Arisa Shollo
email: as.inf@cbs.dk

Using Business Intelligence in IT Governance Decision Making

Keywords: Business Intelligence, decision making, case study, strategic decisions, predictive analytics

Business Intelligence' (BI) has been widely used to describe the process of gathering, analyzing and transforming large amounts of data into information useful for decision making. This paper examines BI from a decision-maker's perspective in an IT governance context through a case study of a large Scandinavian financial institution. The key findings indicate that BI is primarily used to inform structured operational decisions and as an instrument for dialogue in unstructured strategic decisions. Our study shows how 'hard facts' provided by BI are used as a foundation for opening a dialogue and as a supporting instrument to make arguments seem more convincing during decision-making discussions. We also found that standard performance reporting is used more for operational decision making, whereas predictive analytics are utilized primarily in strategic decision making. These results can assist managers looking to improve their operational and strategic decisionmaking processes by indicating the appropriate type of BI for each type of decision.

INTRODUCTION

The importance of intelligence in decision making is recognized from ancient times [START_REF] Tzu | The art of war[END_REF]. People have developed processes, techniques and tools for collecting and analyzing intelligence to support decision making, especially during times of war [START_REF] Kinsinger | The Business Intelligence Challenge in the Context of Regional Risk[END_REF]. Despite the considerable amount of research already conducted, decision making still remains one of the biggest challenges. The issues of decision making and computerized decisions have attracted the attention of academics and practitioners since the use of computers in organizational settings began. In the 1960s operations analysis was seen as the solution. Later, decision support was coupled to the use of computers, leading to decision support systems (DSS) [START_REF] Sprague | A Framework for the Development of Decision Support Systems[END_REF] and executive support systems (ESS) [START_REF] Rockart | Executive Support Systems[END_REF]. After data warehousing [START_REF] Inmon | Building the Data Warehouse[END_REF][START_REF] Kimball | The Data Warehouse Toolkit[END_REF] and on-line analytical processing (OLAP) (Chaudhuri & Dayal, 1997;[START_REF] Gray | Data cube: a relational aggregation operator generalizing groupby, cross-tab, and sub-totals[END_REF] began to broaden the realm of decision support systems in the 1990s, many organizations realized the importance of business intelligence (BI) and sought to leverage it in their work [START_REF] Golfarelli | Beyond Data Warehousing: What's Next in Business Intelligence?[END_REF][START_REF] Wixom | The BI-Based Organisation[END_REF].

The concept of BI has acquired wide recognition in the business world over the last two decades. Although the term 'business intelligence' has been in use since 1800, (Google Ngram Viewer, 2011), it was used in scientific context for the first time in an article by Hans Peter Luhn, an IBM researcher. In the article, [START_REF] Luhn | A Business Intelligence System[END_REF] described an "automatic method to provide current awareness services to scientists and engineers" who needed help to cope with the growth of the scientific and technical literature. However, it was only in the 1990s when Howard Dresner, [START_REF] Dekkers | Organising for Business Intelligence: A Framework for Aligning the Use and Development of Information[END_REF], popularized the term BI, that it was widely adopted to convey the idea that the collected information in a business' IT systems could be exploited by the business itself to extract new insights. Today, the term is used to describe all decision support applications, processes and technologies [START_REF] Shollo | Towards an Understanding of Business Intelligence[END_REF][START_REF] Wixom | The BI-Based Organisation[END_REF].

Over the last two decades, both industry and academia have been focusing on developing and adopting BI technologies to provide intelligence and insights to decision-makers. On one side, organizations have built data warehouses, acquired BI tools, supported acceptance by end users and applied the information to make business decisions. On the other side, academics have been refining the concept of BI along with its associated development processes and best practices. Many organizations have succeeded in transforming raw data into information, actionable insights or knowledge. According to [START_REF] Wixom | The BI-Based Organisation[END_REF], BI has moved from being a peripheral contributor to being a prerequisite for organizational success. However, the outputs of BI -information, actionable insight or knowledge -do not by themselves guarantee its use by decision-makers.

In this paper, we examine how BI is used in decision making from a decisionmaker's perspective. We conducted an extensive and in-depth literature review on the subject along with an empirical study in a financial organization to explore the use of BI in a decision-making environment. This study is part of a larger project in which we are investigating how we can use BI in IT governance and specifically in the IT project prioritization process.

The remainder of the paper is structured as follows. The next section presents previous work on the topic and describes the research gap. In section three we describe the methodology used to conduct the empirical study. Section four presents the results of the study and in section five we discuss the findings of the research and indicate future research directions.

BACKGROUND

From a first look at the literature [START_REF] Davenport | Working Knowledge: How Do Organisations Manage What They Know[END_REF] one understands that BI is related to strategic management and performance management. Looking at BI from a decision-maker's perspective, we investigated the current state of BI in relation to strategic management, performance management and decision making. We conducted a literature review with a focus on how managers use BI, for what purposes and with what effects on performance and strategies in an organization. For an extensive review read [START_REF] Shollo | Towards an Understanding of Business Intelligence[END_REF].

The literature review revealed a gap in the research related to the role of decision making within BI. According to [START_REF] Arnott | Eight Key Issues for the Decision Support Systems Discipline[END_REF] and [START_REF] Yi-Ming | Dynamic Interactive Framework to Link Business Intelligence with Strategy[END_REF], most of the studies of BI have focused on design, development and application of BI tools, neglecting the use of information and knowledge. Thus, there is a substantial amount of literature on how to gather and store raw business-related data. This literature includes studies of structured and unstructured data as well as internal and external data. These studies are combined with literature on developing and employing required technologies, such as data warehouses and document warehouses [START_REF] Baars | Management Support with Structured and Unstructured Data -an Integrated Business Intelligence Framework[END_REF][START_REF] Inmon | Building the Data Warehouse[END_REF][START_REF] Kimball | The Data Warehouse Toolkit[END_REF].

There are fewer studies the analysis and transformation of data into information and information into knowledge. The focus in these studies is on methods [START_REF] Golfarelli | Beyond Data Warehousing: What's Next in Business Intelligence?[END_REF][START_REF] Yi-Ming | Dynamic Interactive Framework to Link Business Intelligence with Strategy[END_REF], techniques [START_REF] Blumberg | The Problem with Unstructured Data[END_REF][START_REF] Baars | Management Support with Structured and Unstructured Data -an Integrated Business Intelligence Framework[END_REF][START_REF] Chung | A Visual Framework for Knowledge Discovery on the Web: An Empirical Study of Business Intelligence Exploration[END_REF][START_REF] Negash | Business Intelligence[END_REF] and technologies, such as OLAP and data mining, that facilitate the transformation of raw data into information and knowledge. Despite the fact that there is a considerable body of literature on technology support, researchers have focused mainly on technology from a problemcentric perspective, overlooking the decision-maker's perspective.

The critical research gap, however, lies in the fact that there are almost no studies, with the exception of a few [START_REF] Davenport | Bi and Organisational Decissions[END_REF], which address decision making based on business intelligence. We argue this represents an oxymoron: while there is a consensus among the authors of all reviewed articles that BI supports decision making, none of them couple the development or use of information with the decision-making process itself. The literature on BI thus does not cover how BI addresses the needs of the decision-making process. Moreover, no studies were found that focus on how the intelligence provided is used in decision making or what processes are in place to ensure the use of intelligence in the decision-making process. One reason for this could be that BI is not a well established field and "current research is largely focused on technology and getting the data right" [START_REF] Arnott | Eight Key Issues for the Decision Support Systems Discipline[END_REF], leading companies to focus only on those aspects when making decisions [START_REF] Presthus | E-business in entertainment: Insights from the use of Business Intelligence in the Norwegian music industry[END_REF]Davenport et al., 2001).

We agree with the contention of [START_REF] Martinsons | A Strategic Vision for Managing Business Intelligence[END_REF] and [START_REF] Davenport | Bi and Organisational Decissions[END_REF] that it is not enough to analyze data, provide information and use knowledge. Organizations must look specifically into decision processes in order to deliver useful information to the decision-makers and for those decision-makers to act upon the information and knowledge obtained. Intelligence is only produced through action (making decisions). As [START_REF] Fuld | Be Prepared[END_REF] states, "intelligence is an asset only if it is used". Unfortunately, in many cases, the produced information is not used, is unsuited for decision-making purposes or is ambiguous and interpreted differently across different contexts [START_REF] Davenport | Bi and Organisational Decissions[END_REF].

The product of BI, among other properties, must match the decision-making environment in which it is used [START_REF] Clark | The Dynamic Structure of Management Support Systems: Theory Development, Research Focus and Direction[END_REF]. However, recent studies suggest that many organizations do not fully understand the link between their BI and the decision-making environment they use it in [START_REF] Clark | The Dynamic Structure of Management Support Systems: Theory Development, Research Focus and Direction[END_REF]Hostmann et al., 2007;[START_REF] Davenport | Bi and Organisational Decissions[END_REF].

