information technology and business practices in ...

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CHAPTER 5

INFORMATION TECHNOLOGY AND BUSINESS PRACTICES IN GERMANY: RESULTS FROM THE 2011 BIT SURVEY Till J. Winkler, Christoph Goebel, Francis Bidault and Oliver Günther

Summary Information Technology (IT) plays a fundamental role in the global economy. Our continuous analysis of firm-level data helps decision makers to better understand IT use in companies and its business applications. This article reports on the findings from the 2011 Business and Information Technology (BIT) project in Germany which surveyed 220 companies across all major industries. We present the descriptive results of this survey structured by a framework of seven major topic areas on IT use in the firm. The key findings show that after the 2008/09 economic crisis, budgets for IT are growing again, strongest in the area of IT security. Business applications such as ERP and Groupware have become established products, while Business Process Modeling, Instant Messaging, and Social Web are still on the rise. Regarding the impact of IT, workforce is becoming more mobile, which increases the need for IT skills especially on the executive level. However, organization structures are hardly affected. Particularly, neither IT outsourcing nor the business units’ IT enablement, substantially diminish the importance of the IT function. Yet, IT outsourcing has become a popular option for several IT-related processes. Business process outsourcing (BPO) still remains on a moderate level. Regarding the company’s ecosystem, IT is mainly used to support operational processes. On supplier side, this refers to procurement management and execution, as opposed to planning and relationship management processes which are hardly penetrated by IT. On the customer side, IT supports tracking and fulfillment, while call centers and sales calls remain to be less automated activities. Analytical technologies such as demand forecasting and customer profiling are primarily used to better understand customer buying behavior. Online channels facilitate client relationship management by providing customer self-service and improved data collection methods. However, online business notably differs from traditional one by a more competitive pricing and lower revenues.

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Finally, from an overall results perspective we observe that IT positively affects a number of key performance indicators such as revenue and market share, while reducing production costs, time to market and, most prominently, internal communication costs. The results provide an interesting picture that helps to understand the broad impact of IT use on current business practices. 5.1. Introduction 5.1.1. Motivation IT plays a fundamental role in today’s globalized information economy. Companies rely on IT to support all major business processes as well as to communicate with their clients, business partners and employees. IT has become a major source of business innovation which helps to increase competitiveness in the global economy. A recent study from Germany indicates that at least 25% of business innovations between 1999 and 2007 are the result of novel information technology (BITKOM, 2011). In order to deploy IT, companies increasingly source software applications, hardware technologies and corresponding services from the market. This trend is also reflected in an increasing demand for IT services. It is expected that by 2030, the German IT sector will almost double in terms of revenues and employees and eventually reach an equal size as the automotive and manufacturing industries (BITKOM, 2010). Thus, IT has already become a production factor sine qua non on the firm level, and will also continue to become one on the industry level. Whereas the general importance of IT for a prospering economy is undisputed, on the firm level we often do not have a clear and comprehensive understanding of how IT use looks like. Most information technology studies either focus on the macroeconomic level, or investigate the adoption of a certain type of information technology (for example Enterprise Resource Planning, Supply Chain Management, or Customer Relationship Management). The goal of the BIT Survey is to investigate the use and effect of a wide range of information and communication technologies on businesses worldwide. It thereby combines the technology perspective (which technologies are implemented) with the business perspective (what is the impact for the organization). This article reports on the key findings from the 2011 BIT project in Germany which surveyed a representative sample of 220 large firms across all industries. The results convey a detailed picture on the impact of IT on business practices covering all major functions of a firm.

