Mobile Enterprise Applications - Current State and Future Directions

Andrea Giessmann

University of Neuchâtel, SAP Research Center St. Gallen

andrea.giessmann@sap.com

Katarina Stanoevska-Slabeva University of Neuchâtel University of St. Gallen

katarina.stanoevska@unine.ch

Bastiaan de Visser SAP Research Center St. Gallen

bastiaan.de.visser@sap.com

Abstract The 2nd generation of mobile applications (apps)

based on Smartphone's and tablet PCs are completely changing the way mobile software is developed, distributed and consumed. In particular mobile enterprise applications (MEA) have a disruptive effect on existing enterprise software and require rethinking in the software industry. Despite their impact, MEA have not been considered sufficiently in literature. This paper contributes to fill this gap by investigating more than 500 existing MEA, by providing an overview of typical characteristics of MEA, and extracting of both future directions in the MEA market and a related research agenda. 1. Introduction

Since Apple introduced the iPhone into the market in 2007, a 2nd generation of mobile apps emerged that is based on a new mobile ecosystem and considerably changes the way mobile applications are produced, distributed and consumed (see also [1-3]). The market for Smartphones and mobile apps has grown steadily since 2007. In particular mobile app stores, for instance the Apple App Store or Google Android Market, have a predicted three-digit growth rate of 677% in the period from 2009 to 2014 [4-7]. This would result in 1.7 million available mobile apps by 2014 [5-7]. The rapidly growing number of available mobile apps is also confirmed by the number of downloaded apps, which according to forecasts will rise from 10.9 trillion in 2010 to a total of 76.9 trillion in 2014 [5]. Encouraged by the strong growth of this market, more powerful and more user-friendly mobile devices are available [8]. Manufacturers of mobile devices rely on extensible platforms and provide corresponding app development environments and market places resulting in a new mobile app ecosystem (see also [1-3]). While, at the beginning, the new mobile ecosystems targeted the enormous masses of end consumers worldwide, all

these developments resulted in a growing interest for mobile apps for enterprises as well [9], [10]. [5] predicts that mobile enterprise applications (MEA) will play a much larger role in the coming years, which will in the future also be reflected in the functionality of the mobile devices, in security and device management.

According to [11], 90% of 250 IT managers have plans to develop new mobile apps within their company by the end of 2011. 44% of these managers even believe that they will develop between five and nineteen new MEA. However, only 55% of the interviewed managers have a long-term strategy for the use of mobile apps within their company. The study shows that there is a considerable interest in MEA and willingness to invest in these technologies. However, companies do not have a clear strategy on how to utilize MEA in the future as this technology is just emerging. The new demand for MEA by companies provides opportunities, particularly for enterprise software providers, but also new challenges.

At present there is a lack of detailed knowledge about the specific characteristics of the 2nd generation of MEA as well as about the current state and future opportunities of the MEA market. A classification scheme providing an “ordering of entities into classes on the basis of their similarities” [12] is needed, in order to distinguish and order currently available MEAs. A reliable analysis of the current state is in turn a prerequisite to identify future opportunities in the MEA market. The paper at hand contributes to fill this gap by considering the following two main research questions: (1) What are the major future opportunities for enterprise software providers to position in potential future MEA markets, and (2) what are the related future research challenges concerning the transition to the 2nd generation of MEA? In order to provide a basis on which to answer these research questions, first a classification schema for MEA is developed in order to provide a structured overview on the current state. Then, based on expert interviews, factors influencing future developments are assessed.

2012 45th Hawaii International Conference on System Sciences

978-0-7695-4525-7/12 $26.00 © 2012 IEEE

DOI 10.1109/HICSS.2012.435

1363

2. Background

This section sets the scene for the research presented within the paper at hand by providing definitions of the basic terms under consideration. These are in particular terms denoting the main components of the new mobile ecosystem [1-3]: MEA considered as a specific type of mobile apps in this paper, mobile devices or platforms, and mobile application stores.

In literature, the exact definition of MEA is still open to debate. Like enterprise applications or enterprise application software (EAS), MEA aim to be used by businesses [13], [14]. McAfee (2006) defines enterprise applications as “the type of IT application that companies adopt to restructure interactions among groups of employees or with business partners” [14]. MEA differ only slightly from enterprise applications. The main difference between MEA and enterprise applications is that MEA are designed specifically for use on mobile devices and wireless networks (see i.e. [15]). In summary, in analogy to the enterprise applications definition and [15], for the purpose of this paper MEA are defined as applications that are designed for and are operated on mobile devices and which facilitate business users within core and/or support processes of their enterprises (see also [16]). The concept of MEA is not new. It first emerged as a new vision in context of the evolution of the mobile communication channel from a pure voice to a voice and data communication channel around year 2000. Related terms, which were coined in literature at that time were m-business, mobile commerce, mobile payment and similar (see for example [15], [17-20]). In particular the term m-business summarizes the 1st generation of MEA, which were based on the early mobile devices and wireless networks capable of supporting data communication (for an overview and classification see [21]). They were typically tailored to the specific needs of a specific company and closely integrated with other enterprise applications. The 1st generation of MEA was also distributed on a license based basis. The features of the 1st generation MEA were restricted by the limited features and abilities of the early mobile devices and wireless networks [21]. The emergence of Mobile Internet and smart phones gave rise to a 2nd generation of MEA, which are based on wireless networks with higher bandwith, sophisticated mobile end devices and a new platform based production ecosystem. The main components of the new ecosystems are mobile devices as platforms

and mobile application stores as new distribution channels.

