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Faculty of Economics and Business
MSc Business Studies – Amsterdam Business School
Do investments in Google Marketing Tools affect performance and
how rational are decision makers?
Author: Rutger Bouwman Student number: 10278621
First supervisor: Dr. ir J. Kraaijenbrink (Jeroen) Second supervisor: Dhr. dr. M.P. Tempelaar (Michiel)
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Table of content
Abstract ... 6
The research question ... 6
Scope ... 6
Results ... 6
Meaning and implications... 7
Chapter 1: Introduction ... 8
1.1 Practical study motive ... 9
1.2 Theoretical study motive ... 9
1.3 Relevance of this study ... 10
1.4 Thesis outline ... 12
Chapter 2: Literature review ... 12
2.1 Google Marketing Tools ... 12
2.1.1 Introduction to online marketing ... 12
2.1.2 Google Marketing Tools ... 13
Conclusion ... 14 2.2 Performance... 15 2.2.1 Google Analytics ... 16 Conclusion ... 18 Performance hypothesis ... 19 2.3 Decision making ... 19
2.3.1 Rationality and bounded rationality ... 20
Conclusion ... 23
2.3.2 Introduction to Utility Theory... 24
Relevance of expected utility and heuristics ... 26
Conclusion ... 27
Expected utility hypothesis ... 27
2.3.3 Introduction to heuristics and biases ... 28
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Most influential authors ... 30
Heuristics and biases in organisational context ... 31
Conclusion ... 32
2.3.4 Introduction to anchoring and adjustment heuristic ... 32
Relevance of anchoring and adjustment heuristic ... 33
Conclusion ... 35
Anchoring and adjustment heuristic hypothesis ... 36
2.3.5 Introduction to sunk cost fallacy ... 36
Relevance of sunk cost fallacy ... 37
Conclusion ... 38
Sunk cost fallacy hypothesis ... 38
2.3.6 Introduction to confirmation bias ... 39
Relevance of confirmation bias ... 40
Conclusion ... 40
Confirmation bias hypothesis ... 41
2.4 Conceptual model ... 41
Chapter 3 Methodology ... 41
3.1 Sample description... 42
3.2 Procedure ... 42
Pre-testing the questionnaire... 43
3.3 Measurement scales ... 43
3.3.1 Dependent Variable ... 43
3.3.2 Independent Variables ... 44
3.4 Data analyses ... 45
3.5 Strengths and limitations questionnaire method ... 46
Chapter 4 Results ... 46
4.1 Preparing data for analysis ... 46
4.2 Descriptive statistics ... 47
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4.4 Hypotheses testing ... 48
4.5 Summary ... 66
Performance ... 66
Expected utility ... 67
Anchoring and adjustment heuristic ... 67
Sunk cost fallacy ... 67
Confirmation bias ... 68
Overall results ... 68
4.6 Final model ... 69
Chapter 5: Discussion ... 69
5.2 Findings and implications ... 69
Performance ... 69
Expected utility ... 70
Anchoring and adjustment heuristic ... 70
Sunk cost fallacy ... 70
Confirmation bias ... 71
Theoretical implications ... 71
Managerial implications ... 71
5.2.1 Limitations and further research ... 72
Assumptions ... 72
Research method ... 72
Performance ... 73
Decision making ... 74
Further research on rationality ... 75
References ... 77
Appendix ... 91
Descriptive statistics of variables ... 91
Flowchart data sample set composition ... 92
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T-tests ... 105
Reliability... 106
Correlation analyses ... 107
Significance and correlation summary ... 109
Regression analyses presuppositions ... 111
Expected utility multicollinearity check ... 111
Anchoring and adjustment heuristic bias multicollinearity check ... 111
Sunk cost fallacy multicollinearity check ... 113
Confirmation bias multicollinearity check ... 113
Normality and linearity check ... 113
Histograms Performance ... 113
Histogram & scatterplot expected utility ... 116
Histogram & scatterplot anchoring and adjustment ... 116
Histogram & scatterplot Sunk Cost ... 117
Histogram & scatterplot Confirmation bias ... 118
Questionnaire in Dutch ... 118
Questionnaire in English ... 122
Introduction ... 122
Personal information ... 123
Expected utility ... 123
Sunk cost heuristic ... 124
Anchoring and adjustment fallacy ... 125
Confirmation bias ... 126
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Abstract
This study investigated the effect of investment decision making in Google Marketing Tools (GMT) and the related performance. A conceptual model has been presented that aligned the most relevant aspects of decision making with GMT and performance outcome. First the theory on Google Marketing Tools, performance and decision making is discussed to clarify the
importance of this study and the available research on these topics.
The research question
Variables in the research question have been subject in many studies and theories. Connecting the variables has not been part of research until now. Diving deeper into existing theory and research it got clear that there are many aspects influencing the decision making process of investing in GMT. Existing research in the field of decision making and rationality narrowed the scope of this study.
The applied focus structures the study. Dominant aspects of decision making: rationality, expected utility theory, heuristics and biases have been analysed.
Scope
Four variables are analysed for their effect on investment decision making in GMT and
performance. Expected utility is one of the independent variables. In this study expected utility was responsible for measuring rational decision making.
From the heuristics and biases program three relevant variables are selected; the anchoring and adjustment heuristic, sunk cost heuristic and confirmation bias. These variables try to explain why people make decisions that are not or less in line with the expected utility theory. These three variables aim to measure deviations from rational decision making.
Results
This study indicates that decision makers act less rational as formulated in the expected utility theory, but instead are influenced by heuristics and biases. Findings of this study are that expected utility and confirmation bias have no significant effect on investment decision making in GMT and performance. In some scenarios the sunk cost fallacy and anchoring and adjustment heuristic variables significantly effect investment decision making in Google Marketing Tools and performance. The sunk cost heuristic has most often been reported for its positive relationship with investment decision making in GMT and performance.