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5.1.2. Evaluation framework To structure the topic areas that have been surveyed, we use a framework which has been inspired by the standard value chain model by Porter (1985) and helps to explain the structure of the global BIT survey (see Fig. 5.1). The main idea of the Porter model is that each firm is part of a value chain which transforms inputs from the business partners (left) to outputs for their customers (right). This transformation is part of the core process of a firm and creates value. To support value creation, certain secondary processes are required, out of which information technology management is in the focus of this research. Thus, IT may support all intermediate steps in value creation. The evaluation framework comprises seven topic areas, each of which will be presented in a separate section. Section 5.3.1 examines the trends which determine the current IT budget in firms. IT budget trends reflect the investment priorities of the firm which also depend on the economic performance and outlook. Empirical research shows that not only IT investment can lead to improved business performance (given appropriate changes in complementary resources), but also high performers tend to invest more into IT (Aral et al., 2006). In Section 5.3.2, we analyze the effect that these IT investments have on technology adoption. IT in the company comprises a whole bundle of technologies ranging from hardware infrastructure to highly specialized software systems. The theory of innovation diffusion (Rogers, 1962) suggests that the adoption of new technologies follows an s-shaped curve. Thus, it is worth taking a closer look which technologies are currently on the horizon, as those might soon turn to mass use and be commoditized. Section 5.3.3 refers to workplace and organization and investigates the longterm impact of technology adoption on the organization. It comprises an individual

Fig. 5.1 Evaluation framework.

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perspective (i.e. impact on the employees work practices) as well as a structural perspective (i.e. impact on the organization structure). The main question regarding the former is whether IT use leads to an increased mobility and dispersion of the workforce. Regarding the latter, we explore whether the borders between business and IT, as well as between IT and the external service providers, are shifted. Section 5.3.4 is entirely dedicated to the topic of IT and BPO and aims to provide a detailed analysis on the processes that companies source from external parties. While IT outsourcing (ITO) refers to pure IT activities such as data center operations, software development or network management, BPO refers to entire processes such as finance, payroll, or customer support. BPO usually requires high IT involvement as information systems represent the main interface to connect external with internal activities within these processes. Up to now, the framework focused on the internal organization and outsourcing. The following two topic areas refer to the ecosystem of the firm. Section 5.3.5 is on trading partners, it examines how business practices related to supplier and procurement management are supported by IT. Here, we further divide between the strategic, tactical, and operational level into planning, procurement, and processing activities. Section 5.3.6 subsequently describes practices regarding the customer facing ecosystem. It is further divided into e-commerce and customer relationship management (CRM) practices. CRM refers to the use of technology to support interaction with the customers while e-commerce focuses on the online channel, only. Of course, certain CRM practices will be easier to implement via the online channel. This correlation is also of particular interest for the investigation. Finally, in Sec. 5.3.7, we conclude the analysis by taking a view on the overall business results of IT use. In particular, we discuss the impact of IT on a number of performance indicators. This chapter correlates with the first section on IT budget trends in as much as we observe the tangible results of IT investment reflected in business performance. Apart from the global BIT framework, a further area regarding the focus topic of IT industrialization was a mandatory part of the 2011 survey. The results of that section are subject to further evaluation and therefore not presented in this article (see Future Work).

5.2. Methodology 5.2.1. Data selection and acquisition Data for the analysis stems from a representative survey among German businesses. The corresponding addresses were purchased from one of the leading

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publishers of company information in Germany. We have chosen this particular database because it provides not only comprehensive firm information but also the opportunity to address the head of IT directly by name. We restricted the survey to the heads of IT in large private sector organizations. The Germany Institute for Research on Small and Medium Enterprises defines a large company as an organization with more than 50 million Euro of revenues and more than 500 employees (If M, 2002). A major reason for this restriction was that some of the questions in the global BIT survey (e.g. those on globalization, e-commerce, governance, and organization) are more likely to apply to large businesses. Industry selection was based on the 2008 edition of the German Classification of Economic Activities (WZ, 2008). All industry sectors except Public Administration, Defence, Social Insurance (84), Education (85), Homes and Institutions (87–88), Private Households (97), and Extra-Territorial Bodies (99) were included. A query to the database yielded 3,285 results, which were subsequently extracted. The obtained contact data was cleaned and corrected. Particularly, in order to avoid duplicate contacts, multiple entries for the same company were eliminated. Missing addresses and forms for addressing the participants were completed and rectified. After the process of data cleaning was completed, we kept 2,886 contacts to be addressed in a mailing. As we considered this number appropriate to achieve the desired sample size (n > 200), no further random selection or sorting was performed. Subjects were addressed in a formal mailing containing a cover letter, a paper version of the questionnaire and a return envelope. Information regarding the survey was complemented by a reference to the web page of the German BIT project. Answers could be given either paper-based or online. The survey contained standard BIT questions as well as questions regarding the focus topic and amounted to 10 pages in length in an A4-booklet format. However, for reasons of convenience, only the focus part has been declared as mandatory. The overall survey was tested to take approximately 45 minutes to complete. As an incentive, a small gadget to charge a mobile phone (worth 5 Euro) was offered to the first 100 respondents. Further, participants could enter a lottery for a modern tablet computer (worth 500 Euro). Therefore participants had to leave their contact details at the end of the questionnaire, as the survey itself did not contain any such information. Invitations to participate were sent out in mid April 2011, and the survey was closed in the end of May 2011 after a two-week extension. Two reminders were sent to the corporate email addresses of the companies, the first after two weeks and the second (final) reminder just before the extension period. Those companies that could not be reminded via email (and had not replied by then) were contacted over the phone. Out of these 1,018 reminder calls, 257 (25%) of the