Mobile devices are considered as platforms, on which mobile apps can be operated [3]. In line with [22] and [23] we define a platform as “a set of subsystems and interfaces that form a common structure from which a stream of related products can be developed and produced efficiently.” In the case of platforms for mobile devices, a platform provider usually offers a standardized Application Programming Interface (API), a software development kit (SDK), as well as a development and testing environment to 3rd-party developers [1], [2]. Some providers also offer support for deployment and hosting of the resulting app as well as force the usage of their mobile application stores for app distribution.

Mobile application stores (mobile app stores) are new distribution channels for mobile apps. These kinds of marketplaces allow participants to be both consumers and providers [24]. Mobile app stores are usually operated by a mobile device manufacturer or by mobile operating system providers and do have the following commonalities [2]: (1.) They are accessible via the Internet and the distribution of the actual mobile apps takes place via a preinstalled application on the mobile device. (2.) Third party developers are provided with an SDK and have to pay a one-time or recurring fee for developing and distributing their apps over the mobile app stores. (3.) All mobile app stores allow developers to offer free as well as paid applications. (4.) Once a developer offers a paid application, the mobile app store provider gets a share of the revenue. 3. Methodology

To answer the research questions defined in the first chapter, a three-stage research approach was chosen:

Step 1: Classification scheme. In order to provide an overview of the current state of development of MEA, a classification scheme for MEA was developed, based on the classification methodology introduced by [25]. According to [25], a classification scheme is “a set of characteristics, which are suitable to classify objects of a specific application domain.” The five phases of the proposed classification methodology were applied as follows: (1.) Inception: The aim is the development of a classification scheme for MEA. The resulting classification scheme should provide a comprehensive, but abstract survey. (2.) Elaborate characteristics: A systematic qualitative literature

1364

research based on [21] and [22] was used to identify major characteristics of MEA in order to acquire a comprehensive set of potential characteristics. (3.) Specify classification scheme: The identified characteristics of MEA were structured by using a morphological matrix according to [28]. (4.) Test: The developed classification scheme was iteratively tested and improved by classing a total sample of 50 apps. Whereas, classing means, apps were assigned to classes that have been previously defined [12], [29]. (5.) Use and maintenance: The resulting characteristic-based classification schema was used to investigate more than 500 mobile apps within the second step of this study. The final classification scheme is presented within chapter 4.

Step 2: Quantitative Investigation. In order to provide a structured overview of the current state on the market for MEA, a bottom up market analysis of existing business apps was performed. In line with [30], the five phases of a market research approach were considered: (1.) Definition: Starting from a basic definition for enterprise software in general, a working definition for MEA was derived, and the object of study defined, see section 2. (2.) Design: The previously developed classification scheme for MEA was used to perform a structured data collection. (3.) Data Collection: The main research effort was dedicated to an empirical investigation of more than 500 existing apps, which were declared as business apps in existing mobile app stores. (4.) Data Analysis: The analysis of the collected data was realized by calculating the frequency distribution per characteristic as well as by using contingency tables in order to analyze relations between characteristics. (5.) Documentation: The results of the analysis are presented in section 4.2 and further discussed in chapter 6. The design of our quantitative investigation is presented in more detail in section 4.1.

Step 3: Qualitative Investigation. In order to answer the research questions “What are the future opportunities for MEA providers?” and “What are future research challenges related to the transition to the 2nd generation of MEA?” six qualitative, semi-structured expert interviews [31] were performed. The interviewed experts are characterized by having a good overview of the area under investigation and come from both research and industry. The interviewed experts have the following positions/roles: (1.) senior researcher in mobile user experience,

(2.) chief executive officer division mobile solutions, (3.) professor for mobile communication, (4.) director for mobile and wireless strategy, (5.) business development and pre-sales for mobile solutions and (6.) head of research department. All six experts have influence on management decisions as well as strategic IT issues as part of their position/role. Each interview lasted from 45 minutes up to an hour and has been recorded. The guidelines for these interviews contained four main subjects with a total of eighteen questions: The first subject established a common understanding for MEA. The second subject assessed the market situation. The third subject delineated factors of influence on future MEA trends. Finally, mobile app stores as a distribution channel for MEA were discussed.