Investing in GMT showed a general positive effect on performance. The performance variable has been divided by the categories profit, turnover, leads and traffic. A detailed analyses on the
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performance effects showed that investing in GMT had the most effect on website traffic. Traffic showed the highest positive effect on performance.
Meaning and implications
The anchoring and adjustment heuristic and the sunk cost fallacy showed statistical evidence to influence performance. Although this study has excluded many variables from the theories, it is interesting for further research to narrow the scope. Detailed research on the individual effects of variables will give decision makers and researchers interesting insight and more implications for further research.
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Chapter 1:
Introduction
How do we make investment decision, do we maximise utility or do we use short cuts in decision making? Do we act rational or not? And how do investments affect performance? Many different answers have been proposed. Is it logic, statistics or heuristics? Logic and statistics have mostly been linked to rational decision making and heuristics to error-prone or irrational decision making. Starting in the 1970s research became more interested in these oppositions. Leading research was done by Tversky and Kahneman in 1974 with the heuristics-and-biases program. This study tries to give more insight in the effects of investment decision making in Google marketing tools (GMT) and the related performance outcome. The GMT´s are part of the online marketing strategy, which is a relatively new research area.
Investment decision making has often been studied. The world changes fast and new
technologies are developed on a daily bases. Especially in the field of marketing, technologies are increasingly data driven. Data can help the decision maker to make better informed decisions. Google makes great contributions in developing data-driven technologies and tools. For this reason the influence of Google in this relatively new world of online marketing is significant. Internet changed business models, opened new worlds and Google plays an important role in our daily lives. A lot of research has been conducted in the field of decision making, online marketing and performance. But no research exists that integrated these variables.
It is relevant to gain more insight since many organisations shift budget from offline to online. Competition increases faster than ever and well informed decision making could lead to better performance. For that reason and observation of decision making behaviour; connecting the dots between investment decision making in Google Marketing Tools and performance got relevant.
Below is a brief example of investment decision making in GMT and how entrepreneurs are influenced in their decision making.
Both organisations X and Y are investing in GMT. They invest thousands of euros yearly. Different reasons have been observed for investing in GMT and the influence of heuristics and biases such as anchoring and adjustment, sunk cost and confirmation bias. The next two examples are based on our daily practice.
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1.1 Practical study motive
The first organisation decided to invest in a new web shop. New technology, up to date Content Management System (CMS) and modern design should enable growth in profit, turn-over and traffic. After four months of development no finished product was delivered yet. A technical SEO investigation revealed multiple improvements. Lots of improvements had to be made and even that would not lead to the best possible result.
Already 60% of the total costs were paid. A rational decision maker, not taking into account the sunk cost, would stop the process. Despite the moderate output the organisation continued the process. Main arguments were unwillingness to waste the already invested resources.
The second organisation executed a broad online marketing campaign. Conversions as well as margins per sale were high. Part of the online marketing was a banner campaign in the Google Display Network (GDN). This campaign led to more than two million views, high ad spends but no conversion. Google Analytics indicated that direct and indirect conversions could be
measured but were not present. A rational choice would be to stop wasting money in this campaign. From an expected utility perspective the available marketing budget would be invested in more profitable campaigns.
The organisation decided to continue the campaign for `branding` purposes. Google also advised the organisation to invest in the display network. This confirmed their thought (anchor) and made the organisation determine to continue investing.
This raises the question, how decision makers are influenced when they invest in GMT and how does it affect organisations’ performance?
1.2 Theoretical study motive
Little research has been conducted to the role of analytics in supporting and informing decision making within organisations (Weischedel, Matear, Deans, 2005). As the use and application of analytics is relatively new, existing research and experience is limited. However, evidence from industry suggests that e-businesses have adopted web analytics at a rapid pace to identify and monitor activities on their websites in order to achieve corporate objectives and identify users’ needs (Weischedel, Matear, Deans, 2005). This calls for broader research on the aspects of decision making effecting investments and the performance outcome. Usage of analytics lies in the field of utility maximization and rationality on decision making since it can assist decision makers to evaluate options based on facts and data. If data analysis methods efficiently have been integrated, it should also enable to maintain pace in decision making.
Gained knowledge from this research supports how decisions are made and what their effect is on performance. This study helps modelling and analysing the decision process of investments
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in GMT and performance outcome. Research on GMT investment decisions and its performance is particularly relevant since the emergence of the Internet presented organisations with an opportunity to improve their performance and enhance revenues (Hoffman et al., 1995). Since surfing the Internet has become a daily activity to most of us, it changed a lot in our behaviour (Chiang and Su, 2012). Behaviour changed in a way that 60% of consumers search for
information about products or services online, and 21% do so on a typical day (Jansen 2010). Since usage intensified, it might be acceptable to assume that focus has to be more implementing Analytics (data) in order to make sound investment decisions. Another view is proposed by Pingle (1995) stating that imitation of competitors can be more useful when decisions have to be made for the first time. Imitation can be seen as alternative to strive for rationality in decision making.
In the fast changing world of Internet many decisions have to be made for the first time and within a short time in order to keep up with the market and customers. It is interesting to investigate how GMT investments and decision making affect performance. Evans and Wurster (1997) predicted that the Internet would represent the ‘most important wave in the information revolution’ (Evans, 1997). As a result, increasing numbers of retailers have responded to these changes in shopping behaviour by building web stores and enhancing the online shopping experience they offer, so as to attract and to retain customers in this highly interactive channel (Doherty et al, 2010). This shift towards online revenues makes it relevant to do more research in the connected field of investment decisions making in GMT and performance.