In the decision-making literature, decision-making environments have been classified according to decision types. A distinction is made between structured and unstructured decisions or, as introduced by [START_REF] Simon | The New Science of Management Decision, 3rd revised edition[END_REF], between programmed and nonprogrammed decisions. Simon stated: "Decisions are programmed to the extent that they are repetitive and routine, to the extent that a definite procedure has been worked out for handling them so that they don't have to be treated from scratch each time they occur" (p. 46). On the other hand, decisions are nonprogrammed "to the extent that they are novel, unstructured and unusually consequential" (Simon, 2007, p. 46). Programmed or structured decisions involve well-defined, measurable and compatible criteria, while nonprogrammed or unstructured decisions come under the heading of "problem solving" (Simon 1977, p. 64-65). Operational decisions tend to be structured, while strategic decisions tend to be unstructured [START_REF] Simon | The New Science of Management Decision, 3rd revised edition[END_REF]. Based on this distinction we will use the following analytic framework to conduct the research study as presented in Table 1.

Different decision types call for different methods of decision making and different information requirements (Gorry & Scot, 1971). Techniques used for operational decisions, for instance, are rarely used in strategic decisions. In this study we focus on the distinction between operational versus strategic decisions as it relates to the use and type of BI used.

Table 1. Analytical framework

Structured / Operational decisions Unstructured / Strategic decisions

How is BI used in structured / operational decision making?

How is BI used in unstructured / strategic decision making?

What kind of BI is used in structured / operational decision making?

What kind of BI is used in unstructured / strategic decision making?

The purpose of this paper is to explore how decision-makers use BI in the context of different decision types based on the analytical framework illustrated in Table 1. In the next section, we describe the methodology used to investigate how decisionmakers use BI as a product, in terms of information, insights and knowledge in decision making.

RESEARCH DESIGN AND PRESENTATION OF THE CASE STUDY

The empirical basis for this research was an in-depth case study exploring the role of BI in decision making. We conducted an exploratory case study to provide insights into the use of different types of BI in different decision-making environments. Case studies are particularly valuable for exploratory research where a thorough understanding of a phenomenon in its context is preferred [START_REF] Benbasat | The case research strategy in studies of information systems[END_REF].

As an empirical setting we chose a financial institution in Scandinavia to explore how managers use BI in an organization with a strong tradition of using of hard data and financial models in forecasting activities.

Research Setting

The organization is an international financial institution with its headquarters in Scandinavia. The organization is recognized as a successful financial company with a high market share. The study was conducted in the IT unit, which is responsible for standardizing and automating processes, and developing IT systems to enhance the efficiency of the entire organization. The IT unit run by the chief information officer (CIO) and is composed of seven development areas managed by development directors. Each development area is further subdivided into departments headed by development managers. In total, there are 38 IT departments, employing 2200 employees.

Data collection

We conducted eight interviews with key IT governance personnel in the case organization. Two additional interviews were conducted with external subject experts to triangulate the data. Background information about the company was also collected and served as complementary material to the interviews. The participants used BI in their everyday work and they were from different levels of the organization. The form of the interviews was semi-structured, with open-ended questions asked about the use of BI in decision makingAn interview guide was created before the interview that included questions such as:

• For what purposes do you use BI?

• Does the information in the reports support many decisions or does each report target a specific decision? • How much do you believe BI influences your decisions? • Do you base your decisions only on the numbers in the reports? • Do you use other channels, methods, connections or tools to support your decisions?

Each interview was conducted at the interviewee's office and lasted an average of 60 minutes. Participants were informed that the interview was about the use of Business Intelligence in their decision-making activities, but were not shown the questions ahead of time. All the interviews were recorded with the consent of the interviewees. The interviews were carried out in English and were transcribed by the researcher afterwards. Table 2 presents the participants and their roles in the organization. External expert on BI

The collected background material included organization charts, reports, spreadsheets, forms, PowerPoint presentations, memos, and meeting minutes. This documentation was collected in order to triangulate the data with the interviews.

Data Analysis

We carefully read through the transcripts of the interviews and the meetings as well as the official notes and the field notes to get a detailed picture of the empirical setting. While reading the interviews and meeting transcripts we were looking for indicators of how BI was used in decision-making processes. In order to investigate our research question, we employed constant comparative techniques (Strauss, & Corbin, 2008) in which we gathered and analyzed qualitative data in a systematic and iterative manner.

During data analysis we applied open coding inspired by grounded theory (Strauss & Corbin, 1998). Content analysis (Strauss & Corbin, 1998) was also employed to assess the collected material. In particular, we read the documents and transcripts to identify themes in the raw data across the different sources. The themes pursued included concepts such as "reports", "hard facts", "scorecard", "performance", "data" and "decision". We organized these first order codes into tables that illustrated a single theme across the various data sources, inspired by the in-vivo coding technique (Strauss, & Corbin, 2008). In the next step, we developed second order themes by using the four key questions of the framework to sort through the data. Those questions were: 1) How is BI used in unstructured strategic decisions? 2) How is BI used in structured operational decisions? 3) What type of BI is used in strategic decision making? 4) What type of BI is used in operational decision making? In the final step, through an iterative analysis of the data, "informing", "dialogue" and "convincing" and their relationship to "BI and decision making" emerged as transparently observable phenomena (Eisenhardt, 1989) in the data.

RESULTS

In this section, we present how and in what forms BI is used in decision making in the following categories that emerged from the data analysis phase.

The use of BI in decision making

Decision-makers use BI for different purposes in the decision-making process. We illustrate each purpose observed in our case in the next paragraphs, attaching representative quotes from the interview data.

Using BI to inform decisions.

The interviewees reported that they use BI to directly inform their decisions. This is especially obvious when BI addresses specific or structured decisions, creating a tight linkage between intelligence and decisions. In this case, data are analyzed with a specific question in mind and the report adresses this specific question.

"So therefore, we need to … come up with some good reporting on how we can allocate our capital in the best manner. So, where do we actually target the customer groups which we want to target and on what kind of product areas do we actually want to target in order to allocate our capital in the right way? So that's sort of the reporting we do, saying okay, we need this, we know that this customer group is very profitable and also have a potential long term relation with the company. So, we need to focus on this area and that's where we should allocate our capital and that would be a clear decision based on our reporting, saying okay, this is the target group that we need to focus on." (Participant 8) "I think, what we learned was that the margins for instance regarding price need to be adapted to the new environment in the financial crisis. So that's something that came quite clear in the reporting that we did, meaning that each brand initiated various projects in order to sort of adopt the price level margins to current environment, so there you can see actually direct sort of decisions being based on the reporting." (Participant 6) Using BI as an instrument for dialogue.

When asked how BI influences their decisions, managers responded that they use the reports, scorecards and dashboards as an instrument for dialogue with other employees and departments in the organization and thus as the basis for further investigation.

"It is a dialogue tool, primarily because other factors are also in play, which are not captured by our scorecard. So, the scorecard is a foundation for measuring performance, no doubt about that, but other factors, I think we call them hygiene factors … it could be other information that [the manager] is just extracting from the market, providing it to the management or all other things that are sort of intangible, that we cannot measure. So we don't believe that you can measure business purely by mathematical numbers and performance, you need to look it as a whole perspective that's why I mentioned it as a dialogue tool." (Participant 8) "We use it both for ourselves correcting the accounts when it is wrong, but we also use this information to have a dialogue with the system owner…" (Participant 7) "Well it creates a dialogue, it creates a conversation in the executive committee saying, do we actually have the right price focus on each sub-customer segment. And that creates a sort of decision-making process" (Participant 8) BI is thus seen as a dialogue opener, allowing managers to engage in a discussion as they interpret the results and try to make sense of the numbers. According to the interviewees, there are other aspects not captured by BI. As indicated in the following quotes, BI is only part of the picture that initiates the dialogue. Tacit knowledge and experience also play a very important role in creating a full picture of the problem or issue discussed.

"I call the managers and ask them what do you do since you are doing so well and what is the problem since you are red in this area." (Participant 4)

"So, as I mentioned it, … I think you need to have a dialogue. You need to have this human touch and you need to get a feeling also what's going on in the business. Also, lots of information is impossible for us to measure, so lots of information is coming, sort of, from discussions and that's also part of the decision-making process. So, I think, all the reporting that we do, all the ratios that we deliver, all the numbers are just a part of it and then the rest is based on your, I wouldn't say gut feeling, but your business knowledge and your conversations with the business, that is also a very important part of decision making." (Participant 8) "When you see the results or the data you should use your common sense, so when you make a decision you should sort of respect your data and the story, you should investigate: does it make sense?" (Participant 2)

Using BI as a convincing argument.