Based on the results of the literature analyses, the classification scheme, the quantitative investigation and expert interviews, an overview of potential future trends for MEA as well as the future research agenda were derived. 4. Classification Scheme

Based on a structured literature research according to [13] and [23] a characteristic-based classification schema for MEA was developed. Characteristic-based classification means each classification object, in this case a mobile app, is characterized by several characteristics [25]. For the classification of MEA we propose five different characteristics: (1.) target group, (2.) price, (3.) functional area, (4.) connectivity and (5.) core business of application provider. Figure 1 illustrates the resulting classification scheme structured by using the morphological matrix according to [28].

In line with [32] the characteristic target group is partitioned into business to business end consumer (B2B) and business to private end consumer (B2C). B2B Apps facilitate mobile transactions within and

Figure 1. Initial classification scheme for mobile enterprise applications based on literature review

> 5 EuroPrice Free < 5 Euro

Functional Area

Data, Collaboration & Communication

Services

Information Services

Productivity Services

Target Group

Business to Business End Consumer (B2B)

Business to Private End Consumer(B2C)

Connectivity Standalone Smart Client Thin Client

Core Business of Application

Provider

Enterprise Application Software Mobile Applications Other Core Business

CRM Office ERP

1365

between organizations. These apps facilitate effective information flows and coordinate operations within and between enterprises. B2C Apps support transactions with individual customers, or consumers. They provide consumers automated services directly on mobile devices. For example, DHL a large parcel service, provides an app with which customers can track shipments and determine the estimated delivery time.

The characteristic price is used to answer the question, what pricing structures MEA have. We distinguish three characteristics: free, less than five euro and more than five euro. The characteristic is restricted to the acquisition price; ongoing costs are not included.

As proposed by [25] functional area is used as a third characteristic, in order to distinguish the functional areas being addressed by MEA. We identify five functional areas currently addressed by mobile enterprise applications: (1.) data, collaboration and communication services. Prominent examples of this area are applications for chat, e-mail or remote desktop, for instance., (2.) Information services, (3.) enterprise resource planning (ERP), (4.) customer relationship management (CRM), and (5.) office applications. The last three characteristics can particularly be summarized as productivity services. Productivity services means apps that facilitate business users within core and support processes of their companies.

The connectivity characteristic is used to answer the question of how strong the investigated mobile apps rely on communication with a server system. Based on [33] we distinguish between (1.) Standalone, meaning the apps do not need any connection in order to provide their full range of functionality. (2.) Smart or full clients, which do also have a wide range of functionality within the mobile app, since “the ability to operate in disconnected mode can be useful even when connectivity is available.” [33] (3.) Thin clients, which are not functioning without data connection and therefore are almost comparable with browsers.

The last characteristic describes the enterprise type, as proposed by [25]. Core business of application provider describes the background of a mobile app provider. The distinction is made as follows: (1.) Providers of (classical) enterprise applications which expand their product offering with mobile apps, (2.) Providers, whose core business is the development of mobile apps and (3.) others which includes all providers that do not belong to the first or second group.

5. Quantitative Investigation on Current State 5.1. Study Design

Our quantitative investigation is based on the five phases of the market research process, which was introduced in chapter 2, and has been operationalized as follows below:

Definition Phase: In analogy with the research questions, the object of study of the quantitative investigation is restricted to mobile apps, which are distributed via existing mobile app stores within the category "business". The following marketplaces were taken into account, since they represent the five biggest marketplaces for mobile apps: Apple App Store for iPhone and iPad, Google Android Market, BlackBerry App World, and Windows Phone Marketplace.

Design Phase: With the previous chapter, a classification schema for MEA was introduced. This schema served as a basis for the data collection. However, after classifying a sample of 50 apps it was necessary to add two more options due to the diversit of applications within the business categories. First, within the characteristic functional area entertainment services have been added. Entertainment services include games as well as applications for videos and music. Second, productivity services were supplemented with another option as more productivity services were identified that could not be assigned to ERP, ERM or office.

Data Collection Phase: The considered mobile app stores comprised, as of the end of 2010, approximately 500,000 mobile apps. The number of mobile apps, which were classified as business, was altogether 15,550 apps. In line with [30], we used a sample of 10% from smaller mobile app stores and a sample of 3% of large apps stores, since otherwise this large app stores would have dominated our investigation. As a result a total sample of 500 apps was determined. Table 1 shows the exact composition of the sample.

Mobile Application Store Category Business

Sample Size

Apple App Store for iPhone 10 072 302 Apple App Store for iPad 2 160 65 Google Android Market 631 63 BlackBerry App World 560 57 Windows Phone Marketplace 134 13

Total / Sample 15 557 500

Table 1. Size of the sample

1366

The samples from the marketplaces were taken randomly from the category business, while the apps have been ordered by ranking. The random selection was performed by using the Random Integer Set Generator of random.org. If an app was selected twice, another app was selected randomly, with the result, that each app was only classified once.