1.3 Relevance of this study
Making “good decisions” (Hastie, 2001) is always essential but nowadays even more important in the relatively new field of online business, online marketing and striving for healthy profits in order to guarantee continuity of the organisation. This study aims to give insight in the relation between decision making, GMT investments and the related performance.
This study has multiple goals. One of the goals was to give better insight in the available research on decision making and rationality, GMT and performance and how or if these variables can be connected. Second goal was to explore the novice research field of online marketing tools Google provides and how they affect performance.
Fast innovation results in ongoing improvement and development of new products and services. This leads to a fast and high velocity playing field. For many organisations it becomes highly relevant to move into the e-business. Budgets for offline switch to online; online marketing relevance increases and the need for data might be vital for survival. What investment decisions
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in GMT influences is essential in order to evaluate their performance. Performance measured in monetary results and investments, but also in non-financial perspective.
So far, there has been little academic research on investment decision making in online marketing tools. This is in sharp contrast to the wide range of literature on strategic decision making, internet marketing and performance. There is limited literature on analytics that measures the results of online marketing tools and can assist the decision making process. However, there is virtually no research that combines these areas to develop a holistic and comprehensive understanding. Even though automatically generated analytics are a crucial tool for all organisations with a web presence, the direct correlation between analytics and decision making has not been tested. Analytics should be able to create a distinct advantage and benefit when they are used for decision making in this environment. According to the established link between Internet use and improved business performance (Rao et al., 2003), businesses that apply the empirically supported data will be in a better position to create a competitive advantage within their industry (Weischedel, Matear, Deans, 2005).
Also Weischedel, Matear and Deans, (2005) underline the importance of well informed decision making and the relevance of Analytics. In common with bricks-and-mortar business, strategy formulation and implementation is a significant contributor to sustainable competitive
advantage in web-based businesses (Varadarajan and Jayachandran, 1999). To make long-term and complex strategic decisions, managers need comprehensive external and internal
information regarding the market, environment and customers, as well as creativity, knowledge and experience (Eisenhardt, 1999; Cope and Waddell, 2001). Decision making is a dynamic process that requires modification and change according to changing conditions (Dickson et al., 2001). Decision making for web operations faces issues similar to those of traditional companies (Merrilees, 2001; Griffith and Krampf, 1998) but is challenging, as the environment is
unpredictable and changing rapidly. As more and more organisations shift to Internet this field of research will be increasingly important. Does rapid change and rational decision making go together is now a relevant question.
Literature on analytics demonstrates their significance to inform decisions and assess
performance. The very limited literature on analytics combined with the innovative character of the Internet suggests that the use of analytics for investments decisions in online marketing within web-based businesses is still at a low level. However, research also indicates that analytics have potential to be used effectively for decision making. On the other hand, the opposite line of research doubts the positive effect of data driven and rational decision making.
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Therefore, the purpose of this study was to investigate if investing in GMT effects performance and if decision makers are affected by expected utility, anchoring and adjustment heuristics, sunk cost fallacy and confirmation bias; this was investigated using the following research question:
How rational are decision makers when they invest in Google marketing tools? And what is the effect of these investments on performance?
1.4 Thesis outline
The study is structured as follows: in the next section 2, the related literature is outlined. In section 3, the developed methodology is described in detail. Finally, the research contribution is concluded in chapter 4 and the limitations of the approach in section 5.
Chapter 2:
Literature review
This chapter features reviewed literature about the most relevant study aspects. The first part of the literature review focuses and the online marketing including GMT. The next part focuses on performance elements. What is the dominant literature on performance and what part of performance measurement is relevant in this study. The literature review also highlights the most relevant theories about decision making; definitions, points of view from researchers and the main aspects of decision making; which are rationality, expected utility, heuristics and biases.
Related to decision making is investments in GMT. A broader insight is given in what these tools are and how organisations can invest; monetary investments, time investment and resource investments. An overview of the available GMT is given as well as a brief summary of their application in online marketing.
2.1 Google Marketing Tools
Google changed many business models and the way organisations operate. The marketing tools provided by Google are a small part of the wide range of available marketing tools. Large investments can be made in Google Marketing tools, which may lead to great effects on firm performance, in both positive and negative way. The relevance and literature on GMT is explained in the next section.
2.1.1 Introduction to online marketing
Internet usage continues to explode across the world with online becoming an increasingly important source of competitive advantage in both B2C and B2B marketing (Leeflang 2014). The Internet has become one of the most important marketplaces for transactions of goods and
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services. For example, online consumer spending in the United States, already in 2007,
surpassed USD 100 billion. Also the growth rates of online demands for information goods, such as books, magazines, and software, are between 25% and 50% (Albuquerque, Pavlidis, Chatow, Chen, & Jamal, 2012). This calls for sophisticated online marketing that includes a powerful set of methodologies and tools that are used to promote organisations, products and services on the Internet. The 1990s being the decade of e-commerce, the early part of the 21st century has become the era of social commerce (Fader & Winer, 2012). Investing in online marketing could deliver great benefits. Often mentioned examples are growth in potential, declining expenses, tailored communication, access to free and extensive of data, competitive advantage and better customer experience and service.
Online marketing tools offer the ability to combine creativity and technical tools, resulting in a wide variety of applications. These tools can be categorized as following; local search, affiliate marketing, lead-based marketing and e-commerce. This research focuses on tools available and owned by Google. Google is the biggest organisation offering a vast array of tools enabling organisations to compete in the world of Internet. Google also gives access to the companied data, enabling organisations to make better informed decisions based on the analytics and data. If organisations use these opportunities and data remains the question.
The next paragraph clarifies what GMT’s are and how they can be applied in organisations.
