27 not much effort put into the app. Omitted companies are Moet & Chandon, Burberry, Jack

Daniel’s, Prada, Hermes, Adobe, Harley-Davidson, Discovery, Gillette, Corona, Ralph Lauren, Johnnie Walker, Smirnoff, Colgate, 3M, Goldman Sachs, Panasonic, Samsung, and Apple. The

companies whose apps were not accessible were: Cartier, Morgan Stanley, Oracle, KFC,

American Express, Cisco, IBM, J.P. Morgan, Nissan, and Allianz.

Collected data were analysed in Microsoft’s Excel program. Pivot tables were used for simple descriptive data and Data Analysis add-in was used for correlations and overall descriptive statistics. Collected rating scale scores were analysed within several types.

First of all, figures were created when applicable with Excel for visual representation of division of data for reader’s better understanding. Furthermore, tables were created when applicable and mean and standard deviation were calculated with excel functions. Lastly correlation values between factors were calculated with Excel function. Correlations values are accompanied with p-values that were calculated through regression function within Excel program.

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4. RESULTS

This section provides results and analysis of the data. Firstly, websites’ results are discussed, and secondly, apps’ results are established. To pick up on section 3.3, scores in the results section represent the quality that the each element, or overall app or mobile website, represents. Higher the score the better the element. For example score of 3.40 represents the result between acceptable (3) and good (4). Therefore, it can be said that the app or mobile website scored above the medium level (the average), which is Acceptable, but it does not possess the features to go all the way to Good, which would mean that something of a considerable amount of extra had been done to promote better user experience. Another example, of a score of 3.90, shows similar outcomes. However, it could be concluded that the second example represents an app or mobile website that is slightly superior to its previous counterpart. It shows that it scored higher in at least one factor making it an overall better product for users.

4.1 Websites results

4.1.1 Overall mobile website results

Engag. Function. Aesthet. Informat. Web. q.

Mean 3.41 Mean 3.99 Mean 3.98 Mean 3.806 Mean 3.800

Median 3.4 Median 4 Median 4 Median 4 Median 3.883

Mode 3.2 Mode 4.33 Mode 4.25 Mode 4.25 Mode 3.592

StaDev 0.73 StaDev 0.83 StaDev 0.70 StaDev 0.578 StaDev 0.605 Range 3.8 Range 3.66 Range 3.5 Range 2.75 Range 3.092 Minimum 1.2 Minimum 1.33 Minimum 1.5 Minimum 2 Minimum 1.671

Max 5 Max 5 Max 5 Max 4.75 Max 4.763

Count 99 Count 99 Count 99 Count 99 Count 99

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29 Overall mean value of Overall Website Quality is 3.80 out of 5. This number suggest that app quality is on decent levels in average. Distribution of scores is narrow with overall standard deviation of 0.60. One company (Kelloggs’) was omitted from the database as it did not have functional mobile website at the time of testing. This left the number of companies analysed on 99. Table 14 summarizes the mean, median, mode, standard deviation, range, minimum value, maximum value, and count of the entire mobile website section. Some of these values are going to be discussed in more depth in following paragraphs.

4.1.2 Industries

Figure 1: Number of companies in industries

99 companies that were tested and data were gathered from them were divided into 17 industry types. As presented in the graph, several industries have just a few representatives from companies. As industries with small number of companies cannot really provide adequate data, those with the number of companies being less than 4 were not taken into account when

0 2 4 6 8 10 12 14 16

Total

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30 evaluating industry data. Therefore, the ones that were left are: Automotive, Technology,

Financial Services, Luxury, FMCG, Alcohol, and Diversifies industry types.

Industry Mean Standard Deviation

Alcohol 3.91 1.03 FMCG 3.83 0.44 Technology 3.79 0.65 Automotive 3.78 0.55 Luxury 3.73 0.48 Financial Serv. 3.66 0.69 Diversifies 3.47 0.61 Grand Total 3.75 0.62

Table 2: Mean and standard deviation of Overall Website Quality based on industry

After keeping only industries with above 4 companies it is visible from the table 2 that the highest performing industry is Alcohol with mean Overall Website Quality of 3.91. Alcohol industry also has the highest standard deviation of 1.03 which suggests that the distribution of scores is wider compared to other industries. Lowest performing industry is Diversifies that has mean Overall Website Quality of 3.47. The differences between the highest performer and the lowest one are quite small which suggest relatively even app quality across industries with only minor differences. Average mean across these selected industries is 3.75, slightly lower than that of all industries which is 3.80. Standard deviation is higher from 0.60 to 0.62. As the average score for selected industry types was 3.75, this leaves Alcohol, FMCG, Technology, and