The interviewees use data as a powerful tool to convince top management about the significance of an issue and its impact. Having data to support your argument legitimizes decisions, particularly in front of other people. In the following quotes one can observe how data are used to convince others in making a certain decision and taking action.

Interviewee: No, [the data] is just the argument, but very often if you want to have something in a hurry, then you need the data because it is the only way that you can convince people that this is a serious problem. You have to be able to tell to people, well this is affecting 100,000 customers, this is affecting all brands and you always have to tell how serious is this issue." (Participant 2)

"…well, we have been looking into your data for the last month. We really have some deviations here, you need to solve it and there we have the material, you know, you can present it to them saying, okay you can see it here, these are the dated deviations throughout the last 30 days." (Participant 7)

Types of BI in decision making

The mainstream BI used by the interviews consisted largely of reports and spreadsheet analysis of past historical performance data. However, the interviewees reported that more advanced analytics such as what-if scenarios are often more useful in strategic decision making.

Standard reporting of past performance.

The interviewees stated that they use the information from reports in structure / operational decisions where the steps to solve the problem are well defined. We provide examples here of how one of the managers uses BI in operational decision making.

"As I said, we need to close the books … so I needed to go down to transaction level actually to see if those transactions ended up correctly in the accounting system, so that was the main purpose…" (Participant 7) "So, BI tools are used … rather for investigations in connection with testing activities or tracing errors, alright? So I'm not using BI for making decisions if we need to man up by one FTE [full time employee] or see if I should get rid of one FTE and which one or whatever could be a part of my decision making as a department head, right?" (Participant 7)

Predictive analytics.

The use of what-if scenarios along with correlations between events and their implications can provide a sound foundation for decision making. This type of BI falls into predictive analytics and is particularly important for strategic decisions. Predictive analytics are used to create a variety of what-if scenarios in order to predict future outcomes through forecasting and deep data analysis.

"At the end of the day, it is a business decision which projects you would like to support. So, you can never automate, but what makes sense is actually not to create the portfolio but when you need to make changes in the portfolio if you take the resources into scope, the bottlenecks, the limitations into scope, then you can use it for a kind of decision base. But it is really more to have a foundation for an enlightened decision … you can never use it mechanically, it would never work. But you can say the consequence of doing this is X and the consequence of doing this is Y. But I think the effect is indirect." (Participant 9)

DISCUSSION AND CONCLUSION

The results presented in the previous section show how BI directly informs structured decisions. BI may also inform partially tactical and strategic decision making when it addresses the specific decision. However, when BI addresses a range of different decisions it is primarily used as an instrument for dialogue or as a convincing tool in tactical and strategic decision making. BI extracted by standard reporting, including scorecards and dashboards, addresses a range of different decisions. This limits its use in tactical and strategic decision making because the information extracted does not address a specific decision but rather generates questions in an effort to illuminate multiple decisions. Organizations face a trade-off between providing BI for decision-specific support and providing BI for a variety of decisions. Decision-specific support requires considerable organizational effort and capital. At the same time, providing BI for a range of different decisions does not necessarily assure adequate support of the individual decisions. [START_REF] Davenport | Bi and Organisational Decissions[END_REF], considering the above trade-off, suggests that companies should select the most important organizational decisions and create the appropriate BI support.

BI also appears as an instrument for dialogue in decision making. While the information or knowledge extracted does not address specific decisions, it can help managers to engage in dialogue to make sense of information and to investigate options. Decision-makers often need to tackle tactical and strategic decisions in which the decision criteria are not well defined or measurable. In this case, information or knowledge extracted is only part of the picture and managers need to engage in a dialogue in order to consider and discuss "the rest of the truth" that is not captured in reports. As the interviewees stated, the dialogues iniated by BI often bring up contextual and business knowledge as part of the discussion. [START_REF] Choo | The Knowing Organisation: How Organisations Use Information to Construct Meaning, Create Knowledge, and Make Decisions[END_REF] refers to this use of information as "enlightenment" in which "information is used to develop a context or to make sense of a situation." BI is premised on the rational-scientific paradigm that there is an objective truth that can be measured. We found that BI is often positioned as "hard facts" and is used by decision-makers as an argument to support or justify their decisions and to convince others. However, using BI as a convincing argument could have some negative implications for organizations. For example, according to March (1995, pp.), "numbers presuppose a concept of what should be measured and a way of translating that concept into things that can be measured". He continues by positioning "...the pursuit of truth as a sham..." in which "decision-makers find it possible to "discover" a truth that happens to be consistent with their own interests." In this view, data provides knowledge, knowledge is power and, therefore, data is power. In organizations in which evidence is required to legitimize decisions, the power of hard facts increases, as does the likelihood that managers will "find" hard facts that confirm their beliefs. This behavior is similar to a decision-based evidence making approach [START_REF] Tingling | Is Decision-based Evidence Making necessarily bad?[END_REF].

Our results show that information extracted from reports, scorecards and dashboards largely addresses and informs structured, operational decisions. This finding is consistent with the results of [START_REF] Isik | Business Intelligence Success: An Empirical Evaluation of the Role of BI Capabilities and the Decision Environment[END_REF] who found that data-oriented BI capabilities are more critical for operational decisions than for strategic decisions. As we move from structured to unstructured decisions, the use of BI in decision making changes from informing decisions to serving as an instrument for dialogue. The use of BI as a dialogue tool initiates conversations that, according to [START_REF] May | The New Know: Innovation Powered by Analytics[END_REF], have a positive impact on organizational performance because they stimulate learning.

The impact of using BI as a dialogue tool in decision making is indirect. Although it does not directly support or inform decisions, it creates a context in which decisionmakers can interpret the information and discuss the problem. Predictive analytics, techniques that exploit patterns found in historical data to identify risks and opportunities, appear to be especially useful in this case. These techniques capture relationships among many factors and allow the development of different possible scenarios in decision making. With predictive analytics, the range of situations in which BI can be used expands considerably, especially in strategic decision-making. On the other hand, we observe that BI extracted by standard reporting, including scorecards and dashboards, is generally used in operational decision making. Their actual use in strategic decisions is limited because of their reactive nature.

The contribution of this paper is twofold. First, our findings illustrate the role of BI in strategic decision-making environments. These findings suggest that BI as a product -information, insight or knowledge -is used to foster dialogue or as a convincing instrument in strategic decision making, but appears to have a more direct impact in operational decision making because it informs specific decisions. Second, we have found that BI standard reporting capabilities that analyze past performance are useful for structured, operational decisions, while predictive analytics that focus on modeling to create competitive advantages are more useful in unstructured, strategic decision making.

This study has several implications for research and practice. From a research standpoint, our study explores the role of BI in decision making as an instrument for dialogue that engages managers in interpretation and knowledge externalization. This use of BI as an instrument for dialogue indicates new perspectives for IS researchers, suggesting that we address BI not as mere facts but as a sensemaking mechanism in decision making. From the point of view of practice, the study has implications for designing and developing BI decision support infrastructures. Organizations should consider the right type of BI according to the nature of the decision and should be aware of the degree to which evidence is required in legitimizing decisions in order to avoid evidence making by decision-makers. To provide adequate support for each type of decision-making environment, we need further studies of the relationship between the use of BI in both strategic and operational decision-making environments. We propose these as possible future research streams.

The purpose of this paper has been to explore the role of BI in decision making. We have reported findings from an empirical study in which managers from a large organization were interviewed in relation to their BI-use in decision making. We have argued that BI is used as a dialogue and convincing instrument in strategic decision making, and have introduced new theoretical insights into BI research.

Table 2 .

 2 Details of the interview participants

	Participant	Role in the organization
	Participant 1	Head of IT Management Support
	Participant 2	Head of IT Credit Processes
	Participant 3	Regional Manager
	Participant 4	Business Analyst
	Participant 5	Head of Forecasting Models
	Participant 6	Business Analyst
	Participant 7	IT Finance Business Analyst
	Participant 8	Performance Management Specialist
	Participant 9	External expert on Project Portfolio Management & BI
	Participant 10

ACKNOWLEDGMENTS

The author wishes to express her gratitude to her supervisor, Professor Karlheinz Kautz and Associate Professor Ioanna Constantiou who offered invaluable assistance, support and guidance. Special thanks also to her friends and Ph.D. students Konstantinos Manikas and Maria Ie Pedersen for invaluable reviewing assistance.

AUTHOR BIOGRAPHIES

Arisa Shollo is an Industrial Ph.D. student at the department of IT Management at Copenhagen Business School. Her interests lie in the area of IT business value, business intelligence, decision making and decision support.


