Corporate culture and decision-making styles among marketing and sales professionals

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Master thesis

CORPORATE CULTURE AND DECISION-MAKING STYLES AMONG MARKETING AND SALES PROFESSIONALS

2015

Student: J.J. Beentjes (10475265) University of Amsterdam Faculty of Economics and Business Specialization: Marketing

Supervisor: dr. E. Peelen Second Reader: dr. U. Konus Master thesis MSc Business Studies Date submission: 31 January 2015

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List of tables and figures

Tables.

Table 1. Types of intuition (Chet Miller & Duane Ireland, 2005, p.22) 15

Table 2. Reliability analysis 38

Table 3. Bivariate correlations among cultures 39

Table 4. Bivariate correlations among culture and decision making 40

Table 5. Spearman’s rho decision making and decision outcomes 43

Table 6: summary of propositions 48

Figures.

Figure 1. Organizational cultures (Cameron and Quinn, 1999). 20

Figure 2. Research model 27

Figure 3. Importance of data 37

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Abstract

In today’s corporate climate, organizational decision making is undergoing a fundamental change; moving from a reliance on a leader’s ‘gut instinct and intuition’ to the use of increasingly data-based analytics. Data-driven decision making is of growing importance and attention, especially for Marketing and Sales professionals. Even though intuition based decision making is under pressure, data-driven decision making has not established a strong foothold everywhere. In addition, there is a growing body of evidence that suggest that culture affects the process of decision making in organizations in many ways. Differences in managerial decision-making styles among Marketing and Sales professionals have been investigated with a focus on the impact that corporate culture has on intuition based and data-driven decision making. Overall, this study demonstrates that a clan orientation significantly affected both data-driven and intuition based decision making. This study contributes to the ongoing discussion about which decision-making styles (intuition or data-driven) appear to be most efficient, taking into consideration that the two styles are very different. Results suggest that the impact that intuition based decision making has on the decision outcome depends on the usage of data-driven decision making. The results of this study show that incorporating both styles; intuition based decision making and data-driven decision making, will contribute to a positive decision outcome.

Key words: Corporate culture; data-driven decision making; intuition based decision making; decision outcome.

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1. Introduction

When looking at solving a Marketing and/or Sales problem, should the problem be approached on the basis of data or should it be done intuitively, or perhaps both? In addition, what type of role does corporate culture play in the way organizations make Marketing and Sales decisions?

Today’s organizations face overwhelming amounts of data, organizational complexity, rapidly changing customer behaviours, and increased competitive pressures (Court, Perrey, McGuire, Gordon and Spillecke, 2013). ‘Organizations are swimming in an expanding sea of data that is either too voluminous or too unstructured to be managed and analysed through traditional means’ (Davenport and Bean, 2012). Communication with customers and other stakeholders has become a dialogue in recent years, and the added complexities and the increasing amount of data make it necessary for corporations to make decisions more quickly. Marketing and Sales professionals are finding themselves in an environment where digital technology is becoming increasingly integrated into their work (Wierenga, Bruggen, Althuizen, 2008). They are required to use many different types of databases and analytical tools to compile and study data. This data will allow them to learn more about customers, select target markets for specified campaigns, customize conversations to measure value for the company, provide more specialized offerings for customers in order to monitor the market and plan marketing and sales events. Digital data is now everywhere, in every sector, in every economy, in every organization (Brown, Chui and Manyika, 2011) and almost everyone is a user of digital technology. In addition Court et al (2013) argued that this ‘goldmine’ of data represents a turning point for marketing and sales leaders. However the increasing amount of data and potential information (Bruggen, Smidts, Wierenga, 1998) creates a complex decision-making environment, especially for Marketing and Sales decision makers. ‘These complex decision-making environments may cause decision makers to lapse into reducing mental effort by using heuristics in decision making’ (Bruggen et al, 1998), such as intuitive judgment, stereotyping and common sense, at the cost of accuracy.

While the ‘Big Data’ topic once concerned only a few data geeks, ‘Big Data’ is now relevant for leaders across every sector, and consumers of products and services also stand to benefit from its application (Brown et al., 2011). Currently ‘Big Data’ and data-driven decision-making are growing in importance and attention (Provost & Fawcett, 2013). Literature provides diverse definitions, but most authors agree with the fact that: ‘Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision

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6 making, insight discovery and process optimization’ (Gartner, 2012). ‘A key tenet of big data is that the world and the data that describe it are constantly changing, and organizations that can recognize the changes and react quickly and intelligently will have the upper hand’ (Davenport, Barth, and Bean, 2013). The use of big data will also bring companies considerable challenges. Among these challenges is how to make effective managerial decisions (LaValle, Lesser, Shockley, Hopkins, Kruschwitz, 2013). McAfee and Brynjolfsson (2012) have identified five management challenges: leadership, talent managers, technology, decision making and company culture. These challenges include the need to ensure that the proper infrastructure is in place, that incentives and competition are active in order to encourage continued innovation, that the economic benefits to users, organizations and the economy are properly understood and that safeguards are implemented that will address public concerns about data (Brown et al., 2011). The cultural challenges and privacy concerns are enormous, but the trend is that facing these technical and organizational challenges head on pay off in the end (McAfee & Brynjolfsson, 2012). It is important to understand these challenges because decision-making is the basic managerial activity at all business levels.

Davenport et al. (2012) suggest that some data-based environments are better suited for real-time monitoring of the environment instead of automating decisions. In addition organizations need to create processes for determining when specific decisions and actions are necessary (Davenport et al., 2013).

The use of big data and data-driven decision making is associated with higher productivity and market value (Brynjolfsson et al, 2011; LaValle et al., 2011). Evidence was found that data-driven decision making is associated with certain measures that affect profitability (Brynjolfsson, Hitt and Kim, 2011; LaValle et al.,2011). McAfee and Brynjolfsson (2012) found evidence that using big data intelligently will improve business performance. Companies who performed better on objective measures of financial and operational results characterized themselves more as data-driven (McAfee & Brynjolfsson, 2012). In their research, McAfee and Brynjolfsson, (2012) found that companies that were in the top third of their industry and that used data-driven decision making were on average 5% more productive and 6% more profitable than their competitors.

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7 Data-driven versus intuition based decision making

The relationship between data-driven and intuition based decision making ‘gut feeling’ is complicated. Intuition based decision making is under pressure and data-driven decision making has not taken hold in many organizations yet. In today’s corporate climate, organizational judgment is in the midst of a fundamental change. It is moving from a reliance on a leader’s ‘gut instinct’ to increasingly data-based analytics (Brynjolfsson et al, 2011). At the same time, a data revolution has been taking place; firms gather extremely detailed data and propagate knowledge from their consumers, suppliers, alliance partners, and competitors (Brynjolfsson et al., 2011). Many marketing decisions are made in complex environments in which numerous variables affect decision outcomes. The market response to these variables is frequently nonlinear and incorporates carryover effects (Chakravarti, Mitchel and Stealin, 1981).