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Alcohol 4.33 1.33 Financial Serv. 4.06 0.90 Diversifies 3.93 0.43 Automotive 3.93 0.98 FMCG 3.92 0.75 Technology 3.88 0.95 Luxury 3.58 0.71 Grand Total 3.94 0.90

Table 3: Mean and standard deviation of Functionality based on industry

Table 3 shows values for one of the factors Functionality based on selected industry types. Interestingly Alcohol has high mean value of 4.33 but also large standard deviation of 1.33 which suggests wide score distribution. Luxury on the other side has a low mean score of 3.58. These numbers suggest that companies from Luxury do not focus much on functionality aspects of their websites, while others like Alcohol and Financial Services do. The average mean score is a high 3.94 with standard deviation of 0.90.

Technology 3.57 0.89 FMCG 3.50 0.51 Alcohol 3.49 0.99 Luxury 3.48 0.70 Automotive 3.27 0.66 Financial Serv. 3.02 0.72 Diversifies 2.80 0.98 Grand Total 3.32 0.78

Table 4: Mean and standard deviation of Engagement based on industry

The numbers about Engagement show that engagement level of companies’ websites is quite low. Highest value being 3.57 that of Technology while Diversifies drops to very low score of 2.80. Selected industries have mean score 3.32 with average standard deviation value being 0.78.

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Luxury 4.13 0.48 Technology 4.07 0.57 Alcohol 4.07 1.22 FMCG 4.03 0.43 Automotive 4.00 0.49 Financial Serv. 3.67 0.83 Diversifies 3.65 0.68 Grand Total 3.96 0.67

Table 5: Mean and standard deviation of Aesthetics based on industry

Aesthetics numbers seem to be evenly distributed across industries with overall mean value

being high 3.96. All of the selected industries focus heavily on aesthetics of their websites except Financial Services and Diversifies which scored lower values of 3.67 and 3.65. Overall average standard deviation is 0.67 which suggest narrow distribution of scores. Alcohol once again leads the standard deviation column which could indicate that among the 7 companies from alcohol industries there are large differences in app quality and factors.

Automotive 3.93 0.47 Financial Serv. 3.90 0.56 FMCG 3.88 0.60 Alcohol 3.75 0.74 Luxury 3.75 0.38 Technology 3.63 0.68 Diversifies 3.50 0.61 Grand Total 3.79 0.57

Table 6: Mean and standard deviation of Information based on industry

Information factor shows quite even distribution, similarly to Aesthetics. The highest performing

and lowest performing are just 0.43 point apart. Overall average mean score is 3.79 and average standard deviation is 0.57.

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4.1.3 Orientation

Figure 2: Number of companies based on Orientation

99 companies were divided into B2B and B2C categories. B2B has 33 companies and B2C has 66.

Orientation Mean Standard Deviation

B2B 3.75 0.49

B2C 3.82 0.66

Grand Total 3.80 0.60

Table 7: Mean and standard deviation of Overall Website Quality based on orientation

As shown in the table 7 the difference between B2B and B2C is very small. B2C has higher mean of 3.82 while B2C scored 3.75, making B2C score slightly above average of 3.80. Standard deviation is smaller within B2B companies with 0.49 while B2C scored 0.66 suggesting slightly wider distribution of scores with the double the amount of companies.

0 10 20 30 40 50 60 70 B2B B2C

Total

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B2B 3.24 0.69

B2C 3.51 0.75

Grand Total 3.42 0.74

Table 8: Mean and standard deviation of Engagement based on orientation

Differences between B2B and B2C in Functionality, Aesthetics, and Information were very small and did not show any pattern leaning towards either of the types of orientation. Only difference being Engagement where B2C scored higher than B2C. B2B has 3.24 mean scale of Engagement while B2C has higher score of 3.51. This might suggest that B2C companies are focusing on the engagement factor within their websites more than B2B companies.

4.1.4 Revenue

Revenue Mean Standard Deviation

1 3.82 0.64 2 3.82 0.63 3 3.91 0.37 4 3.60 0.68 5 3.87 0.62 Grand Total 3.80 0.60

Table 9: Mean and standard deviation of Overall Website Quality based on revenue

Companies were divided into 5 sections based on their revenue as presented in methodology section. First observation derived from the table is that there is no clear relationship between

Revenue and Overall Website Score. Section 3 has highest score of 3.91 and on the contrary

section 4 has the lowest one with 3.60. Section 3 has also smallest standard deviation with other sections being close to each other. Interestingly, all revenue categories scored above the average of 3.80 except for category 4 that scored 3.60.