2.1.2 Google Marketing Tools
With new tools and services constantly emerging, the process of identifying website roadblocks, improving the conversion path and ultimately turning more visitors into customers is set to become less alien to many online advertisers (Heaps, 2011). Especially for decision makers, result of data can be used for a wide range of applications. For example, more relevant advertising should attract more relevant website traffic. Optimizing websites should result in more turnovers. Improving customer service should result in increasing leads. All these
applications and results improved decision making and overall analysis. Google offers a diverse amount of tools which can be relevant in decision making and measuring outcome of use.
Part of this study focused on investment decision making in Google Marketing tools since they have important characteristics in research. GMT is relatively new and research on GMT is still in its early stages. The tools offered by Google give access to a wide range of data, making it
possible to base investment decisions on facts and figures. This can result in utility maximization and rational decision making. To offer more insight in the available Google Marketing tools an overview is listed below.
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Google Analytics gives insight in website and GMT data. If set up correctly, administrators have access to very specific data on website usage, behaviour and traffic.
Google+ originally started as a social media platform. It allowed users to connect to topics and people of interest. Google+ failed as social media platform, but became much more than a new social media platform. It has become the connecting element in many Google services such as Google store, Gmail, search, pictures, hangout, YouTube and drive.
Google Webmaster Tools allows administrators to obtain better insight on vitality of the connected website is in the eyes of Google.
Google AdWords is Google's pay-per-click product. Ads can be created that target specific
keywords. Up to four Ads appear above the organic search results on Google when people search for these keywords.
Google alerts enable you to monitor the web for mentions of specific keywords or phrases. YouTube is since 2006 a Google product. YouTube's more than 1 billion users watch hundreds of millions of hours on YouTube and generate billions of views every day. YouTube processes over 3 billion searches monthly and is the fastest growing video sharing website.
Google Display Network: we’re all spending more time online and 95% of that time is spent reading and engaging with content on websites. The Google Display Network can help you reach all these potential customers.
Remarketing: AdWords is integrated as Google Marketing Tools by many organisations. It led to higher cost per click and squeezed out small organisations from AdWords. This led to increasing popularity of retargeting campaigns since 2010. Remarketing enables advertisers to 'follow consumers around the web'. The targeting capability of Google’s remarketing system and the high conversion rate makes it an interesting marketing tool.
Google Analytics will be further discussed in the next sections. It is not only a GMT; it is also directly linked to decision making based on rationality. As written before, the improved use of analytics can help organisation make better informed investment decisions.
Conclusion
The relevance of integrating analytics and utility maximizations in the decision making process and its effect on performance is contradictory across many studies and researchers. Some researchers state that analytics implementation can be very misleading because such data are not always a good predictor of current and future performance. Other researchers claim the
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overall positive relationship between the deployment of analytics and organisations performance.
Different perspectives of applying analytics and the effect on performance have been discussed. Utility maximization and implementation of analytics vote for rational decision making and positive effect on performance.
The next chapter discusses the literature on performance. Furthermore the relevant aspects and how to apply performance as a measurement tool in this research will be discussed.
2.2 Performance
The potential influence of organisational performance on decision making has attracted particular attention (e.g. Bateman and Zeithaml, 1989; Eisenhardt, 1989; Fredrickson, 1985; Miller and Friesen, 1983). Some authors find empirical support for a positive relationship between performance and the processes of decision making (e.g. Jones et al., 1992; Smith et al., 1988). This may be due to having the luxury or slack of resources needed to absorb the cost of the rational processes of decision making (Fredrickson, 1984). There is, however, an opposite line of argument. This is that less performing organisations may have strong incentives to push decision makers to be more rational because a wrong decision may put an organisation out of business. On the other hand superior performance reduces the desire to search for and analyse information (Bourgeois, 1981; Cyert and March, 1963; Fredrickson, 1985). Performance in this study aimed to measure if investing in GMT affects the profit, turnover, leads and traffic. Indicators of performance are financial as well as non-financial indicators.
For many years, the advances on information technology (IT) have substantially changed organisations (Bailey & Rabinovich, 2001; Takeishi, 2002). Prahinski and Benton (2004) report that IT can improve organisation’ performance. Several methods have been introduced to improve performance covering models based on analytics and click stream analysis (Cho, Kim, & Kim, 2002; Chou, Li, Chen, & Wu, 2010; Kalczynski, Senecal, & Nantel, 2006; Kim & Yum, 2011), online marketing (Tsai, Chou, & Leu, 2011; Wang, Wang, & Farn, 2009) as well as online
effectiveness and website evaluation (Chou & Cheng, 2012; Kim, Kwon, & Chang, 2011)(Schäfer et al., 2013). Though, suitable methodological approaches that are able to enrich existing organisation data by providing internal measure are rare. Furthermore, methods that directly link decisions to investment in GMT and performance do not exist. Therefore performance effects focused on profit, turnover, leads and website traffic.
Organisational performance can be measured through both objective and subjective indicators, and the substitutability of the latter for the former remains controversial. It is quite common for
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organisations to refuse providing researchers with objective data on organisational performance (Sapienza et al., 1988). It became clear that this was a problem at the first stage of research. In order to get relevant research data, non-confidential input has been gathered. Gathered information could also be extracted from Google Analytics, which is implemented by many organisations using GMT’s.
2.2.1 Google Analytics
Lilien (2011) describes marketing analytics as, a “technology-enabled and model-supported approach to harness customer and market data to enhance marketing decision making”. All applied GMT produce data, these metrics can be analysed using Google Analytics. It is free accessible and easy to interpret, therefore even laymen could use analytics in the decision making process. The following sections focus on different views on implementation of analytics and its effect on performance.