Isenberg (1984) argued that intuition is the smooth, automatic performance of learned behaviour sequences that can short-circuit step-wise decision making, thus allowing an individual to know almost instantly what the best course of action is. McAfee and Brynjolfsson (2012) suggested that using data enables managers to make decisions based on evidence rather than intuition. The availability of more and better data should offer opportunities for marketing decision-makers to make better founded decisions (Bruggen, Smidts and Wierenga, 2001). However, more data cannot unconditionally lead to better decision making (Bruggen et al.2001), unless managers learn how to exploit this data in meaningful ways (Lilien and Rangaswamy 1998 cited in Bruggen et al., 2001). A benefit of having more data is that it will positively affect the possibility of attaining decision accuracy (Bruggen et al, 2001). However, processing more data also requires more cognitive effort (Bruggen et al, 2001). Bruggen et al. (2001) argued that the data explosion is especially beneficial for marketers who are able to derive insight from this data and who are not vulnerable to decision biases.

The most intense application of data science and data mining have been seen in diverse areas such as direct marketing, online advertising, credit scoring, financial trading, help-desk management, fraud detection, search ranking, product recommendations, (Provost & Fawcett, 2013). Despite the trend towards data-driven decision making, Burke and Miller (1999) argued that further research should identify specific situations in which intuition works best. The literature suggests that there are several conditions under which intuition is more likely to be accurate

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8 (Salas, Rosen and DiazGranados, 2009). Characteristics of the decision maker (expertise), the decision task and the decision environment have been shown to influence both the tendency to use intuition as a basis for decisions as well as the accuracy of those intuitions (Salas et al., 2009). However, this might be different in situations when it is necessary to make a quick decision or when there are a number of people involved. Particular attention should be paid to decision type, the demographics of a firm’s workforce and management team, the decision maker’s profession or industry, and the nature of the corporate culture (Burke & Miller, 1999). Time pressure increases reliance on intuition primarily because decision makers simply do not have the time to engage in exhaustive search strategies that underlie purely rational models of decision making (Salas et al., 2009). In addition Pretz (2008) found evidence that analysis was an appropriate strategy for more experienced individuals, compared to a holistic, intuitive

perspective that worked best for novices. In the existing literature, many researchers have spoken about the cultural challenge of using data for decisions making (Davenport et al. 2012; McAfee and Brynjolfsson, 2012; Davenport, 2006). However, it is important to consider the influence that different cultural profiles have on the type of decision making that is used. Do certain corporate cultures use data-driven or intuition based methods or perhaps both?

This study will look at the nature of the corporate culture. No clarification was found in the literature that showed which types of cultural profiles (Cameron & Quinn, 1999) enhance data-driven decision making (Brynjolfsson et al, 2011) and/or intuition based decision making (Chet Miller & Duane Ireland, 2005). This gap in the existing literature can be filled by providing theoretical insight regarding the impact that different cultural profiles have on data-driven and intuition based decision making. This gap will be addressed by examining the relationship

between a firms’ cultural profile and the influence that this has on important Sales and Marketing decisions. Looking at the current corporate culture may offer managers an indication of the potential success they could experience using a data-driven decision making process.

Barton and Court (2012) argued that organizations should act on this data revolution by concentrating on targeted efforts to source data, build models, and transform the organizational culture. But what kind of influence does the culture of an organization have on the use of data and intuition? Currently, there is little understanding about whether an organization’s cultural profile has an influence on the decision-making style and finally on perceived decision outcome. Much of the literature refers to an organization’s culture, appearing to lose sight of the great

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9 likelihood that there are multiple organizational subcultures, or even countercultures that are competing to define the nature of situations within organizational boundaries (Smircich, 1983). This research is focused on the overall company culture of organizations. The purpose of this study is to gain a basic understanding of the influence that different cultural profiles have on data-driven and intuition based decision-making styles when making important Marketing and Sales decisions. This research will provide a better understanding of the relationship between the different cultural profiles and data-driven and intuition based decision making (outcomes). This study will use quantitative research. The sample used was comprised of small, medium and large enterprises. Individual decision makers who work in Marketing and Sales, received an online questionnaire. They were asked to define the overall culture of the organization. In addition, the questionnaire included questions about the latest important Marketing and/or Sales decision that Marketing and Sales professionals had made for their organization (in a team).

The next part of this study will explain the theoretical framework, along with the theory about managerial decision making, corporate culture and decision outcomes. The research design, analysis, results, and discussion along with implications for academics and marketers, limitations and suggestions for further research will also be discussed.

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2. Literature review

This research will examine the influence of corporate culture on decision making in small, medium and large organizations in the Netherlands. Different constructs, corporate culture, decision-making styles and decision outcomes will be explained further. The most relevant findings from the current literature about organizational cultures and decision-making style in Marketing and Sales will also be discussed.

2.1. Decision making

Decision making can be explained as the process of deciding something that is important to a group of people or an organization (Hon-Tat, Ai-Chin, Hooi, Rasli, Madi Bin Abdullah, Thean Chye, 2011). Certo & Certo (2005) (cited in Hot-tat et al. 2011) suggest that it is also the process of choosing the best alternative for reaching an objective. Essentially, everyone is a decision maker, because everything we do consciously or unconsciously is the result of some type of decision (Saaty, 2008). Decision making, for which we gather most of our information, has become a mathematical science; it formalizes the thinking that is used so that the actions that are taken to make better decisions are transparent in all aspects (Saaty, 2008). The information that is gathered will help individuals understand occurrences, in order to develop good judgements to make decisions about these occurrences (Saaty, 2008).