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35 Subsequent data within Engagement, Functionality, Aesthetics, and Information factors show the same trend visible in table 8 with section 4 having lowest score while others having similar ones.

4.1.5 Region

Figure 3: Number of companies based on Revenue

Countries were divided into three regions of Americas, Europe and Asia with 53, 35, and 11 representatives respectively.

Region Mean Standard Deviation

Americas 3.92 0.50

Europe 3.71 0.70

Asia 3.53 0.66

Grand Total 3.80 0.60

Table 10: Mean and standard deviation of Overall Website Quality based on region

As presented in the table, Americas is the region with best Overall Website Score of 3.92, making it the only region that scared above the average of 3.80. Europe comes second with

0 10 20 30 40 50 60

Americas Europe Asia

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36 3.71. Asia comes with low score of 3.53. Interestingly Americas has low standard deviation of 0.50 which suggests narrow distribution of scores and overall high quality of websites across companies. Europe and Asia have slightly higher standard deviation of 0.70 and 0.66. Results show that Americas maintains higher standard of quality of websites compared to other

regions. Companies from Asian market on the other hand seem to focus on website quality less.

Americas 3.57 0.66

Europe 3.33 0.86

Asia 2.96 0.43

Grand Total 3.42 0.74

Table 11: Mean and standard deviation of Engagement based on region

Values of Functionality, Aesthetics, and Information followed the trend set by table 9 with similar differences between regions in mean and standard deviation. Interesting insights can be derived from table 10 that shows low values for Engagement especially the one of Asia. This suggest that there is little focus on engagement within the Asian company websites.

4.1.6 Ranking

Rankings Mean Standard Deviation

1 3.92 0.59 2 3.74 0.63 3 3.76 0.51 4 3.73 0.64 5 3.85 0.67 Grand Total 3.80 0.60

Table 12: Mean and standard deviation of Overall Website Quality based on rankings

Companies were divided into five sections based on their ranking in Interbrand’s ranking. It is visible from the table that the highest ranked companies (category 1) show best results of 3.92

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37 score on average. This suggest that leading companies have better quality mobile websites. Category 2, 3, and 4 scored below average, with category 5 scoring 3.85 which slightly above the average of 3.80.

4.1.7 Correlations between factors

Engagement Functionality Aesthetics Information

Engagement 1 Functionality 0.562 1 (0.00) Aesthetics 0.610 0.704 1 (0.00) (0.00) Information 0.521 0.689 0.606 1 (0.00) (0.00) (0.00)

Table 13: Correlations between factors with p-value

A Pearson product-moment correlation coefficient was computed to assess the relationship between each factor. There was only positive correlation present between the variables. As table 11 clearly shows all of the observations had correlation with large effect (± 0.5). Largest effect is between Functionality and Aesthetics with r = 0.704. The smallest effect, but still a strong one, is between Information and Engagement. Overall, across the 6 relations, there was a positive correlation between each of them with strong correlation in all cases. P-value is very low in each of the correlations making it a very significant result as p is lower than 0.05.

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4.2 Apps results

4.2.1 Overall apps results

Engagem. Function. Aesthet. Inform. Q mean

Mean 3.69 Mean 4.162 Mean 4.117 Mean 4.22 Mean 4.050 Median 3.75 Median 4.250 Median 4.333 Median 4.25 Median 4.167

Mode 3.5 Mode 4.25 Mode 4.67 Mode 4.25 Mode 4.31

StanDev 0.69 StanDev 0.605 StanDev 0.840 StanDev 0.38 StanDev 0.487

Range 3 Range 2.5 Range 3.33 Range 2.25 Range 2.27

Minimum 2 Minimum 2.5 Min 1.67 Min 2.75 Min 2.542

Maximum 5 Maximum 5 Max 5 Max 5 Max 4.813

Count 71 Count 71 Count 71 Count 71 Count 71

Table 14: Summary table of mobile website results section

Overall mean value of Overall App Quality is 4.05 out of 5. This number suggest that app quality is on high levels in average. Distribution of scores is really narrow with overall standard

deviation of 0.49. Several companies have been omitted from the database because they either did not have app or the app was not fully accessible because of various reasons (did not have account in the bank, did not have car to connect to app, etc.). This leaves the total number of analysed apps on 71.