Decision making based on Analytics
Weischedel et al (2005) conclude in their research that organisations currently measure website performance and consumer behaviour online but are still uncertain how best to use those metrics to inform strategic marketing and investment decisions. There has been considerable research on the strategic aspects of decision making based on analytics (Varadarajan and Yadav, 2002). Still decision making based on analytics is a relatively new field of research and
experience is limited.
Increased business competition requires more rapid and sophisticated information and data analysis. These requirements challenge performance management to effectively support the decision making process. Business analytics is an emerging field that can potentially extend the domain of performance management to provide an improved understanding of business dynamics and lead to a better decision making (M. Schläfke, R. Silvi, K. Möller, 2012).
Many companies and markets operate in a highly competitive environment and acknowledge that their competitive advantages are no longer sustainable. Therefore, new sources of competitive advantage are created, while others rapidly erode (D’Aveni, 1994). New
opportunities, timing, know-how, and the need for instant and effective service, require more intelligent and analytical decision making tools across the whole organisation. Advanced data analysis, scenario planning, and predictive capabilities are a way to cope with increasing complexity, uncertainty, and volatility. This is supported by a continuously grown amount of data, which are available for organisations (IBM, 2010). As a result, organisations have started to focus on analytical approaches to deal with the data. It is almost inevitable to integrate Google Analytics into decision making when organisations invest in GMT. From rational perspective it
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may be expected that decision making based on data grows fast, which advocates rationality and the expected utility in this study. It may also be suspected that decision making based on
expected utility leads to higher performance. From the bounded rational perspective it may be expected that considerable amount of organisations still make decisions based on biases and heuristics.
Decision making is a continuous process and changes constantly. Fact based decisions striving for more rational decision making is where Google Analytics has its relevance. In the decision making process analytical analyses should be integrated in the early stages of the process. Davenport et al claim that application of analytics are past data, which can be very misleading because such data are not always a good predictor of current and future performance. These potential problems indicate that business analytics should be the first rather than the last step in the decision-making process (Davenport et al., 2010).
Klatt et al (2011) state that decisions based on data and made with the use of analytical tools are normally better than those made without. Their theory suggests that rational decision making leads to higher performance. For that reason, the obvious advantages of analytics and its increasing importance within performance management make it a subject destined for further empirical research.
Analytics effecting performance
Existing research documented an overall positive relationship between the deployment of analytics and firm performance.
While existing studies examined the performance gains organisations can expect from integrating customer analytics (e.g., Brynjolfsson, Hitt, and Kim2011; Germann, Lilien, and Rangaswamy 2012), these studies report aggregate, across industry performance effects. Authors Germann, Lilien, Fiedler and Kraus argue that performance effects differ systematically by industry and that retail organisations can expect to derive greater performance gains from using customer analytics than others. This study is narrowed to measuring if analysing data is predominant to bounded rational decision making and how it affects performance.
The deployment of marketing analytics varies widely across organisations. Many C-level executives remain sceptical regarding the benefits that they could gain from their marketing analytics efforts (Germann, Lilien, and Rangaswamy 2012). The analysis of a survey of 212 senior executives of Fortune 1000 organisations demonstrates that organisations attain
favourable and apparently sustainable performance outcomes through greater use of analytics. The analysis also shows important moderators. For example more intense industry competition
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and more rapidly changing customer preferences increase the positive impact of the deployment of analytics on organisation performance. The advantage of GMT is that all performance can be measured via Google Analytics.
Rapid technological and environmental changes have transformed the structure and content of marketing managers' jobs. In this changing environment, opportunities for the deployment of marketing analytics to increase profitability seemingly should abound. Indeed, an entire stream of research in marketing documents the positive performance implications of deploying
marketing analytics (e.g., Hoch & Schkade, 1996; Kannan, kline Pope, & Jain, 2009; Lodish, Curtis, Ness, & Simpson, 1988; McIntyre, 1982; Natter et al., 2008; Silva-Risso, Bucklin, & Morrison, 1999; Zoltners & Sinha, 2005).
On the other hand, a study of 587 C-level executives of large international companies revealed that only approximately 10% of the organisations regularly employ marketing analytics (McKinsey & Co., 2009). And Kucera and White (2012) note that only 16% of the 160 business leaders who responded to their survey reported using predictive analytics, although those users “significantly outpace those that do not in two important marketing performance metrics”.
A few authors suggest that the use of marketing analytics can slow organisations down, leading to missed market opportunities that are seized by more agile and non-analytics-oriented competition. It is also suggested that “excessive delays in the name of information-gathering breed’s analysis paralysis,” which leads to missed opportunities and below average organisation performance.
Conclusion
For years sophisticated performance management systems have been developed in order to support decision makers with detailed and relevant information. Performance management expended its view also to non-financial performance drivers. From this perspective the
relevance of analytics becomes clear. On the other hand, some authors claim negative effects of rational decision making and utility maximization. Part of this study focused, from a generic perspective, on the effect of investing in GMT on performance. As visible in the conceptual model, research has been done on the effect of decision making in GMT investments. These results have not been taken into account in the research on the relation between investing in GMT and the performance effect.
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The world changes fast, business models erode and the need for data should enable
organisations to optimize their decision making process. The world of increasing performance, Internet and Googles’ measurable Marketing Tools combined, lead to the following hypothesis.
H1: The more decision makers invest in GMT, the higher performance will be.
2.3 Decision making
This chapter contains relevant literature in the field of decision making. The next paragraph starts with a general decision making definition. Next sections discuss the most relevant and applicable aspects of decision making. Different views and perspectives are discussed. The next paragraphs reveal why decision making plays such an important role in the field of investing in GMT and the performance that is the outcome of investments.