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1500

Delivered 1730

1000 Sent 2886

Not delivered 930

500

Not reached 263 Not interested 247

Will respond 75

Not interested 90 Canceled 195 Usable response 220

Reminder calls (1018)

Reactions (534)

Send email 433 No email 0 Invitations (2886)

Reminder emails (2660)

Unknown recipient 29 Online anonymous 26 Online 105 Letter 89 Responses (220)

Fig. 5.2 Survey reminders and response distribution.

companies could not be reached after several trials. Another 247 (24%) of the companies stated that they were not able to participate due to time constraints or corporate policy. The majority of 429 (42%) companies agreed to accept a formal reminder via email, 73 (7%) stated that they are planning to respond to the survey (see Fig. 5.2). The total number of companies that reacted to our requests somehow amounted to 534 companies (19%). A total of 29 of these companies returned the letter due to a recipient that is unknown or has left the company. Even 90 of the companies stated via email, letter or fax, that they were not willing or able to participate in the survey. After data cleaning, we counted 195 (6.8%) data sets from subjects that started the survey online but canceled. The remaining 220 usable responses represent an overall response rate of 7.6% (see Fig. 5.2). Out of these usable responses, 89 (40%) came in as paper-based questionnaires which were subsequently transferred to the online tool for the analysis. A total of 131 (60%) of the respondents directly used the online survey, out of whom 26 answered anonymously (see Fig. 5.2). Data cleaning was an important task to differentiate between anonymous and incomplete answers. We only kept answers from respondents who at least completed the mandatory part of the questionnaire with a low number of missing values. A total of 75% of the remaining datasets have less or equal than three (q3 = 3)

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missing values (median m = 1). Most of these missing values referred to fields with supposedly sensitive or unknown information which were occasionally left blank, such as IT budget or company performance. 5.2.2. Sample characteristics 5.2.2.1. Respondent profiles As intended, the large majority of the respondents were high-level IT decisionmakers with significant work experience. The median working time of respondents in their current company is 11.5 years. We surveyed the position of the respondent on a horizontal (business/IT) and a vertical dimension (top/senior/ manager/staff level). An additional text field regarding the job position allowed for a further validity check. It revealed that many respondents rated themselves to pertain to the second or third level, even if they actually hold the highest position in IT (e.g. Head of IT). Also, there were 11 cases where the respondent left this field blank, for example business executives responsible for IT (e.g. the Chief Financial Officer), or staff pertaining to an IT unit within the business organization (e.g. Demand Manager). For the purpose of evaluation, these cases have been attributed to either side based on the textual job descriptions. The distribution of work years and job positions are depicted in Fig. 5.3. 5.2.2.2. Industry characteristics The respondents’ industries represent a good sample of the German private sector. Based on the first two digits of the Germany industry classification code (WZ, 2008), we clustered the invited companies into 18 industries which had a relative 50

Senior Business Management Business 3% Manager 3%

45 40 35 30

IT staff member 3%

25 20

Chief Executive Officer 4% Top-level Information Officer 20%

IT Manager 20%

15 10

Senior IT Manager 47%

5 0 35

Fig. 5.3 Respondent work years and position.