Table 14 summarizes the mean, median, mode, standard deviation, range, minimum value, maximum value, and count of the entire app section. Some of these values are going to be discussed in more depth in following paragraphs.

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4.2.2 Industry

Technology 4.26 0.36

Automotive 4.12 0.39

FMCG 3.90 0.35

Financial Serv. 3.83 0.66

Grand Total 4.06 0.45

Table 15: Mean and standard deviation of Overall App Quality based on industry

Similarly as with websites only those industries that have at least 5 representatives are considered in following analysis. These are Technology (9 companies), Automotive (13), FMCG (7), and Financial Services (7). From the selected industries Technology performed best with high score of 4.26 and standard deviation of 0.36. Financial Services scored the lowest with 3.83 and had the highest standard deviation of 0.66 as well. Further analysis of individual factors showed similar trend with only minor differences. From the selected industry types only

Technology and Automotive scored above average score of 4.06. 4.2.3 Orientation

B2B 3.87 0.59

B2C 4.13 0.41

Grand Total 4.05 0.49

Table 16: Mean and standard deviation of Overall App Quality based on orientation

B2B consists of 23 companies while B2C consists 48. First observation derived from table 12 is that B2C companies performed notably better in app quality with 4.13 compared to B2B apps with 3.87. This follows the trend set by websites and suggests that B2B companies focus less on app quality than their B2C counterparts. B2C similarly to mobile website results scored above

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40 the average score of 4.05 by some distance. Standard deviation is higher for B2B companies which shows that that score distribution is slightly wider compared to B2C.

B2B 3.78 1.04

B2C 4.28 0.68

Grand Total 4.12 0.84

Table 17: Mean and standard deviation of Aesthetics based on orientation

After taking a closer look at individual factors it is clear that in Engagement and Functionality the scores follow trend set by table 16. However, table 17 shows that difference between B2B and B2C in terms of Aesthetics is significant one. On top of that standard variation for B2B is 1.04 which suggests quite wide distribution of scores.

4.2.4 Revenue

Revenue Mean Standard Deviation

1 4.23 0.38 2 4.08 0.48 3 3.98 0.63 4 3.94 0.52 5 4.11 0.38 Grand Total 4.05 0.49

Table 18: Mean and standard deviation of Overall App Quality based on revenue

Similarly to website’s table 18 there is no visible pattern noticeable when it comes to

relationship between app quality and revenue. Companies from section 1 surprisingly scored highest with 4.23. Lowest scorer is section 4 with 3.94. Overall the differences are small and do not suggest noticeable differences in app quality with higher revenue levels. Similar values without any pattern are observable for factors as well. Section 3 and 4 scored below average number of 4.05.

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4.2.5 Region

Figure 4: Number of companies based on Revenue

Companies were divided into three categories. As seen on figure 4 Americas has 36 companies,

Europe 27, and Asia 8.

Europe 4.13 0.41

Asia 4.04 0.22

Americas 3.99 0.58

Grand Total 4.05 0.49

Table 19: Mean and standard deviation of Overall Website Quality based on region

Results in table 19 show interesting results. All of the regions scored highly in app quality and

Europe finished first at 4.13 with Asia and Americas second with 4.04 and third with 3.99. This is

in contrast with websites data where Asia underperformed compared to its counterparts. This finding can suggest that Asian market is much better at app development compared to website development. Similarly to mobile website results only one region scored above average number of 4.05, this time it being Europe with 4.13, while Asia came close with 4.04.

0 5 10 15 20 25 30 35 40

Americas Europe Asia

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Asia 4.41 0.33

Europe 4.28 0.40

Americas 4.15 0.38

Grand Total 4.23 0.39

Table 20: Mean and standard deviation of Information based on region

After closer look at Engagement, Functionality, and Aesthetics factors it was visible that it follows trend from table 19. However, Information factor presents interesting findings that position Asia on top with 4.41 average Information score. This may point to the fact that Asian apps present higher informational value than the other two regions while having similar numbers across other factors.

4.2.6 Rankings

Rankings Mean Standard Deviation

1 4.29 0.35 2 3.83 0.51 3 4.00 0.49 4 4.19 0.33 5 3.96 0.62 Grand Total 4.05 0.49

Table 21: Mean and standard deviation of Overall App Quality based on rankings

Similarly to mobile websites section, companies were divided into five sections based on their ranking in Interbrand’s ranking. It is visible from the table that the highest ranked companies (category 1) show best results of 4.29 score on average. This suggest that leading companies have better quality apps than the rest of the companies ranked lower. Category 2, 3, and 5 scored below average, with category 4 scoring a high score of 4.19 which above the average of 4.05.