Markus Helfert
email: markus.helfert@computing.dcu.ie

Tony O'brien
email: tony.obrien@hotmail.com

Sustaining Data Quality -Creating and Sustaining Data Quality within Diverse Enterprise Resource Planning and Information Systems

Keywords: Data Quality, Data Governance, Enterprise Resource Planning, Key Performance Assessments

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Over the last two decades data quality has been identified as a major concern for many enterprises [START_REF] Redman | Improve Data Quality for Competitive Advantage[END_REF]; [START_REF] English | The High Cost of Low-Quality Data[END_REF]; [START_REF] Redman | The Impact of Poor Data Quality on the Typical Enterprise[END_REF]; [START_REF] English | Improving Data Warehouse and Business Information Quality-Methods for Reducing Costs and Increasing Profits[END_REF]; [START_REF] Loshin | The Cost of Poor Data Quality[END_REF]; [START_REF] Redman | Data Quality: The Field Guide[END_REF]; [START_REF] Eckerson | Data Quality and the Bottom Line: Achieving Business Success through a Commitment to High Quality Data[END_REF]; Redman (2002); [START_REF] Redman | Data: An Unfolding Quality Disaster[END_REF]; [START_REF] English | Information Quality Applied[END_REF], none more so than those operating enterprise resource planning and information systems (Deloitte 1999). For this reason this study, allied to a practical data quality improvement initiative, attempted to discover 'How can an organisation create an environment where data quality improvements can be sustained'. Any attempt to determine the means by which the quality of data can be improved will produce only temporal ameliorations, unless such improvements become embedded. Without the latter, any gains emanating from the former will be merely marginal or short term at best.

In this paper we use a dual research approach employing both qualitative and quantitative research strategies focussed within the researcher's own organisation. Section 2 places this study within the confines of an actual ongoing data quality improvement initiative, within which the research process described within Section 3 is undertaken. In Section 4 we summarize the practical benefits of this study and present our research findings. Finally in Section 5 we conclude that the outcomes of this research have important implications for both theory and practice

CASE STUDY

Case Background and Description

The basis of the detailed research undertaken as part of this entire study was carried out within the researcher's own organisation Remploy. Remploy is the largest provider of employment opportunities for disabled persons in the UK, currently employing over three thousand disabled people in over sixty individual factories and offices across the entire country, whilst placing over 10,000 others into external open employment each year. A Baan/Infor ERP system was implemented over ten years ago and whilst there have been many benefits overall it was identified that there was still scope for further improvements especially within the areas of data quality and system complexity.

Initial Progress

This research study coincided with the commencement of a data quality improvement initiative within Remploy during 2005. An initial approach was made across a number of fronts to attempt to promote education and training; documentation of procedures; the acceptance of responsibility, ownership and accountability at all levels for processes and data; together with better management of master data with the identification and implementation of 'quick wins'.

Development of Data Accuracy Key Performance Indicators

As part of this initiative seven key performance indicators (KPIs) were established around the order fulfilment process, historically the sources of many of the data quality issues. The KPIs were chosen specifically to reflect the salient elements of these essential commercial operations relating to servicing customer needs. The KPIs were designed to reflect the view of the world as seen through the lens of the ERP system, compared with an actual view which could be obtained by direct observation of the actual physical order process. In other words how closely the 'system' (data within the ERP system) reflects reality (the real world) in the manner described by Wand and Wang (1997: 94), whilst also providing a measure of the quality of the actual data and the related processes. From the individual KPIs an aggregated Index was developed weighted to take account of the aging of the various transactions and this was then used as the definitive measurement of the ongoing quality of the data within the KPIs.

THE RESEARCH PROCESS, RESULTS AND DISCUSSION

Qualitative Study

The qualitative element of the research took the format of a series of discussiontype focus group meetings sharing experiences, ideas, issues, problems and successes, around a basic flexible agenda, employing an action research approach. This approach attempted to generate discussion and interaction to discover peoples' real feelings and attitudes towards their data. In all, forty eight of the fifty four factories and seven business operations and sales teams were covered.

The use of action research in this environment provided the study with a considerable degree of richness in that the researcher had been a member of the organisation for almost twenty years. During this time this 'insider researcher' had worked directly with the majority of the participants and was known to virtually all. This unique approach generated loyalty and trust amongst all parties and not only provided rich material for this study, but also enabled the researcher's colleagues to gain a greater understanding of the significance of quality data and to appreciate the importance of taking ownership of 'their' data. These latter consequences are seen as key to the subsequent improvements that were achieved.

From the outset certain important notions and impressions emerged from the discussions and the analysis and these were subsequently developed as key findings. It was felt that these fell in three broad categories relating to: lessons learnt that should be put in practice at all sites, involving basic quality management principles, ownership, responsibility and support, together with measurement and reporting; positive personal motivational factors which help to engender commitment from individuals, relating to internal competition and targets, an acceptance of best practices and how these relate to one's ideas and principles; together with organizational and cultural environmental elements essentially involving leadership and management issues.

Measurement and Reporting Process

The importance of measurement, analysis, reporting and feedback was emphasised continually throughout the entire research. Figure 1 below traces the progress of the improvement programme by tracking the Index over the initial three years, highlighting a real trend of improvement over this period, albeit with various explainable fluctuations. From analyzing the Table 1, it is evident that there was a significant improvement in the Index (27%), following the commencement of the factory and business meeting programme from December to April, in line with the number of meetings carried out. In addition it may be seen that this level of improvement was maintained immediately following the study and then further improved as the concept of data quality became more established within the organisation.

Summary of the Findings of the Study

There is considerable evidence to suggest that the progress and improvement described above have real significance leading one to believe that there is potential for real cultural change to take place if improvement initiatives are managed correctly.

This has implications for the wider context of this research as seen by the evolution of the measurement, reporting and overall improvement process, which may best be summarised below in Table 2. This highlights the way the improvement process progressed from a traditional topdown management approach to an all encompassing company-wide programme embracing all areas of the business, in line with the development and progression of this entire study There is considerable evidence to claim that any improvement initiative cannot be undertaken in isolation and that everyone needs to become involved. There is also however a caveat, in that at this stage it appears that the level of progress achieved appears to be commensurate with the levels of activity of the internal champions or change leaders, suggesting a climate of 'controlled sustainability' rather than 'self sustainability'. This may be a reflection of the relevant infancy of the overall initiative.

Quantitative Survey

To build upon the progress made within the Data Quality Improvement Programme and the qualitative study, a web-based survey was carried out during the summer of 2009 amongst fellow Remploy colleagues as a means of determining their views, attitudes, thoughts, feelings and opinions with regard to data quality. The overall response to the survey was extremely constructive and encouraging and it identified a significant positive attitude towards data quality in critical areas. One significant result identifies a disparity between peoples' perception of the quality of the data they receive and their own perceived ability to influence and pass on data of sufficient quality to satisfy their own information customers' needs.

Summary of the Findings of the Survey

Table 3 below summarises the organisational-specific findings into a more 'general' format that will enable them to be applied within a wider environment.

Table 3. Summary of the survey findings

Generic Data Quality

Remploy Data Quality • There is a high appreciation of the influence that People, Processes and Data have on the quality of data • There is a realisation of the importance of having the data right first time • The level of positive responses compares very favourably with the previous survey held amongst the data quality community

• The overall attitude towards measurement, reporting and feedback was very positive • There was an appreciation of the importance of education and training • Almost 60% felt that 'everyone' has a responsibility to improve the quality of their own and the organisation's data • There was a huge disparity between the respondents' perception of the quality of the data they influence (82%) and that which they receive (26%) • 90% of respondents identified measurement and reporting, problem resolution and process improvement as key elements for improving data quality

ANALYSIS OF FINDINGS

The direct operational benefits to Remploy of this study as highlighted by the improved Data Accuracy Index have been referred to in depth, but there is also evidence to suggest that there have also been considerable improvements of a cultural and strategic nature. Further operational and strategic advantages have been derived from enhanced reporting, budgeting and forecasting. The myriad of small meaningful ameliorations, both technical and procedural, which have been applied by passionate people during the period since the original Baan implementation, are now gaining greater maturity alongside higher quality data to generate both operational and informational benefits. Finally the recognition of the importance of data in relation to overall governance, risk and compliance has provided enhanced levels of authority and control.

The analysis of the key findings from this research and the subsequent detailed discussion enabled certain principle findings to emerge and these are seen as the main outcomes for knowledge and learning for both practice and theory that have emanated from this study to bring about change not only to improve the quality of data but also impart some degree of permanency.

The role of the champion is seen as key to promoting and embedding change and innovation. Local champions have emerged at various levels and from various functions within the organisation embracing the essential cultural and motivational philosophies to make improvements within their spheres of influence. The concept of measurement, reporting and feedback is a prerequisite for any successful change programme. Within this study this element is not viewed as a mere 'central' monitoring and control mechanism, but has been developed into a reporting process to provide sites and businesses with the information to manage their operations on a day to day basis. The necessity of time and maturity is seen as vitally important in embedding change. This element of 'organisational patience' is important to allow new 'processes', changes and improvements to become accepted and embedded as well as to enable 'people' to accept change, develop themselves, gain experience and learn new skills. In discussing the concept of sustainability, emphasis has been placed upon maintaining the momentum of improvement particularly within the process of measuring the quality of data. One has to be aware however that there may be occasions where the 'costs' of making further improvements within a particular field may outweigh the benefits that may accrue with the risk of sub-optimization. The quantitative survey and subsequent related detailed discussions have highlighted the huge disparity in peoples' perceptions of data quality between the data they influence (82%) and that which they receive (26%). This raises concerns in general as to the quality of communication and the way people view their roles and their own performance and the performances of others.