Previously, Scot and Bruce (1995) defined decision-making style as, ‘the learned, habitual response pattern exhibited by an individual when confronted with a decision situation’. Based on other research, Wierenga, Bruggen and Stealing (1999) identified three basic factors that characterize the decision situation with subcategories: (1) the problem that has to be solved (2) the decision-environment and (3) the decision maker who has to solve the problem. The decision problem (1) can be characterized by structure, depth of knowledge, availability of data and marketing instrument. The decision-environment (2) can be characterized by the level of market dynamics, organizational culture and time constraints. The decision makers (3) can be categorized by cognitive style, experience and attitude toward decision support systems (Wierenga et al. 1999). Business decisions are made in the context of a particular business strategy, a particular set of experience and skills, a particular culture and organizational structure, and a particular set of technology and data capabilities (Davenport, Harris, De Long, Jacobson, 2001). Scott and Bruce (1995) introduced five sorts of decision making; rational (‘I make decisions in a logical and systematic way’), intuitive (‘I make decisions I tend to rely on my intuition’), dependent (‘I often

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11 need the assistance of other people when making decisions’), avoidant (‘I avoid making important decisions until the pressure is on’) and spontaneous (‘I generally make snap decisions’). Thunholm (2004) suggested that the rational and the intuitive styles as defined by Scott and Bruce have a high similarity with the analytical and intuitive dimensions of the cognitive style. However for the other styles, the theoretical foundations are unclear (Thunholm, 2004). The spontaneous style as described by Scott and Bruce might perhaps be viewed as a kind of high-speed intuitive making style (Thunholm, 2004). The definitions of the dependent and the avoidant decision-making styles cannot be related to the analytical and intuitive classification on the information gathering or the information evaluations of cognitive style (Thunholm, 2004). The ‘Spontaneous’ style was an additional style, characterized by a sense of immediacy and the desire to make a decision as quickly as possible (Reyna, Ortiz and Revilla, 2014). Because research suggests that intuition may be integral to successfully completing tasks that involve short time horizons (Dane & Pratt, 2007) time pressure has been included as a different item and avoidant and spontaneous decision making were not added. The intuitive style emphasizes ‘a reliance on hunches and feelings’ (Scott and Bruce, 1995). An intuitive style characterized by an attention to details in the flow of information rather than a systematic search for and processing of information and those using an intuitive style have a tendency to rely on premonitions and feelings (Thunholm, 2004). The rational, dependent, reliance and spontaneous styles were not included. Because this research will focus on Marketing and Sales related team decisions, avoidant decision making was not included. It was decided that the intuitive style would be the focus because this study is centered on two decision-making styles, data-driven and intuition based.

The idea that marketing and sales decisions can be supported with analytical and mathematical models took off in the sixties of the last century (Wierenga, 2008, p.1). Before the sixties, marketing decisions were mainly based on judgment and experience (Wierenga, 2008, p.1). In the literature about data-driven decision making, researchers suggested that data based decisions enable managers to decide on the basis on evidence rather than intuition (McAfee & Brynjolffson, 2012). Davenport et al. (2001) suggested that most companies tend to focus on only technology and data or none of these. Unless executives consciously address the remaining contextual elements, they will find it difficult to improve their firm’s overall analytical capabilities (Davenport et al, 2001). Research suggests that the type of decision making used depends on several factors such as time pressure, (Davenport, 2009; Eisenhardt & Zbaracki, 1992; Hon-Tat et al., 2011),

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12 decision environment (Khatri and Ng, 2000; Miller and Ireland, 2005), kind of decision (Khatri & Ng, 2000; Isenberg, 1984; Dane & Pratt, 2007) and level of the organization in which the decision needs to be made (Eisenhardt & Zbaracki, 1992; Agor, 1987). In this study, a distinction will be made between data-driven and intuition based decision making. However this does not mean that these two constructs are contradictory.

Data-driven decisions. ‘The use of data to make decisions is, of course, not a new idea; it

is as old as decision making itself’ (Davenport, December 2013). ‘Using big data enables managers to decide on the basis of evidence rather than intuition’ (McAfee and Brynjolfsson, 2012). More detailed marketing data about more marketing variables are becoming available (Bruggen van et al. 2001). With vast amounts of data now available, companies in almost every industry are focused on exploiting data to gain a competitive advantage (Provost and Fawcett, 2013). The increased numbers of sources of data that have become available to marketers for their decision making have led to a situation in which potentially better market insight can be derived about the relationships between relevant marketing variables (Bruggen, van et al. 2001). ‘Enabled by the increased capacity of information technology, companies have set up (often huge) databases with records of individual customers’ (Wierenga, 2008). These databases are mostly part of Customer Relationship Management systems. With all the data, it is possible to measure and therefore manage more precisely than ever before (McAfee and Brynjolfsson, 2012). However, the acceptance and use of marketing decision models has been a continuing problem (Wierenga, 2008). Data-driven decision making (DDD) refers to the practice of basing decisions on data and business analytics rather than purely on intuition (Brynjolfsson et al, 2011). Supporters of data-driven decision making attempt to stress the importance of the data exploitation (Davenport, 2007; Brynjolffson et al. 2011). Supporters of data-driven decisions (McAfee and Brynjolfsson, 2012) suggested that data-driven decisions are better decisions comparing to decisions based on ‘gut feelings’. Decision makers should benefit from the availability of more and better data by incorporating the information derived from this data into their decision process (Blattberg & Hoch 1990). However, more data is not always better; too much data can make it more difficult to identify and make sense of the data that matters (Davenport & Prusak, 2005). All organizations need data and some industries are heavily dependent on it (Davenport & Prusak 2005). Record keeping is at the heart of these ‘data cultures’ and effective data management is essential to their success (Davenport & Prusak 2005). In an organizational context, data is most usefully described as

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13 structured records of transactions (Davenport & Prusak, 2005) and information can be described as a message (Davenport & Prusak, 2005). Information is meant to change the way the receiver perceives something, to have an impact on his judgment and behaviour (Davenport & Prusak, 2005). Information must inform and it is data that makes a difference (Davenport & Prusak, 2005). A growing number of firms are embracing the fact that data acts as a catalyst for analysis (Wierenga, 2008) by building strategically and tactically competitive strategies around their analytical capabilities and making decisions on the basis of data and analytics (Davenport, 2006). One of the most important tendency of previous years is that the individual customer has become the unit of analysis (Wierenga, 2008). Academic research suggests that companies that use data and business analytics to guide decision making are more productive and experience higher returns on equity than competitors that do not (Brown et al, 2011). Senior executives who wants to create data oriented cultures must do more than simply sponsor data-to knowledge initiatives (Davenport et al., 2001). They must set strong examples and insist that others make decisions and take actions based on data (Davenport et al., 2001). Brynjolfsson et al. (2011) found that firms that adopt data-driven decision making have output and productivity that is 5-6% higher than what would be expected given their other investments and information technology usage. The use of big data may offer traditional businesses greater opportunities for competitive advantage (McAfee & Brynjolfsson, 2012).

A study by Davenport (2006) found three key attributes among analytics competitors when he analysed companies that use analytics extensively and systematically to outthink and out execute the competition (Davenport & Harris, 2007). The first key attribute is the widespread use of modelling and optimization. Analytics competitors look well beyond basic statistics (Davenport, 2006). The second attribute is the enterprise approach. Analytics competitors understand that most business functions can be improved with sophisticated quantitative techniques (Davenport, 2006). ‘Analytics competitors apply technology, with a mixture of brute force and finesse, to multiple business problems. They also direct their energies toward finding the right focus, building the right culture, and hiring the right people to make optimal use of the data they constantly churn (Davenport, 2006)’.