Eisenhardt and Zbaracki (2002) conclude that decision makers are boundedly rational, that power wins battles of choice, and that chance affects the course of decision making. They also focus on the dominant paradigms: rationality and bounded rationality, politics and power and garbage can. In the following section of this study rationality and bounded rationality are taken into account for their possible influence in the field of investing in GMT and the performance outcome. The different perspectives on rationality are in the end confined to a few variables that gained existence in theory. As Eisenhardt and Zbaracki suggested it their research, it is an opportune time for new visions on decision making. Existing research proved that uncertainty, threatening environments and external control led to less rational behaviour. These settings seem to be present in the novice world of online marketing. The next section focuses on decision making and the novice world of Internet.
As ecommerce begins to mature, many researchers and owners have recognised the need to identify and measure the impact of their ecommerce expenditure (Palmer 2002). As stated above investments decisions and online marketing should therefore acquire high priority; taking into account that investment in marketing tools is often increasing the level of organisations’ success. Many factors should be taken into account during the decision making process. The evaluations of options and decisions that have been made have great influence on the outcome and resulting performance. All decision making processes lead to a certain outcome, which might be implementation of tools and even doing nothing. Relevant decision making in this study focuses on selecting, implementing and evaluation the investments in GMT and resulting performance.
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Previous researchers have been interested in many aspects of decision making that might influence rationality. Inevitably it was necessary to select from the vast array of definitions and paradigms on decision making. The decision was made to exclude many aspects. The included theory has been grouped by their identifying theoretical perspectives, which have been subject to substantial theoretical interest and/or empirical support. The fact that many researchers have been doing research in this field increases the possibility of comparing the findings of this study with those of previous investigations.
The next sections outline the existing knowledge and research on the main aspects of decisions making. Each before mentioned aspect of decision making has been discussed and the
connection to investing in GMT and related performance is clarified.
2.3.1 Rationality and bounded rationality
In its most basic form, the rational model of choice follows the everyday assumption that human behaviour has some purpose. In research on decision making, this translates into a common model of rational action (March and Simon, 1958; Allison, 1971), sometimes referred to as the synoptic or comprehensive model of decision (e.g., Anderson, 1983; Nutt, 1976, 1984).
According to this model, actors enter decision situations with known objectives i.e. increasing online sales or improve performance. These objectives determine the value of the possible consequences of an action. The actors gather appropriate information, and develop a set of alternative actions. They then select the optimal alternative. For example, Simon's identification, development, and selection model (Simon, 1965) is a simplified version of this rational model. As mentioned earlier in this study the world of online changes fast and is relatively new; raising the question if rational decision making is bounded. Dean and Sharfman (1992) examined rationality in a study of 57 strategic decisions in 24 firms. They found that threatening
environments, high uncertainty, and external control decreased rationality. Dean and Sharfman (1992) acknowledge that decision processes are often boundedly rational and so seek to improve the rationality, usually by using more information and creating more diverse viewpoints. Analytics application can be of relevant use in this view.
Existing research proved existence of bounded rational behaviour in a vast array of settings. Different views, perspectives and scenarios for bounded rational behaviour have been proposed. Many arguments can be formulised that decision makers are bounded rational. More views and theory on bounded rationality is given in the next paragraphs.
Mechanisms of Bounded Rationality
In administrative behaviour, bounded rationality is largely characterized as a residual category. Rationality is bounded when it falls short of omniscience. And the failures of omniscience are
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largely failures of knowing all the alternatives, uncertainty about relevant exogenous events, and inability to calculate consequences (Simon 1979). This is applicable to the novice world of Internet and related decision making. Bounded rationality in business environment, characteristics is described by Simon (1979) in three sections.
The Employment Relation: a fundamental characteristic of modern industrial society is that most
work is performed, not by individuals who produce products neither for sale, nor by individual contractors, but by persons who have accepted employment in a business organisation and the authority relation with the employer that employment entails. Acceptance of authority means willingness to permit one's behaviour to be determined by the employer, at least within some zone of indifference or acceptance. Relevant in this study is that it could have influence on the feedback given to the decision maker, search for alternatives and how performance is measured. Since investing in GMT covers a broad area it might lead to a misfit between investment and performance.
Organizational Equilibrium: Barnard (1938) described organisations survival in terms of the
motivations that make their employees, investors, customers and suppliers willing to remain in the system. This willingness could have considerable effect on participants to stand out of the crowd. A perceived “good decision” can be imitation of the competition in order not to take large investment risks nor get entrenched in it.
Mechanisms of search and satisficing: if the alternatives for choice are not given initially to the
decision maker, then he must search for them. As an alternative, one could presume that the decision maker had formed some aspiration as to how good an alternative he should find. As soon as he discovered an alternative for choice meeting his level of aspiration, he would stop searching and choose that alternative. Simon (1979) called this mode of selection satisficing. Opposite to optimal search and satisficing lies the confirmation bias and anchoring and adjustment, which will be explained in the heuristics and biases section of this study.
According to Eisenhardt and Zbaracki (1992) the original paradigm shaping debate over whether decision makers are rational or boundedly rational is no longer very controversial. Empirical research clearly supports the existence of cognitive limits to the rational model. Decision makers satisfice instead of optimize, rarely engage in comprehensive search, and discover their goals in the process of searching. The empirical research also suggests that many decisions follow the basic phases of problem identification, development and selection, but that they cycle through the various stages, frequently repeating, often going deeper, and always following different paths in fits and starts. Furthermore, the complexity of the problem and the
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conflict among the decision makers often influence the shape of the decision path (Eisenhardt and Zbaracki 1992).
Some authors are convinced of the bounded rationality. It is therefore interesting to focus on this theory and explore where it has common ground with investments in GMT and their effect on performance.