CONCLUSIONS AND IMPLICATIONS

The conclusions drawn from this study contend that this research has unearthed new knowledge as to the means by which data quality improvements may be sustained within diverse enterprise planning and information systems. Further evidence of some form of real sustainability becomes apparent when one examines the overall performance of the KPI Index in subsequent years. During the year to 31st Match 2011 a further 7% improvement was achieved making an aggregate 59% improvement overall in the four and a half years. This substantiates the belief that the data quality improvement processes described are becoming truly embedded in many area of the organisation.

Fig. 1 .

 1 Fig. 1. Data Accuracy KPI Index Improvement tracker

Table 1

 1 below relates the movements in the KPI Index with progression of the site meetings qualitative study both during and following the programme.

Table 1 .

 1 Data Accuracy KPI Monthly Performance

	Month 11	12	01	02	03	04	05	06	07	08	09	10	11
	Year	08							09
	No of
	Mee-	0	13	18	3	15	8	0	0	0	0	1	0	0
	tings
	Index
	Impr. %	0% 9% 7%	-4% 16% 1%	1%	5%	0%	2%	-3%	7%	2%
	Month
	Index
	Impr. %	0% 9% 15% 12% 26% 27% 27% 31% 31% 32% 30% 35% 37%
	Cum

Table 2 .

 2 Evolution of the improvement process

	"What gets measured gets done"	A good start, but by whom?
	moving to:
	"What gets measured by the Exec gets done quicker"	A further improvement, but too top-down
	leading finally to:
	What is measured, communicated,	Bottom-up supported by top-down
	discussed and agreed at all levels has a	A potential key to sustaining any kind of
	very good chance of becoming	change?
	embedded"

AUTHOR BIOGRAPHIES

Markus Helfert is a Lecturer in Information Systems at the School of Computing, Dublin City University (Ireland) and head of the Business Informatics Group. His research centers on Information Quality Management. He teaches and supervises research projects, both at undergraduate and postgraduate level. Markus Helfert has authored academic articles and book contributions and has presented his work at international conferences. Markus Helfert holds a Doctor in Business Administration from the University of St. Gallen (Switzerland).

Tony O'Brien is a Finance Manager within Remploy, the largest provider of employment opportunities for disabled people within the UK and is currently leading the information and data quality initiative within the organization. He has over thirtyfive years experience within the fields of Finance and IT and has been involved in the implementation and development of a number of Enterprise Resource Planning systems. Tony holds an MBA and recently completed a Doctorate of Business Administration at Nottingham Trent University, focusing upon sustaining data quality within planning and information systems.


Ulrich Bretschneider
email: bretschneider@wi-kassel.de

Jan Marco Leimeister
email: leimeister@wi-kassel.de

Getting customers' motives: Lean on motivation theory for designing virtual ideas communities

Keywords: virtual ideas communities, open innovation, user motivation, customer integration, motives

Virtual ideas communities such as Dell's "Ideastorm" or Intel's "Ideazone" are very popular in practice. In such communities distributed groups of individual customers focus on voluntarily sharing and elaborating innovative ideas to support company's new products development. However, a look at existing ideas community leads to the conclusion that many of them are featured to the minimum necessary. Typically, they fail to provide technical components and organizational arrangements that are able to motivate customers to submit ideas. Based on insights from motivation theory it is known that such components and arrangements could serve as incentives for submitting ideas, as they activate customers' corresponding motives, which again lead to idea submission. In reverse, this means when knowing customers different motives one can systematically derive adequate components and arrangements from it. The aim of this paper is to derive components from customers' motives. Our research approach is two-folded. First, we applied an online survey among participants of the SAPiens ideas community. We empirically queried motives that lead participants to submit ideas. After that we come up with an empirical tested set of six motives (self-marketing, fun, altruism, recognition, product improvement and enhancement as well as learning). Second, we used these six motives in order to derive a set of adequate components from it. Our research will deliver important examples and insights how to arrange virtual ideas communities with technical and organizational components and arrangements in order to make them more effective, so that more customers are willing to submit ideas.

INTRODUCTION

Open Innovation: Customer integration into innovation activities

In the 20th century, many leading industrial companies generated, developed and commercialized ideas for innovations in self-reliance. Nowadays, companies are increasingly rethinking the fundamental ways of managing their innovation activities. According to Chesbrough's open innovation paradigm, overcoming companies' boundaries in order to open up for other resources for innovation becomes more and more important [START_REF] Chesbrough | The era of open innovation[END_REF]. In this context customers are seen as one of the biggest resource for innovations. Customer integration into innovation activities is a strategy of value creation in which customers are taking part in innovation value creating activities. Customers respectively product users often have rather high product expertise as well as knowledge and creativity potential, which they gained by regular product usage. However, this customer's knowledge is hardly accessible for companies. When integrating customer into the product innovation process companies profit by getting access to customer's product know-how.

In particular, when integrating customers into the early stages of the product innovation process, which focuses on generating innovations ideas, companies tend to get access to customer innovation ideas. On the one hand, ideas expressed by customers reflect their needs and wishes. On the other hand these ideas can represent suggestions describing how ideas can be transferred into marketable products. These so called "need information" and "solution information" constitute valuable input for the product innovation process [START_REF] Von Hippel | Sticky information and the locus of problem solving[END_REF].

In literature and practice certain methods for integrating customers into the early stages of the innovation process are discussed. Von Hippel's "Lead-User-Approach" is a popular example of this understanding of customer integration [START_REF] Von Hippel | Lead users: a source of novel product concepts[END_REF]. The Lead-User-Method implies systematic identification of single innovative customers, so-called lead users, and their integration into workshops in order to generate ideas and concepts for new products or services together with companies' employees.

In literature and practice ideas competitions are described as another familiar practice to get access to customer ideas. An ideas competition can be defined as an invitation of a company to its customer base in order to submit innovation ideas to a certain topic within a certain short timeline and typically via an Internet platform. An idea-reviewers committee evaluates these contributions and selects the winner [START_REF] Piller | Toolkits for Idea Competitions: A Novel Method to Integrate Users in New Product Development[END_REF][START_REF] Leimeister | Leveraging Crowdsourcing: Activation-Supporting Components for IT-Based Ideas Competitions[END_REF].

Recently, a novel method becomes relevant in practice. This alternative method can be constituted as "Virtual Ideas Communities". Ideas communities are initiated by companies and seek to offer customers a virtual forum for submitting innovation ideas. On the virtual community platform customers can post their ideas, vote for other participants' ideas and comment and/or discuss on other participants' ideas in order to help making ideas better in a collaborative manner.

While online user innovation communities in general are not a new phenomenon, as at least the open source software phenomenon demonstrates, with ideas communities there is an underlying difference. Firms run idea communities -from initial community building until continuous community management. This allows them to control the community in total and because of this to use its idea outcome non-restrictively. In contrast to that, so far known online user innovation communities, like open source communities or online communities of enthusiasts in basketball that share ideas for improving the design or other features of sport shoes [START_REF] Füller | Innovation Creation by online Basketball communities[END_REF], are run completely by and for users, which made it difficult for firms harnessing communities' outcome for new product development.

So, ideas communities, which we define as distributed groups of individual customers focused on sharing and elaborating innovation ideas supported by computer mediated community platforms as well as initiated and run by firms, are an emerging popular strategy in order to gain ideas for innovations from customers. Companies like Google, Intel, BMW, SAP, or Acrobat are only a few examples that run ideas communities.

Theoretical background: motivation theory

Motivation psychology differentiates between the notion "motive" and "motivation". A motive is seen as an individual's psychological disposition [START_REF] Von Rosenstiel | Grundlagen der Organisationspsychologie[END_REF]. This disposition describes how important certain goals for an individual are. Some motives are inborn but a relatively stable set of motives is developed during an individual's socialization process [START_REF] Heckhausen | Motivation und Handeln[END_REF]. This set of motives constitutes an individual's cognitive subsystem. Motivation describes the process how an individual's motives become activated. The basic principle of motivation is characterized in motivation psychology as follows: In a particular situational context, an adequate motive will be activated and subsequently cause certain behaviour. In such situational contexts certain things, that an individual perceives, will serve as incentive that stimulates corresponding motives. So motives can be seen as incitement to human act and behaviour [START_REF] Von Rosenstiel | Grundlagen der Organisationspsychologie[END_REF]. Von [START_REF] Von Rosenstiel | Grundlagen der Organisationspsychologie[END_REF] illustrates the activation of human behaviour in a simple model, shown in the following figure. One can distinguish between intrinsic and extrinsic motivation: There are certain activities and behaviors that some people naturally engage in, such as eating or drinking. Deci calls this intrinsic motivation, because the underlying motives are stimulated by an inborn feeling, such as hunger or thirst, not by a situational context as described above [START_REF] Deci | Intrinsic Motivation and Self-Determination in Human Behavio[END_REF]. Beside the motives that belong to the class of internal motivation there are several other motives, which do not arise from an individual's inborn desire. They arise directly from external stimuli that are perceived from above mentioned situational context. These motives can thus be categorized into the class of so called external motives [START_REF] Deci | Intrinsic Motivation and Self-Determination in Human Behavio[END_REF].