Turbulent versus stable environment. Khatri and Ng (2000) suggested that data is more reliable in a stable environment rather than in an unstable environment. When firms are operating in stable markets, it is relatively easy to build mathematical models and perform some form of

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14 optimization (Wierenga, et al., 1999). Analytics might not be a good fit in situations when it is necessary to make a quick decision (Davenport, 2009). Almost all quantitative models, even predictive ones, are based on past data, so if one’s experience or intuition indicates that the past is no longer a good guide to the present and future, it may be necessary to employ other decision tools, or at least to create some new data and analyses (Davenport, 2009). In a stable environment, there is not much pressure to collect data quickly and perhaps data gathering is less costly (Khatri & Ng, 2000). Decisions based on facts may then achieve better performance than decisions based on judgments or hunches (Khatri & Ng, 2000).

In 1998 Bruggen et al. had already argued that the use of a marketing decision support system (MDSS) increases the effectiveness of marketing decision makers. An MDSS was effective because it assists it users in identifying the important decision variables and subsequently make better decisions based on those variables (Bruggen et al 1998). Wierenga and Oude Ophuis (1997) argued that as marketing decisions are becoming more complex, decision support tools are of increasing importance for management. Hoch and Schkade (1996) did research on the effect that the decision environment has on the importance of MMSS. They found that in a predictable environment, historical cases and a pattern matching strategy ultimately offered adequate support to decision makers. Their findings indicated that the degree to which a decision support tool is effective may depend on the decision environment. Bruggen et al. (1998) argued that a Marketing Decision Support System is useful in preventing the use of anchoring and adjustment heuristics, especially in situations in which decision makers are relatively inexperienced. Using the MDSS helps to prevent decisions that are based on avoiding risks (Bruggen et al. 1998). In some decision situations, a Decision Support System (DSS) can help managers make better decisions (Power & Sharda, 2007). Based on a quantified market response model, a data-driven support system can answer ‘what if’ questions (Lilien and Rangaswamy, 1998). These support systems use algebraic, decision analytic, financial, simulation, and optimization models to provide decision support (Power & Sharda, 2007). In today’s competitive, knowledge-based economy, organizations require the assistance of business intelligence (BI) tools to collect, analyse, and disseminate information so that knowledge workers are able to make informed decisions (Hedgebeth, 2007). Data technologies can now analyse sensor, geolocation, behavioral and social media data, furnishing organizations with innovative tools to better understand customers and markets, manage risk more effectively, and inform decision making (Big Data Survey Europe 2013). Business Intelligence

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15 applications support activities such as decision support, data mining, data warehousing, score carding, dash boarding, and financial analysis decisions (Hedgebeth, 2007).

Intuition based decisions. Organizations always need to make quick and accurate

decisions on a timely basis (Patton, 2003). Seebo (1993) (cited in Khatri & Ng, 2000) described intuition as subconscious, complex and quick. Intuition is not necessarily biased as presumed in previous research on rational decision making (Khatri & Ng, 2000). Intuition can be conceptualized in two distinct ways: as holistic hunch and as automatic expertise (Khatri & Ng, 2000). Intuition that is based on a holistic hunch corresponds to judgement or choice made through a subconscious synthesis of information drawn from diverse experiences (Chet Miller & Duane Ireland, 2005). Intuition that is based on automated expertise is less mystical, corresponding to recognition of a familiar situation and the straightforward but partially subconscious application of previous learning related to that situation (Chet Miller & Duane Ireland, 2005). Table 1 shows the summarized descriptions of intuition (Chet Miller & Duane Ireland (2005).

Intuition requires years of experience in problem solving and is founded upon a solid and complete grasp of the details of the business (Isenberg, 1984; Seebo, 1993). Davenport (2009) described decisions that are based on intuition as ‘relying on one’s gut and experience to make decisions. However according to Jedrzejczyk (2012), intuition is a mental ability that enterprise managers should possess, particularly the managers of the strategic and tactical fields. Khatri and Ng (2000) suggested that intuition is central to all decisions, even those based on the most concrete hard facts. Khatri and Ng (2000) argued that intuition is based on a deep understanding of the situation and is not an irrational process. It is a complex phenomenon that draws from the store of knowledge in our subconscious and is rooted in past experience (Khatri & Ng, 2000).

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16 Many executives and managers embrace intuition as an effective approach to important decisions (Chet Miller & Duane Ireland, 2005). Agor (1987) argued that managers with good intuition have a sense or vision of the future and are thus better equipped to move their organization in response to it, which allows them to see new possibilities in any given situation. These managers are particularly adept at generating new ideas and providing ingenious new solutions to old problems; usually they function best in rapidly changing environments or crisis settings (Agor, 1987). However, if decisions are only made intuitively, individuals would be inclined to believe that all types of information are useful and that the larger the quantity, the better (Saaty, 2008). Relying or over relying on intuitions in certain circumstances can be a source of error (Sales et al., 2009). Within organizations, intuition has been posited to help guide a wide range of critical decisions (Dane & Pratt, 2007). In their study, Khatri and Ng (2000) found intuitive synthesis to be an important part of senior managers’ strategic decision-making. They defined intuitive synthesis as a combination of experience, judgment, and ‘gut feeling’. Burke and Miller (1999) argued that intuitive decision making has had a bad reputation, the result of a prevailing lack of understanding, unfounded generalizations, and varying interpretations presented in the research literature (Burke & Miller, 1999). Miller and Ireland (2005) suggest that intuition can speed up decision making, which can be important in a complex, changing world. Khatri and Ng (2000) argued that scholars have emphasized rational decision making over intuitive decision making. Several authors argue that a theory of strategic decision making must consider both the rational and the intuitive processes (Khatri & Ng, 2000; Simon 1987). Previous research does suggest that senior or top managers often use intuition in decision making (Agor, 1987). In addition, Khatri and Ng (2000) found in their research that was focused on senior managers, that intuitive processes are often used in organizational decision making. In line with Andersen (2000) who found that the majority of the managers believed that intuition is effective approach. In addition, Khatri and Ng (2000) found intuitive synthesis to be an important strategy process factor which managers often exhibit in their strategic decision making. Experience and judgment variables were used extensively in strategic decision making. Thomas Peter and Robert Waterman, Jr. report in their best-selling book; In Search of Excellence that the ten best-run companies in America encourage the use of intuitive skills and nurture its development in their management cultures (Agor, 1987). Khatri & Ng (2000) stated that the use of intuitive synthesis was found to be positively associated with organizational performance in an unstable environment, but negatively so in a stable

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17 environment. However, Miller and Ireland (2005) suggest that managers should consider using automated expertise only when two conditions are met: (1) exploitation of existing strategies and technologies is the goal; and (2) time or other resource constraints clearly prevent raising knowledge to an explicit level.