Data Analysis Phases: The analysis of the collected data was performed by calculating a frequency distribution as well as by using contingency table analysis. The results of the analysis are presented within section 4.2 and are discussed in chapter seven (Documentation Phase). 5.2. Results

The data analysis is divided into two parts: First, a univariate data analyses in terms of a frequency distribution was performed and within the second part a bivariate analyses was performed by using contingency table analysis [34].

Frequency distribution

For each characteristic, introduced within section 4, both the absolute frequency as well as the percentage distribution was calculated and summarized in table 2.

The characteristic target group recorded the type of mobile users, which are actually addressed by apps within the business categories of mobile app stores. Business end consumers are addressed by 273 apps out of the 500 apps and make therefore 54.6% of the target group, while 5.4%, respectively 27 apps, address both B2B and B2C. 40% Of the investigated apps address private end consumers. Examples for these applications are “Speak Italian,” “Job search” and “Save battery.”

Price: The analyses showed that 62%, respectively 310, of the investigated apps are for free. Only 63 apps (12.6%) do have a price higher than five Euro and 127 apps (25.4%) cost less than five Euro. Hence, the forecasted trend of [4] towards free mobile apps can be confirmed. However, it must be investigated whether the frequency distribution of the price changes are significant in combination with other characteristics.

Characteristic & Attribute Fre-quency

Percent

Target Group Private end consumer 200 40.0% Business end consumer 273 54.6% Private & Business end consumer 27 5.4% Price per Application Free 310 62.0% < 5 Euro 127 25.4%

> 5 Euro 63 12.6% Functional Area Data, Collaboration and Communication Services 51 10.2%

Entertainment Services 12 2.4% Information Services 231 46.2% Productivity Services 206 41.2% Connectivity Standalone 213 42.6% Smart Client 113 22.6% Thin Client 174 34.8% Core Business of Application Provider Enterprise Application Software 16 3.2% Mobile Applications 247 49.4% EAS & Mobile Applications 39 7.8% Others 198 39.6%

N = 500 applications

Table 2. Frequency distribution of characteristics

With regards to the characteristic functional area information services claim the largest shares with 231 apps (46.2%). 206 apps (41.2%) of the investigated apps have been identified as productivity services. The distribution of apps within the sub-groups of productivity services is as follows; Office: 75 apps (15%), CRM: 31 apps (6.2%), ERP: 48 apps (9.6%) and 52 apps (10.2%) within others. It is reasonable to assume that the target group does have an influence on functional area. However, this can only be shown by using a bivariate analysis method.

Concerning connectivity, standalone apps represent the largest part with 42.6% or 213 mobile apps. With respect to the fact that enterprise applications usually follow a client-server architecture model this result was unexpected. Also, against the background that mobile apps are characterized in particular by ubiquity and the utilization of mobile Internet, this result is surprising. This could indicate that connectivity in the context of MEA only plays a minor role or that connectivity is affected by another characteristic.

The last characteristic, core business of app providers is dominated by mobile app providers with 49.4% (247 apps). The share of EAS providers is unexpectedly low. Pure enterprise application providers do have a share of 3.2% and mixed providers gain 7.8%. Hence, it could be presumed that the market for MEA either is not relevant, not yet addressed by EAS providers or that mobile app stores are not an appropriate distribution channel for MEA.

Contingency of characteristics

In order to uncover conjunctions between MEA characteristics further bivariate data analyses were

1367

needed. In the following we have used contingency tables, which are characterized by their ease of use in nominal numbers. The representation of the resulting tables in this section was omitted in favor of readability. However, the results are present within the appendix of the paper at hand.

The result of the contingency table of the characteristics target group and functional area (see Table 3) is a completely different frequency distribution. While before information services have been the largest group with 46.2%, the division into B2C and B2B shows that this is only true for mobile apps, which address private end users (136 apps respectively 68%). When considering only B2B apps, it turns out that those apps cover productivity services with a frequency of 55,3%, while information services only have 29.3%. Another interesting result is the new frequency distribution within the sub-areas of productivity services. In particular, the sub-areas CRM and ERP are only represented with the B2B target group (11,4% and 48%) and are not represented at all within the B2C target group. The larger number of apps addressing B2C strongly influences the analyses. As a consequence, in the following analysis only the 273 apps that actually address business end consumers are considered.

As a result of the contingency table between functional area and price (see Table 4), the trend to free applications as suggested by [4] and [35] be confirmed. Only the office and other applications of productivity services sub-areas have more paid apps than free apps. However, this could be explained by the fact that both office applications as well as other productivity services do not rely on specific backend systems. Hence, providers must either finance themselves via the app price or by advertising or a combination of both. Apps belonging to the CRM and ERP group in contrast, are mostly free. The reason for this could be that providers of these mobile apps already earn license fees for an ERP or CRM system.