Cognitive limitations
Several authors reveal cognitive limitations in their studies (e.g., Cyert and March, 1963; Carter, 1971; Anderson, 1983; Pinfield, 1986). Theory and case studies are presented which
demonstrate that goals can be inconsistent across people and time, search behaviour is often local, and standard operating procedures guide much of organisational behaviour.
This study focuses on the relatively new field of online marketing. It is presumed that routines are not common yet; many (investment) decisions have to be made for the first time and course of action is unsure.
Carter (1971) formulated a fine-grained view of search processes by segmenting them into two types. Personnel-induced search occurs when strong executives with definite objectives in mind stimulate search, and opportunity-induced search occurs when organisations engage in search when unexpected opportunities arise. Search processes can be influenced by many factors, one of them is imitation. Relevance and application of imitation has been defined in the next
paragraph.
Imitation
This section focuses on the use of imitation as an alternative to rationality and the extent to which imitation can complement rationality in the search for better decision making leading to increasing performance.
Rather than comparing alternatives before making a choice, decision makers often simply imitate the choices made by others. Imitation may be advantageous when comparing
alternatives is relatively costly. However, if everyone strictly imitates, then improvements in choice cannot occur (Pingle, 1995).
Imitation frequently occurs. Apple imitates Samsung and Samsung imitates Apple. Market entrants imitate more established organisations. Governments in less developed countries imitate governments in more developed countries. Yet, in spite of its pervasiveness in reality, imitation has not found much of a place in economics. In economics, rationality is the standard behavioural assumption. The fact that imitation is notably present in reality but notably absent in economic theory gives rise to a number of questions (Pingle, 1995). Why do individuals and
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organisations imitate? What is the relationship between imitation, investment decisions and performance?
Conlisk (1980) showed that rationality and imitation can be complementary in an economic system. He developed a model where a portion of the initial population is imitators and a portion is rational optimizers. All agents had identical preferences and could switch from the use of rationality and imitation. Using this model, Conlisk theoretically demonstrated that when rationality is more expensive to apply than imitation, imitators and rationalizers would always coexist. In addition, he showed that this coexistence benefited both groups in that the level of satisfaction attained when coexistence was allowed was higher than when either rationality or imitation were forced on all agents. That is, Conlisk’s work suggests that imitation and
rationality can complement each other as agents search for optimal choices. This partition in theory clearly underlines the existence of different views on rationality versus bounded rationality.
Various researchers recognized that conscious optimizing is costly and that non-rational methods of determining behaviour may economize on available resources. Schumpeter (1934) and Simon (1955) made this point explicitly, and the idea is implicit in Hayek’s (1973)
evolutionary view of economic behaviour and organisation. Baumol and Quandt (1964)
examined the idea of “optimally imperfect decisions.” Day (1992), building on these studies, has identified six non-rational modes of decision making behaviour that reduce decision costs in the face of cognitive limitations.
Summarizing Pingle’ (1995) most relevant findings on rationality and decision making, the following is formulated. Decision makers allocate more resources to decision making: (1) when a decision is being made for the first time; which might be often in the relatively new field of online marketing, (2) immediately after a change in the decision making environment, and (3) when the decision-making environment is particularly challenging. As mentioned before the environment of e-business and online marketing is very challenging and tends to change fast. The tendencies to imitate and compare alternatives are higher in these instances resulting in less or bounded rational decision making. Conlisk’s (1980) theoretical work used imitation as a complement to the rational process of comparing alternatives and accelerates improvements in decision making.
Conclusion
From different perspectives full rational decision making is questioned and rationality is described in many different forms. Many external factors influence decision makers in their pursuit for optimal choices. A vast array of literature on decision making and rationality led to a
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narrowed scope. From theory some leading authors contributed research resulting in leading paradigms. The most relevant and proved literature has been selected to integrate in this study. Three elements of the heuristics and biases theory and the expected utility theory serve to narrow the scope of this study. This selection of variables also enables quantitative research on investment decision making in GMT and performance.
The next chapter introduces the utility theory, which has a broad history in decision theory. Rationality, utility maximization and risk are some key elements of this theory and study.
2.3.2 Introduction to Utility Theory
The well-known utility theory is by many seen as the foundation of decision theory. Utility theory aims to study and explain the preference of decision makers under uncertainty. The utility function has been mostly adopted for the demonstration and quantification of risk attitudes. Expected Utility Theory (EUT) is still seen as the standard theoretical tool for cost versus benefit analysis under conditions of risk and uncertainty. As mentioned earlier in this study, investment in GMT comes with uncertainty and risks. It is therefore that the expected utility is relevant in the research on decision making in investments on GMT and its related performance. The next part of the literature review gives an overview of the existing research and literature on the expected utility and its applicability in this study.
Long ago, Bernoulli formulated the idea that people might choose among gambles based on the expected values of utility associated with those outcomes and not the expected values of outcomes. With the axiomatic treatment by von Neumann and Morgenstern (1947), Expected Utility Theory (EUT) became the standard model of decision making under risk, and has served well as a normative and prescriptive framework. Ever since von Neumann and Morgenstern axiomatized expected utility theory, it is regarded as the general analytical framework for economic analysis of decision making under risk and uncertainty. Problems such as the choice of investment decision making. The reasonableness of the behavioural interpretation of the
assumed preference structure is largely responsible for the fact that the theory is considered a model of rational behaviour.
Expected utility has been elaborately applied in economics and is widely regarded as the foundation of decision theory. Currently utility theory is trying to explain behaviour in the absence of structure and information. It has also been applied to study the preference of decision makers under uncertainty.