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4.2.7 Correlation between factors

Engagement Functionality Aesthetics Information

Engagement 1 Functionality 0.406 1 (0.00044) Aesthetics 0.618 0.616 1 (0.00) (0.00) Information 0.351 0.140 0.208 1 (0.0027) (0.2446) (0.0815)

Table 22: Correlation between factors with p-value

A Pearson product-moment correlation coefficient was computed to assess the relationship between each factor. There was only positive correlation present between the variables. As table 22 clearly shows there were two observations with large effect (± 0.5). This is between

Engagement and Aesthetics with r = 0.618, and between Functionality and Aesthetics with r =

0.616. There were medium effect correlation between Engagement and Functionality with r = 0.406, and between Engagement and Information with r = 0.351. Correlation with small effect was observed between Aesthetics and Information with r = 0.208, and between Information and

Functionality with r = 0.140. Overall, across the 6 relations, there was a positive correlation

between each of them, with strong correlation in two cases. P-value show significant results (p < 0.05) except for correlation between Information and Functionality (p = 0.2446), and

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4.3 Performance across apps and mobile websites

Apps Mobile Websites

Qua Mean Qua Mean

Mean 4.050 Mean 3.800 Median 4.167 Median 3.883 Mode 4.31 Mode 3.592 StanDev 0.487 StanDev 0.605 Range 2.27 Range 3.092 Minimum 2.542 Minimum 1.671 Maximum 4.813 Maximum 4.763 Count 71 Count 99

Table 23: Comparison of performance across apps and mobile websites

Table 23 shows the comparison of app and mobile website results. Apps scored slightly higher on average with 4.05 compared to mobile websites’ 3.80. Median follows the similar trend. However, it is interesting that mode, the number that is repeated more often than any other, is only 3.60 within mobile website results. Apps have a more healthy 4.31 score. The standard deviation is higher among mobile website suggesting wider distribution of scores compared to apps. This also corresponds with range that is almost 1 point higher within mobile websites than apps. Minimum values again confirm the trend that mobile website’s results are much widely distributed. Count summarizes the number of analysed companies from each category.

To conclude, overall the apps scored slightly higher than mobile websites but both overall mean numbers are relatively high. Apps reached the Good level that suggests that developers

(companies) promoted user experience that is above the medium basic level of a functional app. Mobile website result is almost there with 3.80 which suggest that leading companies went extra mile to ensure the mobile website are above medium level of functioning mobile website.

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5. DISCUSSION AND CONCLUSIONS

This study presented two rating scales to evaluate apps and mobile websites among leading companies. The study followed up and expanded a previous ones that researched websites, corporate blogs, or social media. It provided summary of the app and mobile website usage. Results confirmed several expectations introduced in the literature review. Highest ranked companies ended up with the highest score for both apps and mobile websites. However, this result was not supported in other categories. The reason for this might be the fact that the top 20 companies have much higher concentration of companies with the large amount of revenue compared to other categories. As findings by Liu et al. (1997) show, the leading and largest companies with highest revenues tend to invest more in informational technology. This was partially confirmed within the revenue section among the apps, where similarly to rankings section, category 1 led with highest score. Within the mobile website section the category 1 was just third highest in terms of score, but the differences between categories were very small. However, it was still expected that higher revenue would mean higher quality score. Therefore, this result does not match the one spotted in the corporate websites study by Liu et al. (1997). As expected, B2C companies scored much higher in both apps and mobile websites compared to B2B companies. The reason for this is, as presented in literature review, that B2C companies aim for mass audience and therefore focus much more on informational technology provided to users.

Industry types did not follow the expected results. This might be because of lack of

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46 significant data that could show different outcomes. Also the predictions were made on

literature from late 1990s and early 2000s when companies from several industries were not as skilled and experienced in information technology. This meant that companies from the industry types, which naturally dealt with information technology, were in ‘advantage’ compared to others. However, nowadays, in the technology age, every company has to focus significant amount of resources towards information technology as it became one of the most crucial aspects of contemporary business.