For our research one can draw on this motivation model. So, adapted to the case of ideas communities certain components of ideas communities can be interpreted by a customer as a mentioned incentive that again activates this person's individual corresponding motive and then finally lead to idea submission.

1.3

Research aim, approach, and methodology Many ideas community offer only a limited range of attractive components, such as technical functionalities, tools or organizational arrangements. Typically, ideas communities only offer three, technical based core-elements. These are IT-based systems for idea up-loading, idea commenting, and idea evaluating. Certainly, the lack of attractive components and arrangements is a main reason why most ideas communities count only few ideas. However, providing more attractive components is the manipulating variable that firms can use in order to influence customers' willingness for idea submission, as can be learned from above mentioned motivation theory.

So, our research aimed at identifying much more attractive components and arrangements for virtual ideas communities than existing core elements. The underlying approach of our research in accordance to the above described motivation model is as follows: Knowing customers' motives one is able to determine adequate components and arrangements that serve as incentives for stimulating theirs corresponding motives and than in turn will make them submit ideas in a much more willing manner.

So, our research seeks to deliver technical-as well as organizational-based components and arrangements that are able to raise idea output in ideas communities. As Schneiderman [START_REF] Shneiderman | Creating Creativity: User Interfaces for Supporting Innovation[END_REF] emphasized the necessity of efficient environments enabling innovation and creativity processes in the scope of customer integration, our findings will contribute for designing such effective environments within idea communities.

Our research approach is two-folded. First, we applied an online survey among participants of the SAPiens idea community. We empirically queried motives that generally lead SAPiens participants to submit ideas. After that we come up with an empirical tested set of six motives (self-marketing, fun, altruism, recognition, product improvement and enhancement as well as learning). Second, we used these six motives in order to derive a set of adequate components and arrangements from it.

Case background: The SAPiens ideas community

SAPiens is an Internet based ideas community (www.sapiens.info) initiated and run by the ERP software producer SAP. SAPiens was launched in summer 2009 and targeted users of SAP software. Each submitted idea, phrased in an average length of five-line phrases, was visualised in an idea pool, a separate section of the online platform. Figure 2 shows the homepage of the SAPiens ideas community.

Until March 2010 156 SAP users became registered members of the SAPiens community. Of those users, 149 actively participated by submitting at least one idea. The rest participated by just scoring and commenting submissions of other users or simply lurk. The comments and user evaluations helped the ideas presenters to refine their ideas.

Fig. 2. Homepage of the SAPiens ideas community

MOTIVES OF SAPIENS' MEMBERS

In order to research motives that make SAPiens members submitting ideas we query a set of eleven possible and adequate operationalized motives among SAPiens members with the help of an online survey. Before that, we extracted queried motives from an extensive literature review. After data collection we analysed empirical data with the help of factor analysis. All results are presented in the following sections.

Literature review

Human motivation has been discussed prominently in the field of open source community research. Various motives are examined that make open source software programmers participate in open source software projects. As open source software communities are basically comparable to ideas communities it is worth to check if motives examined in the open source domain could be extracted to our case. So we conducted a literature review. We examined six empirical studies out of the field of open source research that deal with programmers' motives for participation in open source communities. We focused on its examined motivation factors and analyzed which of them are appropriable for the use of our own survey. Based on the insights of this research we applied 11 motives, which are briefly described as follows.

The first motive is fun. Fun is a prominent motive studied in several open source motivation studies, e.g., [START_REF] Hars | Working for free? Motivations for participating in open-source projects[END_REF], [START_REF] Lakhani | Why Hackers Do What They Do. Understanding Motivation and Effort in Free/Open Source Software Projects[END_REF], and Osterloh et al. [START_REF] Osterloh | Open Source Software Production: Climbing on the Shoulders of Giants[END_REF]. In open source context, the fun motive is described as having fun or enjoying one-self when programming. Applied to ideas communities the fun motive is manifested in having fun in developing ideas.

The second motive out of the class of intrinsic motivation is intellectual stimulation. Raymond describes programmers who are motivated by this factor for engaging in open source communities as people "…who enjoys the intellectual challenge of creatively overcoming or circumventing limitations" [START_REF] Raymond | The New Hacker's Dictionary[END_REF].

In their study [START_REF] Lakhani | Why Hackers Do What They Do. Understanding Motivation and Effort in Free/Open Source Software Projects[END_REF] found out that the top single reason to contribute to open source projects is based on intellectual stimulation. Applied to ideas communities developing ideas for participants is intellectually stimulating.

An important motive considered in studies that explore motivations of open source software programmers is "altruism", e.g., [START_REF] Hars | Working for free? Motivations for participating in open-source projects[END_REF]. Open source software programmers who are motivated by altruism seek to increase the welfare of the open software community by writing program code without expecting any reward. Altruism can be interpreted as the direct opposite to selfishness or as "doing something for another at some cost to oneself" [START_REF] Ozinga | Altruism[END_REF]. Altruism can also be presumed to be a driver that motivates customers to participate in ideas communities.

Another intrinsic motive considered in open source motivation studies is "reciprocity". Shah [START_REF] Shah | Motivation, Governance & the Viability of Hybrid Forms in Open Source Software Development[END_REF] as well as Lakhani and Wolf [START_REF] Lakhani | Why Hackers Do What They Do. Understanding Motivation and Effort in Free/Open Source Software Projects[END_REF] found out that some open source programmers participate because they felt a sense of obligation to give something back to the open source community in return for the software tools it provides. This motive could also be assigned to the case of customer participation in ideas communities. So, some customer may feel obliged to SAP in return for the use of the SAP software.

One motive out of the class of external motives is the so called recognition, e.g., [START_REF] Hars | Working for free? Motivations for participating in open-source projects[END_REF] or Hertel et al. [START_REF] Hertel | Motivation of software developers in open source projects: An internet-based survey of contributors to the Linux kernel[END_REF]. Recognition contains expected reactions of significant others, such as other programmers. Motivation to contribute to a open source community should be higher the more positive the expected reactions of significant others are, weighted by the perceived importance of these significant others. This relation is formally expressed as a multiplicative function. Applied to ideas communities participants expect positive reactions from other participants as well as the organizer. These reactions by thirds may be caused by the submitted ideas displayed on the Internet platform.

Furthermore, people may consider participating in ideas communities as an effective way to demonstrate their capabilities and skills shown through their submitted ideas. Their achievements in ideas communities can be used to demonstrate competence to the organizer of the ideas community or others. Reactions by thirds may be caused on the basis of submitted ideas. Participating in ideas community, therefore, can be a good channel for self-advertisement for those seeking new job opportunities, for example. This phenomenon is mainly discussed in the field of researching motivations of open source programmers as self-marketing motive, e.g., [START_REF] Hars | Working for free? Motivations for participating in open-source projects[END_REF] or Hertel et al. [START_REF] Hertel | Motivation of software developers in open source projects: An internet-based survey of contributors to the Linux kernel[END_REF].

In the context of open source communities identification is examined also as a motivational factor. Identification is a reason for programmers engaging in open source communities when other participants sharing someone's aims, ideals, etc. [START_REF] Hars | Working for free? Motivations for participating in open-source projects[END_REF][START_REF] Osterloh | Open Source Software Production: Climbing on the Shoulders of Giants[END_REF][START_REF] Hertel | Motivation of software developers in open source projects: An internet-based survey of contributors to the Linux kernel[END_REF][START_REF] Lakhani | Why Hackers Do What They Do. Understanding Motivation and Effort in Free/Open Source Software Projects[END_REF]. Kelly and Breinlinger [START_REF] Kelly | Identity and injustice: exploring women's participation in collective action[END_REF] as well as Simon et al. [START_REF] Simon | Collective identification and social movement participation[END_REF]) used identification in order to explain why people engage in social movements of specific social groups such as older people, women, etc. Applied to ideas communities, people may regard for participating because they feel aligned to the organising firm of the ideas community in a manner that marketing science characterizes as customer's brand loyalty or company awareness [START_REF] Aaker | Dimensions of Brand Personality[END_REF]. So, identification with the organizing firm is a motivational factor worth to be include to our survey.