The contingency table of MEA between functional area and connectivity is presented in Table 5. The analyses shows in contrast to the previously preformed univariate analysis that standalone apps and smart clients are almost equally represented (35,5% and 36,3%) for B2B apps. Smart clients are used in particular in the functional areas of Communication Services, ERP and CRM. This is hardly surprising, especially since communication services such as email and chat applications as well as ERP and CRM applications, usually rely on client-server architecture. Hence, MEA are just a new kind of client. Noticeable are the results for office applications, which are

typically implemented as standalone apps (42%). This also makes sense since office applications usually do not rely on backend systems. Thin clients on the other hand are mainly used to implement information services (51.3%).

Table 6 shows the contingency table between functional area and core business of app provider, whereby only apps are taken into account that address B2B. In contrast to Table 2 the percentage of EAS providers together with enterprise applications & mobile app providers has significantly increased, as the target group was limited to B2B. The result shows that EAS providers usually address MEA for ERP and CRM solutions, while pure mobile app providers focus on office applications as well as data, collaboration and communication services. Information services are particularly likely to be provided by other producers. One reason for this phenomenon might be that many companies use mobile apps for marketing purposes. Providers of marketing mobile apps do not belong either in the class of the EAS providers or in the class of mobile apps providers. 6. Qualitative Investigation on Future Directions

To fill the gap between the current state and the next evolutionary steps of the MEA market, interviews with six experts have been conducted. The following four themes have been discussed within the interviews: (1.) Establishing a common understanding for MEA, (2.) Assessing the market situation, (3.) Factors of influence on future MEA trends and (4.) mobile app stores as a distribution channel for MEA. In the following the results of the qualitative investigation are presented.

Mobile Enterprise Applications

In order to validate the definition for MEA, which was derived from literature and presented within chapter 3, the term has been discussed with the interviewed experts. All experts agreed with the given definition and added that MEA should create added value for a company by realizing an increase in productivity and/or reduction of expenses. They also stated that MEA should integrate into existing IT landscapes of companies and must fulfill high security standards.

Assessing the Market Situation

The experts describe the current market for MEA as small and heterogeneous. The greatest potential of

1368

MEA is currently seen in the support of field staff. For example, for service and maintenance, sales and mobile access to CRM and ERP systems. Whereby, several experts mentioned health care as a promising industry for mobile enterprise applications.

The experts also agreed that mobile apps, which integrate customers into a business process (B2C), will play a significant role in the future. By integrating customers via mobile apps into the business process media breaks are avoided and thus increased productivity is achieved. Prominent examples of this are mobile apps of insurance companies that enable customers to file a claim directly via their mobile app. This might lead to a process improvement on the one hand as well as to increased customer satisfaction on the other hand.

Experts expect, in particular, classical providers of enterprise applications to respond to the growing MEA market since they have specific knowledge on business processes as well as a large base of customers. According to the experts there are two ways EAS providers might react: (1.) EAS providers begin to develop mobile apps themselves, and closely integrate them with their existing enterprise solutions. (2.) They provide platforms with well-defined interfaces that allow other mobile app providers to develop MEA that are integrated with their solutions, see also [36].

With regards to further evolution of the MEA market experts agree, this must be discussed in two parts. On the one hand they see the market for general MEA covered by 1st generation MEA. They state, the first generation of MEA was already introduced in 2003/2004 and was realized by Personal Digital Assistants and PocketPCs [17-19]. The hype towards Smartphones might initiate a transition from 1st generation end devices to Smartphones. Furthermore, the experts see a high potential in very specialized MEA that address specific niche application areas However, according to experts these kinds of apps do not represent a new market, but rather, new 2nd generation MEA. Nowadays, by leveraging the new powerful mobile devices as well as the associated platforms it will be possible to develop and provide MEA even for very specific use cases.

Factors of Influence

According to experts, the evolution of mobile devices and applications on the consumer market is the main influencing factor acting on the market for MEA. This statement is justified by the fact that employees are also private end consumers. Consequently, their experiences and requirements of mobile devices and apps from the private end consumer’s perspective are

transferred to the corporate environment. The hype in mobile devices and apps in recent years already led to more powerful devices coming on the market and consequently, a wide range of apps [37]. The experts are confident that this trend will continue and will also continue to have a strong influence on MEA in the future.

Another influential factor identified by the experts is the capability and ubiquity of the mobile internet. However, this factor has become less important, since in the past, the operators have steadily expanded the networks and the use of mobile internet experienced a price decline. Nevertheless, the experts see the access and the bandwidth of the mobile internet as a basis for MEA.

In addition to this, all experts mentioned data security on mobile devices but also within the mobile apps as an important factor. In particular, MEA that access the IT infrastructures of companies must meet high security standards since sensitive corporate information can be exchanged. Currently, this factor is only partially fulfilled, according to the experts.

Mobile Application Stores

In principle, the experts agree that the concept of mobile app stores is a valid distribution channel for MEA. However, during the interviews, it became clear that three types of mobile app stores must be distinguished.

First, publicly accessible mobile app stores. These stores are deemed appropriate for companies if there is a possibility to restrict the selection of downloadable mobile apps for their employees. The reason for this constraint is that the apps must be first tested and approved in terms of security and compatibility.