Several studies found several factors in which intuition is most useful. Agor (1987) argued that that intuition is most useful in particular circumstances. He identified the following conditions as those under which intuitive ability seems to function best: (a) High level of uncertainty, (b) little previous precedent, variables are less scientifically predictable, (c) there are no facts or facts are limited, facts don’t clearly point the way to go, (d) analytical data are of little use, when several plausible alternative solutions exist to choose from. In addition, Burke and Miller (1999) argued that incorporating intuition into decision making appears relevant and applicable in the following scenarios; when decisions need to be consistent with the organization’s culture and values, when time is of the essence (Oblak & Lipuscek, 2003; Khatri & Ng, 2000), when explicit cues are lacking because policies, rules, guidelines, or expert guidance is absent, when uncertainty prevails because of new product planning or strategy formulation, when quantitative analyses require a check and balance. In addition, the findings from Khatri and Ng (2000) suggest that intuition should be used cautiously and less often (perhaps, in combination with rational analysis) in a stable and moderately unstable environment, but more often in a highly unstable context. Intuition may be integral to successfully completing tasks that involve high complexity and short time horizons, such as corporate planning, stock analysis, and performance appraisal (Isenberg, 1984; Dane, and Pratt, 2007). According to Miller and Ireland (2005) intuition can speed up decision making, which can be important in a complex, changing world. It looks as though this might depend on the kind of decision the organization needs to make; complexity of the decision, time horizon and the amount of people involved.

Agor (1987) conducted a quantitative study of over 2,000 managers in the private and public sectors. He found that the intuitive ability varied by managerial level. Managers at the top in every organization that was studied scored higher than middle or lower-level managers on their ability to use intuition to guide their key decisions (Agor, 1986). The follow up study suggested that top executives value intuition as a tool for managerial decisions (Agor, 1987). In the research from Agor (1987), the sample of top executives studied strongly believed that intuition was one of the skills they used to guide their most important decisions (Agor, 1987). Intuition may be thought

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18 of as a cognitive conclusion based on a decision maker’s previous experiences and emotional inputs (Burke & Miller, 1999). Burke and Miller (1999) found that intuition plays a significant role in a decision maker’s daily work life, and the overwhelming majority of participants in their research use intuition to some degree in making workplace decisions. They found that employees who have more experience, who are older or who hold managerial positions tend to use their intuition more (Chet Miller & Duane Ireland, 2005). In line with Chet Miller and Duane (2005) the research from Pretz (2008) suggests that more experienced respondents would prefer the intuitive perspective over the analytical perspective. Intuition appears to be a characteristic of many managers (Aarum Andersen, 2000).

McAfee and Brynjolffson (2012) believe that ‘throughout the business world today, people rely too much on experience and intuition and not enough on data’. Davenport et al. (2001) argued that data-driven decisions tend to reflect a reality that is often overlooked by organizations, especially when decisions are based on experience or intuition alone (Davenport et al.2001). According to Davenport (2009), intuition based decisions are easy and require no data; the subconscious can be effective at weighing options but he argued that it is typically the least accurate approach to making decisions and decision makers are easily swayed by context (Davenport, 2009) The first question a data-driven organization asks itself is ‘What do we know?’ instead of ‘What do we think’ (McAfee & Brynjolfsson, 2012). An intuitive style reflects a strong reliance on one’s gut and experience rather than on a systematic search for and processing of information (Thunholm, 2004). This might lead to the following proposition.

Proposition 1: Data-driven decision making will be negatively related to intuition based decision making.

2.2 Corporate culture

The word culture is most commonly reserved for societies or for ethnic or regional groups, but it can also be applied to other human collectives or categories: an organization, a profession, a family (Hofstede, 1980/1981). The culture concept has been borrowed from anthropology, where there is no consensus on its meaning (Smircich, 1983). The notion of organizational culture has been important in the study of organizational behavior for the past decade (Barley, Meyer and Gash, 1988; O'Reilly, 1989; Smircich, 1983). The management literature is peppered with studies of organizational culture (Deshpandé, Farley andWebster, 1993) and according to Schein (2004) the

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19 concept of culture has been the subject of considerable academic debate in the last twenty-five years. A number of these concepts relate to culture or follow culture in that they deal with things that members of a group share and/or hold in common, however Schein (2004) suggested that ‘none of them can usefully be thought of as ‘the culture’ of an organization’. Most authors agree that ‘corporate culture’ can be defined as a complete set of values, beliefs, assumptions, and behavioral patterns that organizational members have in common and that form the core identity of an organization (Denison, 1990; Cameron & Quinn, 1999; Deshpandé & Farley, 2004; Barney, 1986). Schein (1985) defined corporate culture as ‘a pattern of shared basic assumptions that was learned by a group as it solved its problems of external adaption and internal integration, that has worked well enough to be considered valid and, therefore, to be taught to new members as the correct way to perceive, think, and feel in relation to those problems’. Schein (1996) made a distinction between three levels of culture (degree to which the cultural phenomenon is visible to the observer); artifacts, espoused beliefs and values and underlying assumptions. ‘When one brings culture to the level of the organization and even down to groups within the organization, one can clearly see how culture is created, embedded, evolved, and ultimately manipulated, and, at the same time, how culture constrains, stabilizes, and provides structure and meaning to the group members’ (Schein, 2004, p.1). The subculture of an organization reflects national culture, professional subculture, and the organization’s own history (Hofstede, 1980/1981).

Hofstede (1980) argued that culture affects organizations in the decision-making processes. Culture affects not only the alternatives that are considered but also the actual choice among them (Hofstede, 1980). According to Yrjo-Koskinen et al. (2010) there is a growing body of evidence that suggests that culture affects the process of decision making in organizations in many ways. An organization is postulated to have a ‘strong culture, which is usually defined to be widely shared among employees (Lee & Yu, 2004). Culture is both a dynamic phenomenon that encloses us at all times, being constantly achieved and created by our interactions with others and shaped by leadership behavior and a set of structures, routines, rules and norms that guide and constrain behavior (Schein, 2004, p.1). According to Barney (1986) a firm’s culture can be a source of sustainable competitive advantage if that culture is valuable, rare and imperfectly imitable.