Insights from open source motivation research reveal that many open source programmers participate in open source projects because of their willing to improve functionality of the software or failures in the lines of code [START_REF] Hars | Working for free? Motivations for participating in open-source projects[END_REF]. This could be also relevant for participants of ideas communities. By submitting an idea participants may accentuate the necessity for improving the functionality or a defect of the underlying product. So, product improvement is a motivational factor worth to be include to our survey.

Furthermore, in the open source software research the need motive is discussed. As several studies, e.g., Gosh et al. [START_REF] Ghosh | The Free/Libre and Open Source Software Developers Survey and Study -FLOSS[END_REF] reveal that programmers engage in open source communities because they have a personal need or just detect a need for a certain kind of software. They appeal to an existing community or even form a new open software community in order to implement their need. Applied to the SAPiens ideas community customers may motivate to submit an idea because they detect a certain personal need which they phrase into an idea. So, the need motive seems to be worth included in our study.

Another motive out of the class of extrinsic motivation is learning. Learning is also discussed in the field of open source motivation research. Hars and Ou [START_REF] Hars | Working for free? Motivations for participating in open-source projects[END_REF] found out that some open source programmers are motivated for participating in open source projects by the prospect of selecting learning experiences. This motivation factor can be adopted for the present study. So, customers may also participate in ideas communities to expand their personal skills, capabilities, and knowledge.

Different open source motivation studies found out that open source software programmers also seek for contacts to peers in order to make new friends or socialize with others [START_REF] Hertel | Motivation of software developers in open source projects: An internet-based survey of contributors to the Linux kernel[END_REF]. When applied to ideas communities we expect that customer also have this motive to contribute to ideas communities.

Survey

The survey seeks to explore the motives that make participants of the SAPiens ideas community contribute ideas. Since perceived motivation-related issues can be best expressed by the participants of the SAPiens community themselves, we conducted a standardized questionnaire survey. 29 items were formulated in order to measure the 11 motives (see table 1). Using a rating scale ranging from 1 (strongly disagree) to 5 (strongly agree), participants were asked to rate the degree to which extent each motive makes him or her submitting ideas to the SAPiens ideas community. The questionnaire used in this study was structured, tested and consequently adapted to the needs of the target audience. The questionnaire was pre-tested by 10 experts pursuing doctoral and Master's degrees in information technology and business administration. The objectives of the pre-test were to ensure that none of the items were ambiguous as well as that the items adequately captured the domain of interest. Expert opinions' indicated that the content of the items was valid.

We run the online survey in March 2010. The questionnaire was implemented using the online-survey service "2aks". Each participant of the SAPiens ideas community that submitted at least one idea (N = 149) was provided with a personalized link to the online survey by eMail. The survey was administered over a period of four weeks. Eighty-seven participants provided adaptable answers to the questionnaire which represents a 58.39% response rate. 70.11 % of those adaptable answers were men (n = 61). 60.92 % (n = 53) of those adaptable answers were between 20 and 30 years old. As it concerns the occupation of these participants, with 55.17 % (n = 48) students were overrepresented in the sample. The rest were either SAP consultants or persons in charge that work with SAP applications once a day or at least a few times a week.

Results

We tested construct validity of our 11 motives and related 29 items based on an exploratory factor analysis. We analyzed the items with the help of the statistical software program SPSS 17.0. In order to check whether the data was appropriate for factor analysis we pre-analyzed the Measures of Sampling Adequacy (MSA) for the whole data structure as well as for individual items. The items REZ2, PV2, IH1, REZ4 as well as REZ3 showed MSA values that were lower 0.5. According to Cureton and D'Agostion's recommendation, who deemed that items achieve sampling adequacy if values are equal or exceed the criterion of 0.5 (Cureton and D'Agostino Getting customers' motives: Lean on motivation theory for designing virtual ideas communities 201 1983), these items were excluded within six iterations. After the sixth iteration all remaining items were above 0.6 and exploratory factor analysis was applicable. Furthermore, we pre-checked the global MSA value after the sixth iteration in order to ensure applicability of explorative factor analysis. With a MSA of 0.729 Cureton and D'Agostion's (Cureton and D'Agostino 1983) stringent 0.5-criteria was met, too.

The factor analysis resulted in six factors with eigenvalues higher 1 (varimax rotation). All the six factors explain a total of 66.321 % variance. The first factor explained 14.149 % variance. It was mostly determined by all items that represent the expected motive self-marketing as well as the item KZG1. As the intention to seek for peers in order to get in contact can be seen as an assumption for self-marketing activities as well as peers constitutes the target audience of self-marketing activities the KZG1 loading on this factor can be accepted plausible. Because of this, we will call this factor as "self-marketing" (component 1 in figure 5). The second factor explained 13.887 % variance and mostly was determined by all "fun" items. Furthermore, the item IH2 also loads on this factor. As intellectual stimulation can be interpreted as a form of fun we will accept including this item in factor 2. Following this argumentation we will call this factor "fun" (component 2 in table 2).

The items ALT1 and ALT2 as well as CI2 load on another factor, which explained 11.066 % variance. As altruistic feelings only will be brought toward a certain person or organization with whom or which one can identify this seems plausible. Thus, the third factor can be called "altruism" (component 3 in table 2). On the fourth factor load 3 items that expected to explain "recognition" (component 4 in table 2), solely (10.040 % variance).

The fifth factor, which represents a 9.989 % expression of variance, we call "product improvement and enhancement" (component 5 in table 2) as all need items as well as one of two product improvement items load on it. Finally, the sixth factor which explained additional 7.190 % variance was mostly determined by the supposed learning items. As supposed, learning (component 6 in table 2) seemed to be an independent motive.

The items REZ1, ANER2, CI1 as well as KZG2 were excluded as their values are < 0.55 according to Hair et al.'s recommendation, who deemed that items achieve acceptable factor loadings if values are equal or exceed the criterion of 0.55 [START_REF] Hair | Multivariate Data Analysis[END_REF]. After this complex explanatory factor analysis its results support the contention that our model has adequate construct validity.

The reliability of the resulting factors was checked using Cronbach's alpha. A Cronbach's alpha of 0.7 or higher [START_REF] Nunnally | Psychometric Theory[END_REF]) was used as an acceptable value for internal consistency of the measure. The Cronbach's alphas of the four factors range from 0.772 to 0.860 (compare table 2). These values support the contention that all the factors had adequate reliability.

As examination of validity as well as reliability of an underlying research model by solely applying explanatory factor analysis respectively Cronbach's alpha do not meet modern requirements [START_REF] Bogazzi | Assessing Construct Validity in Organizational Research[END_REF], according to Homburg and Giering's recommendation [START_REF] Homburg | Konzeptionalisierung und Operationalisierung komplexer Konstrukte: Ein Leitfaden für die Marketingforschung[END_REF] we secondly tested our new model, based on its six remaining factors and its corresponding 20 items, by applying confirmatory factor analysis and using Amos 18.0. First, we checked the global fit of the new model. The Goodness of Fit Index (GFI) was 0.951 and the Adjusted Goodness of Fit Index (AGFI) was 0.933. These indices were well over the under threshold of 0.9, which indicates an adequate fit [START_REF] Browne | Alternative ways of assessing model fit[END_REF]. In order to check reliability of the model, we measured all Individual Item Reliabilities, which exceeded the minimum threshold of 0.4 [START_REF] Homburg | Konzeptionalisierung und Operationalisierung komplexer Konstrukte: Ein Leitfaden für die Marketingforschung[END_REF]. Hence, good reliability is confirmed (compare table 3). Furthermore, all factors of our new model showed good values for Composite Reliabilities as well as good values for Average Variance Explained (AVE), so that convergent validity can be assumed (compare table 3). Values of 0.6 regarding the Composite Reliability and 0.5 for the AVE can be seen as minimum values for indicating a good measurement quality [START_REF] Bagozzi | On the evaluation of Structural Equatation Models[END_REF]. The discriminant validity of the factors was checked by using the Fornell-Larcker criteria, which claims that one factor's AVE should be higher than its squared correlation with every other factor [START_REF] Fornell | Evaluating Structural Equation Models with Unobservable Variables and Measurement Error[END_REF]. Tables 3 and4 depict that discriminant validity can be assumed for the six factors of our new model.

DERIVING COMPONENTS FOR VIRTUAL IDEAS COMMUNITIES

The purpose of our motivation study was to explore customers' motives for submitting ideas to the SAPiens idea community. Overall, the results suggest that there are six motives (self-marketing, fun, altruism, recognition, product improvement and enhancement as well as learning). In this section we exemplary use four of these six motives in order to derive adequate technical and organizational components and arrangements from it. Our research will deliver important examples and insights how to arrange virtual ideas communities with more attractive technical and organizational components and arrangements in order to make them more effective, so that more customers are willing to submit ideas.