Second, mobile app stores provided by enterprise application software providers. These stores contain MEA that are designed and integrated in line with the existing product portfolio of the EAS provider and can be developed by both the EAS provider as well as a third-party. According to experts, in this way quality and compatibility would be ensured and niche markets can be addressed much more effectively.

In the third case companies operate their own mobile app stores in order to distribute MEA to their employees, customers and partners; so called in-house or corporate mobile app stores. However, this approach tends to be worthwhile only for major corporations, according to the experts.

7. Discussion

1369

The results of the quantitative analysis show that there is already a substantial number of 2nd generation MEA on the market. The combined results of the quantitative and qualitative research furthermore show that it can be expected that the pressure on providers of enterprise applications to extend and enhance their offerings with MEA will grow in the future. Besides extension of existing enterprise applications with MEA, the coverage of new niche application areas is another factor that will push demand for MEA. This development trend results in technical and business challenges for EAS providers.

From a technical perspective, the biggest challenge is, on the one hand, the transfer of existing 1st generation MEA to the 2nd generation of MEA. Another challenge is the adjustment of existing enterprise applications for mobile extension with MEA. MEA require a modular approach as apps typically are built around dedicated tasks and should provide fast and convenient access to data and functionality. Thus, an important question of technical design is the number and scope of APIs that are needed in order to adjust existing monolith enterprise applications with MEA. Further challenges are related to the security requirements towards 2nd generation MEA.

The business challenges can be summarized as follows: (1.) Assessment of the customer and business value of potential new MEA particularly in niche areas. (2.) Development of decision criteria for choice of the different options for development and distribution of MEA. (3.) Development of new business models, typically n-sided business models (see [1] and [2]), for distribution and sales of MEA that consider also other players in the new mobile app ecosystem. In particular, these are mobile platform and mobile app store providers as well as independent developers.

The challenges from the perspective of EAS providers are also the basis for future research directions in this area. They can be summarized as follows: (1.) Research in user and market demand for MEA, (2.) Assessment of user acceptance of different kinds of MEA in terms of scope, functionality and usability. In close relation to this development of best practices of good MEA design from a user and technical perspective. (3.) Development of reference models for adjustment of existing monolith architectures of enterprise applications to modular APIs and MEA. (4.) Based on experiences with 1st generation MEA and knowledge about features of 2nd generation MEA assessment of security risks and development of security solutions. (5.) Analysis of business models and development of generic business

models from the perspective of EAS providers that are embedded in the new MEA ecosystem and consider innovative distribution based on different types of mobile app stores and revenue sharing opportunities with independent developers. These new research directions are to a certain extent similar to research directions that can be found in literature for 1st generation MEA [15]. However, in particular research questions related to user acceptance, design and innovative business model are new due to changes in the MEA ecosystem [15] (see for example for comparison with 1st generation mobile value chains also [20]). 8. Conclusion and Outlook

The main goal of the research presented in this paper was, based on a newly developed classification scheme as well as quantitative and qualitative market and trend analysis, the investigation of the current MEA market from the perspective of EAS providers. Another goal was to derive a research agenda for MEA based on the research findings. The assessment of future development was based on a quantitative analysis of 500 existing MEA and on qualitative expert interviews. The findings of the two sided analysis reveal that in the future a strong demand towards 2nd generation MEA can be expected. This will require providers of enterprise software to consider transfer of 1st generation MEA to 2nd generation MEA and identification of new niche application areas for MEA. Furthermore, this requires adjustment of existing enterprise software as well as development and establishment of innovative business models grounded in the new ecosystem for MEA.

The identified challenges of enterprise application providers were the basis for the identification of future research directions and challenges. 12. References [1] V. Gonçalves and P. Ballon, “Adding value to the network: Mobile operators’ experiments with Software-as-a-Service and Platform-as-a-Service models,” Telematics and Informatics, vol. 28, no. 1, pp. 12-21, 2011. [2] K. Stanoevska and T. Wozniak, “Opportunities and Threats by Mobile Platforms : The ( New ) Role of Mobile Network Operators,” in 14th Int. Conf. Intelligence in Next Generation Networks (ICIN) (Berlin), 2010, no. 1995, p. 6. [3] M. Kenney and B. Pon, “Structuring the Smartphone Industry: Is the Mobile Internet OS Platform the Key?,” Journal of Industry, Competition and Trade, vol. 11, no. 3, pp. 239-261, Jun. 2011.