Correlations between factors were positive in both apps and mobile websites categories. This shows that quality increase in one factor has effect on quality among other factors as well. The findings were stronger among mobile websites. Reason for this might be that while mobile website usually provide the same information and functions across the field, no matter the company or industry types, apps tend to have different purposes. Some apps are solely

informational, some provide fun for users and focus on aesthetics, and some are purely focused on functionality (for example app for opening car, app to control TV). This variability within apps could explain the lower correlations between factors compared to mobile websites.

Region results presented interesting findings. While in website section Americas finished on top with considerable differences compared to Europe and Asia, in app section Americas finished last. The differences were however smaller than within mobile websites which again points to overall high quality of apps across the market. The reason for these results might again be the lack of representatives from regions, especially the app section lacked companies in Asia region as a result of omitting of considerable amount of companies.

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47 To conclude, results across apps and mobile websites showed that both categories have above the medium level standard of apps and mobile websites. The study provided data about the quality of apps and mobile websites of leading companies. It showed that these leaders seem to promote the companies’ image, attract users, and enhance public relations through good quality apps and mobile websites. This study represents an initial attempt to examine the situation of apps and mobile website within the market of information technology leaders.

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6. LIMITATIONS, FURTHER RESEARCH AND IMPLICATIONS

This study has several limitations. First, the data collection could be done by several collectors to enhance reliability. Although the scales are reliable even when completed by just one person, more than one collector is still advisable as it eliminates small deviances, should they occur. Second, larger number of companies evaluated would benefit the results. For example some industry types did not have enough members to incorporate them into industry analysis. Similarly Asian region had small number of companies. Higher number of companies, for example from list of Fortune 500, would produce stronger results.

Third, the research was conducted within limited period of time. Some websites or apps did not work or were inaccessible, which could have been just temporary issue. However, due to time constraints, these were omitted from results.

Fourth, due to time, as well as man power constraints, the data were collected using one smartphone. For more robust results several mobile devices could have been used with different operating systems, different performance, different price range, etc. to look at disparities between various mobile devices as well as between various apps provided for each operating system.

Future research could, besides addressing the limitations of this study, compare the results from different time periods to look if companies tend to enhance and change their apps or websites for better user experience or not.

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49 The study contributes to literature by examining topic that was not dealt with before. It is

unique and valuable in terms of providing overview of quality levels of apps and mobile websites of leading companies. The dynamic nature of the ‘app and mobile website industry’ provides possibilities for future research within this topic. It also provides practical implications as managers and companies can employ the findings of study to improve their app and mobile website strategies to enhance user experience. Study showed the strengths and shortcomings of contemporary apps and mobile websites, something that can be used by not just leading companies but their followers as well as the companies act as information technology leaders and it is possible that other large and evens small companies will follow suit in terms of app and mobile website usage.

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7. APPENDIX

7.1 Appendix A. MARS-1

The Rating scale assesses app quality on four dimensions. All items are rated on a 5-point scale from “1.Inadequate” to “5.Excellent”. Circle the number that most accurately represents the quality of the app component you are rating. Please use the descriptors provided for each response category.

SECTION A

Engagement – fun, interesting, customisable, interactive (e.g. sends alerts, messages, reminders, feedback, enables sharing), well-targeted to audience

1. Entertainment: Is the app fun/entertaining to use? Does it use any strategies to increase engagement through entertainment (e.g. through gamification)?

1 Dull, not fun or entertaining at all 2 Mostly boring

3 OK, fun enough to entertain user for a brief time (< 5 minutes)

4 Moderately fun and entertaining, would entertain user for some time (5- 10 minutes total)

5 Highly entertaining and fun, would stimulate repeat use

2. Interest: Is the app interesting to use? Does it use any strategies to increase engagement by presenting its content in an interesting way?

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51 1 Not interesting at all

2 Mostly uninteresting

3 OK, neither interesting nor uninteresting; would engage user for a brief time (< 5 minutes)

4 Moderately interesting; would engage user for some time (5-10 minutes total) 5 Very interesting, would engage user in repeat use

3. Customisation: Does it provide/retain all necessary settings/preferences for apps features (e.g. sound, content, notifications, etc.)?

1 Does not allow any customisation or requires setting to be input every time 2 Allows insufficient customisation limiting functions

3 Allows basic customisation to function adequately 4 Allows numerous options for customisation

5 Allows complete tailoring to the individual’s characteristics/preferences, retains all settings

4. Target group: Is the app content (visual information, language, design) appropriate for your target audience?

1 Completely inappropriate/unclear/confusing 2 Mostly inappropriate/unclear/confusing

Exploratory study on top 100 global brands’ mobile marketing effort through apps and mobile websites