First of all, we detected "Self-Marketing" as a significant motive. Because of this, organizers of ideas communities should procure possibilities that optimally display and represent participants' skills and capabilities. For example, implementing a profile site for every participant on the Internet platform of an ideas community -that displays participants' vita, competencies etc. as known from social network communities like Xing -would be fruitful in this context.

As our results show, the motive "Recognition" also was relevant. This suggests that organizers should play an active part in ideas communities and get in contact with participating customers, for example by commenting or giving positive feedback to participants' ideas, or praising participants' ideas as much as possible. In order to display positive reactions by the firm, organizers should assign "trophies" for customers' contributions by branding high quality ideas with a star, for example. These collectible achievements may more likely cause again positive reactions from other participants of the community. In order to get recognition from other participants rating systems would be fruitful. With the help of rating systems other users can quickly leave their opinions on ideas, for example by labeling a row of stars on which users can rate each idea. Our findings reveal "Learning" as a relevant motive, too. That means that customers also participate in ideas communities in order to expand their personal skills, capabilities, and knowledge. So, in order to raise the likelihood of ideas submissions organizers should implement environments, where participants can select learning experiences when developing ideas. For example, coining mentors or tutors assisting participants actively in developing or elaborating ideas would be a possible measure in this context.

In light of the motives "Recognition" as well as "Learning" it is also important to implement an atmosphere of cooperation amongst the members of the community, not only because the principle goal of ideas communities is that its members discuss and enhance innovation ideas. An atmosphere of interpersonal cooperation will also raise members' willingness to give recognition to other participants as well as members' willingness to share learning experiences to other participants. When there is any kind of competitive culture on the other side, as it can be observed in ideas competitions, it will cause a non-cooperative behavior and may even cause a schism within the community. So, in order to implement a collaborative culture the organizers have to take appropriate organizational measures in the scope of current community management.

Furthermore, when building and running ideas communities firms should take into account that fun is an important motive that leads to ideas submission. Thus, firms have to establish organizational structures or design artifacts that serve customers' fun during an individual's process of generating ideas. For example, external mentors that will support participants in the manner of a ghost-writer would be an adequate design element in this context. Furthermore, the community platform should offer a personal site where members can display their collection of ideas. Spending time in creating ideas, managing, sharing, and curating the individual collection will give pleasure to the members. Furthermore, in terms of the recognition motive the owner's individual ideas collection is validated and recognized when other members comment, rate or even just view the displayed ideas in the collection. Furthermore, such an ideas collection can serve as self-marketing tool as the displayed ideas mirrored indirectly owner's competences, creativity potential etc. In this context, owners may be seen as an expert in the area of interest, which enhances its reputation and in turn increases the likelihood of submitting more ideas of good quality.

Getting customers' motives: Lean on motivation theory for designing virtual ideas communities 205

FUTURE RESEARCH AND LIMITATIONS

Our results provide only a few examples for components that can be derived from our empirical tested motives. Certainly, there are a lot more to explore. As these components are derived by plausibility efforts, they have to be evaluated in a further step. Only when tested empirically one can make sure that hypothetically derived components are really leading to idea submissions. So, future research has to develop and apply empirical tests that prove effectiveness of each component in accordance to its corresponding motive.

One of the major limitations of this study involves the sample of the motivation survey. First, the sample size was relatively small. Despite the fact that the size was absolutely adequate for applied factor analysis as well as regressions analysis our results would be more meaningful with a higher sample size. Second, the proportion of students included in the sample is relatively high. Despite the fact that students can be considered as users of the SAP software applications, our results might impose some limitations concerning the generalizability. Future research should test and validate the model by collecting more data sets as well as data from a different composition of subjects consisting of more "typically" SAP users, like SAP consultants or accounting clerks working with SAP applications. research projects. His teaching and research areas include IT innovation management, service science, ubiquitous and mobile computing, collaboration engineering, e health, online communities, and IT management. You can contact him on leimeister@uni-kassel.de.

Fig. 1 .

 1 Fig. 1. Motivation model, adapted from Von Rosenstiel (2003)

Table 1 .

 1 Adapted motives

	Motive	Reference
	Fun	Contextualized from Hars/Ou (2002); Hertel/Niedner/Herrmann (2003); Lakhani/Wolf (2005); Shah (2005).
	Intellectual stimulation	Contextualized from Lakhani/Wolf (2005).
	Altruism	Contextualized from Hars/Ou (2002); Shah (2005).
	Reciprocity	Contextualized from Ghosh et al. (2002); Lakhani/Wolf (2005); Shah (2005).
		Contextualized from Ghosh et al. (2002); Hars/Ou (2002);
	Recognition	Hertel/Niedner/Herrmann (2003); Lakhani/Wolf (2005); Shah
		(2005).
	Identifying with the organizing firm	Developed in this research by building on Hars/Ou (2002); Hertel/Niedner/Herrmann (2003); Lakhani/Wolf (2005); Osterloh/Rota/Kuster (2002).
	Product improvement	Contextualized from Ghosh et al. (2002); Hertel/Niedner/Herrmann (2003); Shah (2005).
	Need	Contextualized from Ghosh et al. (2002); Hars/Ou (2002); Lakhani/Wolf (2005); Shah (2005).
	Learning	Contextualized from Ghosh et al. (2002); Hars/Ou (2002); Hertel/Niedner/Herrmann (2003); Lakhani/Wolf (2005).
	Contact to peers	Contextualized from Ghosh et al. (2002); Hertel/Niedner/Herrmann (2003).

Table 2 .

 2 Rotated component matrix

	Items					Components
	I attended the SAPiens	1	2		3	4	5	6
	ideas community
	because…
				1.		Fun
	… I have fun in working	0.065	0.660		0.268	0.065	0.117	-0.039
	out ideas and creative
	solutions. (S1)
	… I perceive composing	0.043	0.630		0.026	0.209	0.325	0.176
	creative ideas as a kind
	of self-realization. (S2)
	… I take much pleasure	0.255	0.785		0.203	0.107	0.118	0.030
	in being creative. (S3)
			2.	Intellectual stimulation
	… I'm stimulated by	excluded as item did not achieve critical MSA value
	generating	creative
	ideas. (IH1)
	… I'm intellectually	0.190	0.898		0.065	-0.023	0.082	0.135
	challenged	by
	developing	creative
	ideas. (IH2)
			3.	Altruism
	… I want to benefit	-0.106	0.360		0.569	0.203	0.300	0.141
	others by contributing
	an idea. (ALT1)
	… I want to make my	0.058	0.106		0.727	0.141	-0.020	0.162
	idea available to the
	general public without
	expecting any return.
	(ALT2)
			4.	Reciprocity
	… I believe that SAP	0.024	-0.050	0.468	0.161	0.261	0.508
	goes to the time and
	effort of developing the
	SAP software, so that I
	want to regive SAP my
	idea. (REZ1)
	… I want to reciprocate	excluded as item did not achieve critical MSA value
	to SAP as I use the SAP
	software gratis. (REZ2)

Table 3 .

 3 Values for Individual Item Reliability, Composite Reliability, and AVE

	Factor	Item	Individual Item	Composite	AVE
			Reliability	Reliability	(>/= 0.5)
			(>/= 0.4)	(>/= 0.6)
	Self-Marketing	MO_SM_1	0.557
		MO_SM_2	0.800
				0.860	0.608
		MO_SM_3	0.564
		MO_KZG_1	0.503
	Fun	MO_S_1	0.433
		MO_S_2	0.577
				0.871	0.639
		MO_S_3	0.828
		MO_IH_2	0.647
	Altruism	MO_ALT_1	0.490
		MO_ALT_2	0.493	0.778	0.552
		MO_CI_2	0.881
	Recognition	MO_ANER_1	0.677
		MO_ANER_3	0.927	0.860	0.676
		MO_ANER_4	0.424
	Product	MO_BEDA_1	0.725
	Improvement and Enhancement	MO_BEDA_2 MO_BEDA_3	0.427 0.647	0.781	0.574
		MO_PV_1	0.418
	Learning	MO_L_1	0.725
				0.698	0.536
		MO_L_2	0.626

Table 4 .

 4 Squared Multiple Correlations

			Squared Multiple Correlations
		Self-	Fun	Altruism	Recog	Prod Imp +	Learning
		Marketing			nition	Enh
	Self-		0.00289	0.0729	0.2401	0.0729	0.2704
	Marketing
	Fun	0.0289		0.0324	0.0225	0.00289	0.0324
	Altruism	0.0729	0.0324		0.0729	0.1156	0.1444
	Recognition	0.2401	0.0225	0.0729		0.1089	0.2116
	Prod Im + Enh	0.0729	0.00289	0.1156	0.1089		0.0441
	Learning	0.2704	0.0324	0.1444	0.2116	0.0441