1370

[4] Distimo, Distimo Report - Full Year 2010. Utrecht, Netherlands: , 2011, pp. 1 - 28. [5] IDC, Market Analysis - Worldwide and U.S. Mobile Applications, Storefronts, and Developer 2010 – 2014 Forecast and Year-End 2010 Vendor Shares: The “Appification” of Everything, no. December. Framingham, USA: , 2010, pp. 1 - 48. [6] Frost & Sullivan, An Insight into the U.S. Smartphone Application Storefront Market. Palo Alto, USA: , 2010, pp. 1 - 57. [7] Canalys, Google ’ s Android becomes the world ’ s leading smart phone platform - Canalys reveals smart phone market exceeded 100 million units in Q4 2010, no. January. Palo Alto, Singapore and Reading (UK): , 2011, pp. 1-4. [8] I. D. Constantiou, J. Damsgaard, and L. Knutsen, “The four incremental steps toward advanced mobile service adoption - Exploring mobile device user adoption patterns and market segmentation.,” Communications of the ACM, vol. 50, no. 6, pp. 51 - 55, 2007. [9] Gartner, Gartner Identifies the Top 10 Strategic Technologies for 2010. Stamford, Connecticut, USA: , 2009, pp. 1 - 3. [10] Gartner, Gartner Identifies the Top 10 Strategic Technologies for 2011. Stamford, Connecticut, USA: , 2010, pp. 2 - 4. [11] Sybase, Sybase Survey Finds Mobile Enterprise Apps Poised To Take Off In 2011. Dublin, California, USA: , 2011, pp. 1 - 2. [12] K. D. Bailey, Typologies and Taxonomies: An Introduction to Classification Techniques. Thousand Oaks, California: Sage Publications, Inc, 1994, p. 96. [13] J. Sutherland and W.-J. van den Heuvel, “Enterprise Application Integration and Complex Adaptive Systems,” Communications of the ACM, vol. 45, no. 10, pp. 59-64, 2002. [14] A. Mcafee, “Mastering the Three Worlds of Information Technology,” Harvard Business Review, no. November, pp. 141 - 149, 2006. [15] E. Scornavacca, S. J. Barnes, and S. L. Huff, “Mobile Business Research Published in 2000-2004: Emergence , Current Status, and Future Opportunities,” Communications of the Association for Information Systems, vol. 17, no. 1, pp. 635 - 646, 2006. [16] F. F.-H. Nah, K. Siau, and H. Sheng, “The value of mobile applications: a utility company study,” Communications of the ACM, vol. 48, no. 2, pp. 85-90, 2005. [17] Y. Yuan and W. Zheng, “From Stationary Work Support to Mobile Work Support: A Theoretical Framework,” in Proceedings of the International Conference on Mobile Business (ICMB’05), 2005, pp. 315-321. [18] R. Malladi and D. P. Agrawal, “Current and Future Applications of Mobile and Wireless Networks,” Communications of the ACM, vol. 45, no. 10, pp. 144 - 146, Oct. 2002. [19] S. Sarker and J. D. Wells, “Understanding mobile handheld device use and adoption,” Communications of the ACM, vol. 46, no. 12, pp. 35-40, 2003.

[20] S. Barnes, “The mobile commerce value chain: analysis and future developments,” International Journal of Information Management, vol. 22, no. 2, pp. 91-108, Apr. 2002. [21] K. Stanoevska-Slabeva, “Mobile Business-The New Frontier of the Digital Economy,” in The Digital Economy- Anspruch und Wirklichkeit, K. Stanoevska-Slabeva, Ed. Berlin, Heidelberg, New York: Springer Berlin / Heidelberg, 2004, pp. 459 - 476. [22] M. McGrath, Product Strategy for High Technology Companies - Accelerating your Business to web Speed, 2nd ed. New York, USA: McGraw-Hill, 1995, p. 400. [23] J. I. M. Halman, A. P. Hofer, and W. van Vuuren, “Platform-Driven Development of Product Families: Linking Theory with Practice,” Journal of Product Innovation Management, vol. 20, no. 2, pp. 149-162, 2003. [24] R. Copeland, “Telco App Stores - Friend or Foe ?,” in Proceedings of the 14th International Conference on Intelligence in Next Generation Networks (ICIN), 2010, pp. 1 - 7. [25] P. Fettke and P. Loos, “Classification of reference models: a methodology and its application,” Information Systems and e-Business Management, vol. 1, no. 1, pp. 35-53, Jan. 2003. [26] J. Webster and R. T. Watson, “Analyzing the Past to Prepare for the Future : Writing a Literature Review,” MIS Quarterly, vol. 26, no. 2, p. xiii - xxiii, 2002. [27] Y. Levy and T. J. Ellis, “A Systems Approach to Conduct an Effective Literature Review in Support of Information Systems Research,” Informing Science Journal, vol. 9, pp. 181-212, 2006. [28] F. Zwicky, Discovery, invention, research through the morphological approach. New York, USA: Macmillan, 1969, p. 276. [29] A. Marradi, “Classification, typology, taxonomy,” Quality and Quantity, vol. 24, no. 2, pp. 129-157, May. 1990. [30] P. Hague and P. Jackson, Market Research: A Guide to Planning, Methodology and Evaluation, 3rd ed. London: Kogan Page Business Books, 2002, p. 278. [31] J. W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 3rd ed. Los Angeles: Sage Publications, Inc., 2009. [32] K. Siau and Z. Shen, “Mobile communications and mobile services,” International Journal of Mobile Communications, vol. 1, no. 2, pp. 3-14, 2003. [33] J. Jing, A. Helal, and A. Elmagarmid, “Client-server computing in mobile environments,” ACM Computing Surveys, vol. 31, no. 2, pp. 117 -157, 1999. [34] G. A. Ferguson, Statistical analysis in psychology and education. New York: McGraw-Hill, 1966, p. 446. [35] Appcelerator & IDC, Q1 2011 Mobile Developer Report. 2011, pp. 1-17. [36] S. Elop and S. Ballmer, “Open Letter from CEO Stephen Elop , Nokia and CEO Steve Ballmer , Microsoft,” no. 2011. Nokia Conversations - The Offical Nokia Blog, p. 1, 2011. [37] IDC, Mobile Phone Market Grows 17.9% in Fourth Quarter, According to IDC, no. 5. Framingham, USA: , 2011, pp. 1 - 3.