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20 Quinn and Rorhbaugh (1983) developed the ‘Organizational Culture Assessment Instrument’ (OCAI) for measuring the culture of an organization. The OCAI is based on the Competing Values Framework (CVF) from Cameron and Quinn (1999). In the model, they use different dimensions: flexibility and discretion versus stability and control, and external focus versus internal focus and integration. Corporations commonly use these dimensions and six characteristics of the organization-dominant characteristics; organizational leadership, management of employees, organizational glue, strategic emphases and criteria of success (Naranjo-Valencia et al, 2011). In the case of subcultures, it might be possible to have another dominant culture within one department that does not fit with the overall corporate culture. This model defines four types of organizational cultures that show how a corporate culture can be characterized. Cameron and Quinn (1999) proposed a model that defined four cultures – adhocracy, clan, market and hierarchy (depicted in figure 1).

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21 Clan culture refers to an organization that is focussed on the relationships between people, connected to flexible processes that cares for employees (as a part of the family) and client sentences. In addition, collegiality of decision making is central (Cameron & Quinn, 2006, P. 175). The company’s orientation is focused on collaboration, the relationship between people is central and the development of employees is very important. A clan binds the group with loyalty and tradition. A leader in a clan is a stimulator, mentor and/or father figure. Effectivity criteria are focused on cohesion, morale and development of human resources. The organization has a few layers, making employees work in every department. Top managers tend to have higher clan scores (Cameron & Quinn, 2006, p.79). An important finding from Khatri and Ng (2000) was the admittance by senior managers that they rely on ‘gut-feelings’ in strategic decision making. Because the clan is focused on developing human resources and its leaders act as a parent figure, the following proposition has been made:

Proposition 2a: There is a negative relationship between clan orientation and data-driven decision making.

Proposition 2b: There is a positive relationship between clan orientation and intuition based decision making.

The orientation of the adhocracy culture lies in creativity and the external positioning is central. Adhocracy organizations are characterized by an ability to change and be adaptable when new opportunities are created. Leaders within the adhocracy are innovators, they experiment and innovate. Flexibility and individualism are important drivers. Naranjo-Valencia et al. (2011) found that organizational culture can influence the innovation and imitation orientation of the firm both positively and negatively. Naranjo-Valencia et al (2011) found that the adhocracy cultures foster an innovation orientation. Adhocracy organizations are looking for the newest features, growth opportunities and creativity. The focus lies on futurism and management of continuous improvement. It might be possible that the adhocracy culture is using data-driven decision making because they have an orientation toward innovation and entrepreneurship whereby using new resources is central. Incorporating data-driven decisions include the need to ensure that the correct infrastructure has been established and that incentives and competition are in place to encourage continued innovation (Brown, et al. 2011). In order to collect and process data, a company must

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22 have the appropriate technology and tools (Davenport, 2009) because using new resources (characteristic of Adhocracy culture) might be positively related to using new technologies and tools for data-driven decisions. Entrepreneurship might also be related to intuition decisions because organizations will make decisions that they think that are good. It may lead to quick decisions, whereby analytics might not be a good fit (Davenport, 2009). Because the dominant attributes of the adhocracy culture; entrepreneurship, creativity and innovation can result in both intuition and data based decision making, the following proposition has been made:

Proposition 3a: There is a positive relationship between adhocracy orientation and data-driven decision making.

Proposition 3b: There is a positive relationship between adhocracy orientation and intuition based decision making.

The orientation of the hierarchy culture is focused on control. Internal control is maintained by rules, specialized jobs and centralized decisions (Cameron & Quinn, 2011). In the hierarchy culture, leaders are coordinators and formal rules and procedures are central. Stability and control are important factors. They are characterised by formalized and structured workplaces. An example of a hierarchy is most government institutions. Naranjo-Valencia et al. (2011) found that hierarchy cultures are associated with imitation. It might be possible that the hierarchy is less focused on data-driven decision making and possibly more focused on the intuition based decision making because of the overall status of data-driven decision making. In a hierarchy, decisions are centralized, in addition it might be possible that a Hierarchy does not encourage sharing of information, which is necessary for data-driven decision making (LaValle et al, 2011). In addition, several researchers (Chet Miller and Duane Ireland, 2005; Pretz, 2008) found that employees who have more experience, who are older, or who hold managerial positions tend to use their intuition more. The following proposition has been made:

Proposition 4a: There is a negative relationship between hierarchy orientation and data-driven decision making.

Proposition 4b: There is a positive relationship between hierarchy orientation and intuition based decision making.

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23 The orientation of the market culture is focused on competition and productivity. The external focus of the company is focussed on relationships. In addition, the need for control and stability is high. Leaders within a market culture are hunters and are focused on reputation and success. Beating the competition and becoming a market leader is very important. Organizations with a market culture are mostly multinationals or fast growing organizations. The strategic emphasis of a market culture lies on creating a competitive advantage and market superiority. As data-driven strategies take hold, they will become an increasingly important part of a competitive advantage (Barton & Court, 2011). The following proposition has been made:

Proposition 5a: There is a positive relationship between market orientation and data-driven decision making.

Proposition 5b: There is a negative relationship between market orientation and data-driven decision making.

Several authors argue that organizations with ‘strong’ cultures are apt to be more successful (Deal & Kennedy, 1982; Barney; 1986). In their research, Deshpande et al. (1993) found that the ‘market cultures’ were associated with the best performance. They also found that (Japanese) companies with corporate cultures that stress competitiveness (markets) and entrepreneurship (adhocracies) outperformed those dominated by internal cohesiveness (clans) or by rules (hierarchic).

Davenport (2009) argued that culture is a soft concept and that analytics is a hard discipline. Davenport (2006) suggested that employees within data-driven organizations are approved to base their decisions on hard facts. In an analytics culture, there is sometimes stress between innovative or entrepreneurial impulses and the need for evidence (Davenport, 2006). Creating a culture that values data-driven decision making is an ongoing and highly challenging task (Davenport et al., 2001). It is vital to maximizing an organization’s analytic capabilities Davenport et al. (2001) argued that organizations that want to progress on several fronts in turning data into knowledge may need to transform the culture to more of a data orientation and inculcate the analytical skills. 'These skills can be externally sourced in the short term (Davenport et al., 2001), but need to be present internally for an organization that is committed to data-driven decision making (Davenport et al., 2001).

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2.3 Decision Outcome

According to Khatri and Ng (2000) a broader view of organizational performance, includes indicators of non-financial performance in addition to those of financial performance.