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T.J. Winkler et al. Table 5.1. Industry classification. Classification code range 01; 10–11

Industry Food and Agriculture

13–15

Textile

19–21

Chemicals and Pharma

24–25

Metal and Raw Materials

26–27

Electronics and High-tech

28

Mechanical Engineering

29–30

Automotive

35–38

Energy and Utilities

41–43; 68; 80–81

Construction and Real Estate

45–47

Retail Trade

49–53

Transportation and Traffic

55–56; 79

Tourism and Gastronomy

58–63

Telecommunication and Media

64

Banking

65–66

Insurance

69–78

Professional Services

86

Health Care

All other

Other industry

frequency between 1% and 12% in our primary database (see Table 5.1). The participants were asked to classify their company based on this list. Figure 5.4 shows the relative industry distribution of invited and respondent companies. Some deviations are notable, for example for Construction and Real Estate (positive) and Retail Trade (negative deviation). So one may conclude that these industries are rather over- or under-represented in the sample. However, testing the observed and the expected industry frequency distribution by a chi-square test (χ2 = 35.86; df = 18; p < 0.01) does not reject the null hypothesis that the observed sample is consistent with the industry distribution from the database. This speaks in favor of an absence of a nonresponse bias regarding certain industries in the sample. Based on three bipolar scales, we could further classify the companies according to their main business activities (Figs. 5.5, 5.6, 5.7). We see that 51% of respondents are from manufacturing and 41% from service industries. The 8% who do not attribute themselves to either category, stem from companies who clearly have mixed activities, for example utilities who produce and distribute energy (see Fig. 5.5).

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Invitations in % (n=2886) Responses in % (n=220)

Automotive Banking Chemicals & Pharma Construction & Real Estate Electronics & High-tech Energy & Utilities Food & Agriculture Health Care Insurance Logistics & Transportation Mechanical Engineering Metal & Raw Materials Professional Services Retail Trade Telecommunication & Media Textile Tourism & Gastronomy Other 0.0

5.0

10.0

15.0

Fig. 5.4 Respondent industries. 100 50 0 Manufacturing industy

Mixed

Service industry

Fig. 5.5 Manufacturing versus service industry.

The next question strongly correlating with the classification in manufacturing versus services, is about the type of products (Spearman’s rank correlation, ρ = 0.75). Most companies in the sample (68%) deal with physical products (like in manufacturing) rather than informational products (like in service industries). However, there are also firms producing information products, for example a software developer, as well as service businesses who deal with physical products, e.g. retailers (see Fig. 5.6). Finally, we checked whether the firms in the sample rather sell to corporate or private customers. The majority (67%) focuses on business-to-business

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T.J. Winkler et al. 150 100 50 0 Physical products

Mixed

Informational products

Fig. 5.6 Physical versus informational products.

100 80 60 40 20 0 Business to consumer

Mixed

Business to business

Fig. 5.7 Business to consumer versus business to business.

(see Fig. 5.7). This is not surprising as most physical consumer products pass multi-tiered supply chains spanning several businesses and retailers, before reaching the end consumer. 5.2.2.3. Firm sizes Regarding firm sizes, most companies represented in the sample are rather large, which is partly a result of our pre-selection. 91.8% of the companies have more than 500 employees, 95.3% have more than 50 million Euros of revenues. Still, some companies fall below these thresholds as we also selected companies from the database which fulfill only one of these size criteria. The size comparison of invited (expected) and responding (observed) companies reveals that large companies have responded over-proportionally (see Fig. 5.8). The mean of employees is much higher for respondent (mresp = 4,783) than for invited firms (minv = 1,739). This difference is confirmed by a chi-square test over the employee classes that rejects the null hypothesis of consistent distributions (χ2 = 106.4; df = 9; p < 0.1). A reason for this may be that respondents themselves have recognized that the survey was directed to larger firms. Also larger firms may be more accustomed to take part in such surveys and have developed better procedures to handle such requests. Regarding financial measures for firm size (revenues), we had to treat firms in the financial services sector separately. Since for banks and other financial institutions revenues are not an appropriate measure of firm size, we asked for the

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50.0% Invited

40.0%

Responded

30.0% 20.0% 10.0% 0.0%

Fig. 5.8 Employe firm size (invited and responded).

70

Revenue

60

Assets under management (financial services)

50 40 30 20 10 0