1371

13. Appendix

Functional Area

Target Group

Data, Coll. & Comm.

Enter tainment

Infor- mation

Productivity Services Total Office CRM ERP Others

Private consumer

∑ 6 11 136 17 0 0 30 200 % 3.0% 5.5% 68.0% 8.5% .0% .0% 15.0% 100%

Business consumer

∑ 41 1 80 53 31 48 19 273 % 15.0% .4% 29.3% 19.4% 11.4% 17.6% 7.0% 100%

Privat & Business

∑ 4 0 15 5 0 0 3 27 % 14.8% .0% 55.6% 18.5% .0% .0% 11.1% 100%

Total 51 12 231 75 31 48 52 500 10.% 2.4% 46.% 15.% 6.2% 9.6% 10.% 100.0%

Table 3. Contingency Table: Target Group and Functional Area

Connectivity Functional Area Free < 5 Euro > 5 Euro Total

Data, Collaboration and Communication Services 25 (61.0%) 8 (19.5%) 8 (19.5%) 41 (100.0%)

Entertainment Services 1 (100.0%) 0 (0.0%) 0 (0.0%) 1 (100.0%) Information Services 59 (73.8%) 15 (18.8%) 6 (7.5%) 80 (100.0%)

Productivity Services

Office 23 (43.4%) 21 (39.6%) 9 (17.0%) 53 (100.0%) CRM 21 (67.7%) 4 (12.9%) 6 (19.4%) 31 (100.0%) ERP 36 (75.0%) 4 (8.3%) 8 (16.7%) 48 (100.0%) Others 7 (36.8%) 4 (21.1%) 8 (42.1%) 19 (100.0%)

Total 172 (63.0%) 56 (20.5%) 45 (16.5%) 273 (100.0%)

Table 4. Contingency Table: Functional Area and Price, limited to Business End Consumers

Connectivity Functional Area Standalone Smart Client Thin Client Total

Data, Collaboration and Communication Services 4 (9.8%) 26 (63.4%) 11 (26.8%) 41 (100.0%)

Entertainment Services 0 (0.0%) 0 (0.0%) 1 (100.0%) 1 (100.0%) Information Services 32 (40.0%) 7 (8.8%) 41 (51.3%) 80 (100.0%)

Productivity Services

Office 42 (79.0%) 9 (17.0%) 2 (3.8%) 53 (100.0%) CRM 5 (16.1%) 22 (71.0%) 4 (12.9%) 31 (100.0%) ERP 4 (8.3%) 30 (62.5%) 14 (29.2%) 48 (100.0%) Others 10 (52.6%) 5 (26.3%) 4 (21.1%) 19 (100.0%)

Total 97 (35.5%) 99 (36.3%) 77 (28.2%) 273 (100.0%)

Table 5. Contingency Table: Functional Area and Connectivity, limited to Business End Consumers

Connectivity Functional Area

EAS Provider

Mobile App Provider

EAS& Mobile App Others Total

Data, Collaboration and Communication Services 2 (4.9%) 34 (82.9%) 3 (7.3) 2 (4.9%) 41 (100.0%)

Entertainment Services 0 (0.0%) 1 (100.0%) 0 (0.00%) 0 (0.0%) 1 (100.0%) Information Services 2 (2.5%) 14 (17.5%) 0 (0.0%) 64 (80.0%) 80 (100.0%)

Productivity Services

Office 1 (1.9%) 50 (94.3%) 2 (3.8%) 0 (0.0%) 53 (100.0%) CRM 3 (9.7%) 15 (48.4%) 12 (38.7%) 1 (3.2%) 31 (100.0%) ERP 8 (16.7%) 18 (37.5%) 20 (41.7%) 2 (4.2%) 48 (100.0%) Others 0 (0.0%) 17 (89.5%) 2 (10.5%) 0 (0.0%) 19 (100.0%)

Total 16 (5.9%) 149 (54.6%) 29 (14.3%) 69 (25.3%) 273 (100.0%)

Table 6. Contingency Table: Functional Area and Core Business of Provider, limited to Business End Consumers

1372