Decision problems often involve a choice between two or more uncertain options (Ritov, 1996). Normally people expect to find out what the result of the choice will be (Ritov, 1996). Often, uncertainty is the source of the difficulty (Simon, 1987). ‘An outcome is a state of affairs that exists as a consequence of a given alternative having been chosen by a (strategic) decision maker’ (Harrison & Pelletier, 1997). Each choice may have a good outcome under one set of environmental contingencies, but a bad outcome under another (Simon, 1987). Decisions might have several outcomes, not only financial ones, but they might have several outcomes such as regret

(Inman & Zeelenberg, 2002), perceived decision quality (Dooley & Fryxell, 1999) and justifiability (Inman & Zeelenberg, 2002).

In point of fact, the outcome that a (strategic) decision maker should seek is one that simply meets the (strategic) objective (Harrison & Pelletier, 1997). There is no requirement to exceed a given objective (Harrison & Pelletier, 1997). If a higher level of attainment is sought, simply escalate the objective, but not behind the point of attainability (Harrison & Pelletier, 1997). This variation is called a satisficing outcome, and it is recommended for most strategic choices (Harrison & Pelletier, 1997). Satisficing outcomes normally result from a judgemental decision making strategy in which the decision maker accepts the reality of imperfect information regarding the outcome of a given choice (Harrison & Pelletier, 1997).

Decision research has taken the role that emotions play in choices and decisions seriously. Regret is the decision emotion that has received the most research attention (Connolly & Zeelenberg, 2002). Regret and disappointment are emotions that can be experienced in response to an unfavourable outcome of a decision (Dijk, Pligt, Manstead, Empelen and Reinderman, 1998). After making a decision under uncertainty, a person may discover, upon learning the relevant outcomes, that another alternative would have been preferable (Bell, 1982). This knowledge may include a sense of regret. This is especially true if it is learned that the alternative course of action would have resulted in a more favourable outcome (Zeelenberg, Beattie, Pligt and Vries, 1996). Regret is an emotion that every person feels at some point in their lives. Regret is a more or less painful cognitive and emotional state of feeling sorry for misfortunes, limitations, losses, transactions, shortcomings or mistakes (Landman, 1993). This is in line with Sugden (1885) (cited

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25 in Inman and Zeelenberg, 2002) who defines regret as ‘the painful sensation of recognizing that ‘what is’ compares unfavourably with ‘what might have been’’. In addition, Bell (1985) defined regret as a psychological reaction to making the wrong decision. Most people can readily recall or imagine situations in which a poor decision led to painful regret (Connolly & Zeelenberg, 2002). Ritov (1996) found that in choosing between uncertain options, people are more inclined to compare outcomes per states of the world if they expect to learn what would have happened with each option.

Decision outcome and data-driven decision making. Data-driven decisions tend to be better

decisions (McAfee & Brynjolfsson, 2012). According to Davenport et al. (2001) data-driven decisions tend to reduce bias and unreliable human traits in decision making. Data-driven decisions tend to be more consistent, impersonal, and more cost efficient to replicate, transfer, and leverage (Davenport et al 2001). Davenport et al. (2001) suggest that data-based decisions can be modelled to predict the future, helping managers make decisions that are timely and responsive to their organization’s environment.The following proposition has been made:

Proposition 6a: Data-driven decision making is positively related to decision quality, risk perception, justifiability and perceived goals.

Proposition 6b: Data-driven decision making is negatively related to regret.

Intuition based decision making and decision outcome. Chet Miller and Duane Ireland (2005)

showed that employees who have more experience, tend to use their intuition more (Chet Miller & Duane Ireland, 2005). In addition, Pretz (2008) found that more experienced respondents would prefer the intuitive perspective over the analytical one. However, in the research from Burke and Miller (1999) two-thirds of their respondents felt that intuition led to better decisions and 12 percent reported no effect on their decision quality. Aarum Anderson, J. (1999) argued that intuition as a decision-making style appears to be related to organizational effectiveness. The following proposition has been made:

Proposition 7a: Intuition based decision making is positively related to perceived decision quality, risk perception, justifiability and perceived goals.

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Data-driven decision making, intuition based decision making and decision outcome. Many

executives and managers embrace intuition as an effective approach to important decisions (Chet Miller & Duane Ireland, 2005). However, if decisions are only made intuitively, one is inclined to believe that all types of information are useful and that the larger the quantity, the better (Saaty, 2008). Relying or over relying on intuitions in certain circumstances can be a source of error (Sales et al., 2009). The following proposition has been made:

Proposition 8a: Data-driven decision making will result in a more positively perceived decision outcome than intuition based decision making

Proposition 8b: Data-driven decision making will result in less regret than intuition based decision making.

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3. Research design

In order to answer the research question, an explorative study was conducted using a deductive research approach. This involved testing propositions by using a research strategy specifically designed for the purpose of its testing (Saunders and Lewis, 2012). Eight propositions were measured to examine relationships between corporate culture, decision-making style and decision outcome (of Marketing and Sales employees in the Netherlands). The research design will

provide a description of the procedure and research setting, the sample description and measures of variables. Finally, a brief description will be given that details the statistical approach that was taken in order to test the relationships in the proposed model (Figure 2).

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3.1 Procedure and research setting

An online, sectional questionnaire was used to collect the data. The survey was cross-sectional for several reasons; the intent was to measure the current culture and decision-making style and not necessarily the change in decision making or culture over time. The survey strategy is associated with a deductive research approach (Saunders, Lewis, and Thornhill, 2012). It is a popular and common strategy in business and management research and is most frequently used to answer ‘what’, ‘who’, ‘where’, ‘how much’ and ‘how many’ questions (Saunders, Lewis, and Thornhill, 2012). Data collected using a survey strategy can be used to suggest possible reasons for particular relationships between variables and to produce models of these relationships (Saunders, Lewis, and Thornhill, 2012). The focus of this research was to study the relationships between corporate cultural profile, decision-making style and decision outcomes.

A probability sampling method was used, to ensure generalizability (Saunders, Lewis, Thornhill, 2012). All choices for choosing a sample method depends on the ability to gain access to data. Increasingly, researchers are inviting potential participants using a variety of electronic media such as intranets, blogs, and bulletin boards in addition to invitation via general letters or all user emails (Saunders, 2011).

The data was collected using an interesting database from an employment agency in the Netherlands. In addition online networks were used. The database consisted of national and international companies, small, medium and large companies in the Netherlands from diverse industries and sectors. Contact information was already available and it was possible to directly send an e-mail to the companies. Companies that were selected for our sample had twenty or more employees.

An introductory letter that explained the nature of the study and included a link to the questionnaire was sent by e-mail to all contact persons in the database. They received a request to either complete the questionnaire or if they did not belong to the requested population, share the questionnaire with one of the Marketing or Sales people within the organization.

To avoid reaching people who did not want to be involved in some way or who were not representative of the population, a selection was first made and the decision was made to only access our network and the network of another student who had an interesting network of marketing professionals. The networks included people who have been working within different organizations in several industries. In addition the survey was shared by other people in their Facebook and

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29 LinkedIn. Because we knew some of the people in the sample we wanted to raise the response rate. We approached the respondents via LinkedIn by a private message with an introductory letter. Thirty six people in the network, who we spoke weekly were also asked in person or by telephone to fill in the questionnaire to raise the change that they started to fill it in. Because we knew them in person it may raise the change that they filled in the survey. Saunders and Lewis (2012) suggest that it is easier to use any existing contacts when you have to get access to an organization than in the case of a ‘cold approach’. Because we know them the probability that they filled in the questionnaire was higher.

Because data was collected in a standard manner, it was important to ensure that questions were expressed clearly so they were understood in the same way (Saunders, Lewis, Thornhill, 2012).A pilot study was conducted to refine the questionnaire to ensure that respondents would not have problems understanding or answering questions and have followed all instructions correctly (Saunders, Lewis, Thornhill, 2012). Because the number of people in a pilot test should be sufficient to include any major variations in the population that are likely to affect responses (Saunders, Lewis, Thornhill, 2012). The questionnaire was sent to five other part-time students who were similar in characteristics of the sample.

The questionnaire was conducted in Dutch. The items used to measure ‘corporate culture’ were already available in Dutch. The items of ‘decision-making style’ and ‘decision outcome’ were obtained from English studies. The original questions from standard scales were translated from English to Dutch. The translation was done by a graduate student of the Vrije Universiteit, course English. The purpose of the translation was to translate the questions from English to Dutch in short and simple questions. The Dutch questionnaire was back translated by two other students of the University of Amsterdam. See the appendix for the complete questionnaire (in Dutch).

3.2. Sample

To conduct the research participants were needed. The population consisted of people within the Netherlands who are working in Marketing and Sales, within different organizations across different levels in the organization. In the second quarter of the year two third of the population of the Netherlands belong to the workforce population. In total it were 7.2 million people. All these people had a minimum of twelve hours work a week and were between 15 and 65 years old. In 2014 there were spacious 1.4 million organizations in the Netherlands. When we take out the Sole Proprietorship organizations and only select on organizations who have twenty or more employees

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30 we get a population of 31.765 organizations in different industries and sectors. Because the change that there is somebody in the organizations who does something within marketing might be smaller in organizations with a few people we take only organizations with twenty or more people into account.

The sample consisted of professionals employed in various industry segments over the Netherlands (small, medium and large enterprises). All participants had Marketing and/or Sales background in common.

3.3. Measures

Culture (4 variables), decision-making style (2 variables) and decision outcome (5 variables) were the three constructs included in this study. To ensure validity we mainly used validated measures for the literature. To establish reliability we used measures with demonstrated reliability or multiple items were included.

Corporate culture. The Organizational Culture Assessment Instrument (OCAI) developed

by Cameron and Quinn (1999) was adapted to measure corporate culture. The OCAI was used for the distinction between different cultural profiles. The OCAI is a validated instrument (Howard, 1988; Quinn & Spreitzer, 1991 in Naranjo-Valencia et al. 2011) and used by thousands of companies worldwide. It has been studied and tested in organizations for more than twenty five years by a group of thought leaders from leading business schools and corporations (Quinn & Rohrbaugh, 1983; Quinn & Cameron, 1983; Quinn, 1988; Cameron & Quinn, 2005). The purpose of the OCAI is to assess six key dimensions of organizational culture. The OCAI consists of six questions, including four items each. Using OCAI, Cameron and Quinn (1999) proposed a model that defines four cultures – adhocracy, clan, market and hierarchy. All six dimensions of organizational culture the OCAI proposes were adapted in this study: (1) dominant characteristics, (2) management of employees, (3) organizational glue, (4) criteria of success, (5) leadership style and (6) strategic focus. Yu and Wu (2009) mentioned the following advantages of the CVF and its matched scale OCAI compared with other models and scales: (1) It is empirically validated in cross-cultural research (Yu, T. and Wu, 2009): A large amount of studies have established the reliability and validity of the CVF and OCAI (Howard, 1998; Ralston et al., 2006 in Yu & Wu, 2009). (2) The questionnaire of OCAI includes only 24 items thus are very convenient for practical operations (Yu & Wu, 2009). According to Cameron and Quinn (2002) OCAI is a practical designed instrument which is in a short time implementable. The total questionnaire of the OCAI

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31 includes questions about the current and the preferred organizational culture. In this study we were only interested in measuring the current culture of the organization, only questions for current culture were adapted. The participants were asked to divide 100 points over four alternatives that correspond to the four culture types (six dimensions), according to the organization they work in. It was not necessary to translate the questions because we have adapted the Dutch version of the questionnaire from OCAI-online and Ask Advise.

Decision making. Decision making variables (used as dependent and independent variable)

were all measured on a seven-point Likert-type scale, anchored at 1, ‘strongly disagree’ to 7, ‘strongly agree’. Decision making was measured by the latest Marketing and/or Sales related group decision (minimum of two people) within the organization. A group decision was chosen because decisions are seldom made by chief executives alone (Harrison, and Pelletier, 1997). In this research data-driven decision making and intuition based decision making were included. Data-driven decision making. In the absence of an existent data-driven decision scale, a reliable scale to measure data-driven decisions was developed. Based on data-driven decision making literature, the measurement scale for data-driven decision making was comprised of six items. The items measured the usage of evidence and analyses by making a decision. Brynjolfsson et al. (2011) developed a measure, based on three questions, of data-driven decision making that rates how strongly firms use data across the company. In this research one of the questions was adapted and adjusted to the decision making context of this research. The other questions were not in line with the context of this research.

Intuitions based decision making. Intuition based decision making was measured by adapting five intuitive style items from the Decision-making style Inventory (DMSI) developed by Scott and Bruce (1995). The General Decision-Making Style Inventory (Scott & Bruce, 1995) is one of the most widely used measures for decision-making styles in judgment and decision-making literature (Curseu & Schruijer, 2012). Scott and Bruce (1995) reported high levels of internal consistency, face validity and a factor structure. The DMSI determines an individual’s preferred decision-making style. The original instrument consists of 25 items, scored on a five-point Likert-type scale, with five items identified for each style. It measures five different decision styles; Rational decision making (thorough information search and logical evaluation of alternatives. Cronbach’s Alpha; .77 to .85), Intuitive decision making (Reliance on gut feelings and hunches: Cronbach’s Alpha; .78 to .84), Dependent decision making (advice seeking and reliance on others.