25 of 128 Rational behaviour
Rational choice theory is often described as the “economic model of rationality” because many researchers use it to conceptualize problems about consumer choices or investment decisions (Mas-Colell et al. 1995). It can be formulated as follows: the decision makers should first structure a problem (i.e. investment options) and define a set of alternative decisions. Second, for each alternative, they should specify a measure that reflects their preferences (subjective utility) and evaluate the probability of each alternative occurring. Third, decision makers should compare alternatives, select the one with the highest expected value, and then implement it (Keeney 1982, von Neumann and Morgenstern 1947, Savage 1954). Does rational decision making occur in this research was one of the main questions. The described literature on expected utility and rationality in economic context gives structure to the research. The next chapter provides more insight on the expected utility and its economic context.
Economic context of expected utility
Utility theory gives a framework for the evaluation of alternative choices made. Utility refers to the satisfaction that each choice provides to the decision maker. The decision maker can either be an individual or an organisation. Utility theory assumes that any random given decision is made based on the utility maximization principle; the best choice is the one that provides the highest satisfaction to the decision maker.
Decisions are often made under uncertain conditions. Investment in extra sales force may lead to new business generation, or to a waste of budget, time and effort. Marketing investments may keep you ahead of competition or may cause you to lose focus on business development resulting in loosing after all. Expected utility theory is the account of how to choose rationally when one is not sure about the resulting outcome.
In economics, expected utility theory is often described as an account of how people actually make decisions in an economic context. These applications of expected utility theory are
descriptive, and don't bear directly on the normative question of whether utility theory provides a good account of rationality. In this research expected utility theory is integrated from a
descriptive point of view. That is, the theory of how people do make decisions.
Expected utility theory makes faulty predictions about people's decisions in many real-life choice situations (Kahneman & Tversky 1982). This does not settle whether people should make decisions on the basis of expected utility considerations. Consistency, in this case, means that choices conform to a set of axioms that are logically equivalent to a utility function. The axioms are simple principles which all reasonable people will agree should be confirmed to by a rational decision maker. The relevant axioms have been discussed in the next chapter.
26 of 128 Axioms of expected utility theory
Von Neumann & Morgenstern’s (1947) classic expected utility theory was a major attempt to formulate an axiomatic foundation for representing the preferences of a rational decision maker and has largely influenced decision making theory (Machina 1982; Savage 1954). Integration of the axioms allowed for predictions of future relationships between an individual’s preferences, given the fundamental assumption that people are rational. The axioms were 1) Completeness. For any two options, the rational decision maker either prefers one to the other or is indifferent between the two. 2) Independence. If a rational decision maker prefers option A to option B, then she should not also prefer option B to option A. 3) Transitivity. If a rational decision maker prefers gamble A to gamble B, and gamble B to gamble C, then she should not prefer gamble C to gamble A. 4) Continuity. Assume a decision maker prefers $100 to $50 to $0. There exists a gamble A that pays off $100 if you win and $0 if you lose, for which a rational decision maker is indifferent between that gamble and a sure gain of $50. However, anomalies continued to accumulate (Oppenheimer and Kelso, 2015).
Relevance of expected utility and heuristics
Based on the existing literature on the utility theory researchers began to search for alternative paradigms for decision making. One prevalent approach was to look for domain-specific,
decision making algorithms known as heuristics. Leading authors in this domain are Gigerenzer & Gaissmaier 2011 and Tversky & Kahneman 1974. They formulated simple decision rules, for example, choose whichever option comes to mind most easily, or choose the option that is highest on the most important dimension. These simple decision rules did not require
calculation of optimal utility. Instead, they addressed the problem of decision making from an information processing approach. In this approach the specific information available to the decision maker is considered. Taken into account is also how that information could be used to achieve desired outcomes in a boundedly rational world. Because heuristics are typically domain specific, new heuristics were introduced in a fast pace. This has led to a wealth of descriptors of apparent decision strategies, with limited predictive ability. Dougherty et al. (1999) formulated, “whereas it is sometimes possible to identify which heuristic participants use a posteriori, it is much more difficult to predict which heuristic will be used a priori.”
Moreover, although there have been various attempts to unify the heuristics into an integrated theory (e.g., Gigerenzer & Gaissmaier 2011, Gigerenzer et al. 1999, Shah & Oppenheimer 2008), the bulk of heuristics remain disconnected and are only loosely based in psychological theories of information processing (c.f. Dougherty et al. 1999, Wallsten 1983). This has led researchers to start connecting decision processes to other cognitive systems and to ask whether the
anomalous phenomena that heuristics were meant to explain could be modelled as emergent properties of a more integrated cognitive framework. This approach has led to a number of
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exciting advances in the study of decision making (Oppenheimer, Kelso 2015). For these reasons this research was limited to existing research of the most influential authors on the heuristics theory, Tversky & Kahneman and Gigerenzer. Only the most relevant and applicable heuristics or biases have been included in this research and have been discussed in the next chapters.
A common practical objection of behaviour researchers against the use of alternative paradigms is that utility theory might not be the best model to understand human choice behaviour but it allows a fair approximation and prediction of observed behaviour by estimating effective empirical parameters in the utility function. For the past several decades, researchers have repeatedly observed patterns of behaviour for which utility theory cannot easily account. It has become the norm to use expected utility theory for modern theories of human behaviour, to the point where the incremental value of identifying additional anomalies has become rather limited. Kuhn argued that researchers cannot shift from one paradigm until there is a viable alternative paradigm. Laboratory violations of the utility theory have long called its empirical application into question. Recent criticism based on the concavity of the utility function has triggered renewed discussion of the suitability of utility theory applications as a descriptive theory of risky choice behaviour. However, the lack of evidence against the utility theory in specific applications has led to its continued use in nearly all applied work (Just and Peterson 2010).
Conclusion
Rationality or bounded rationality, risk, uncertainty and striving for “good” decision making are from one point of view best merged in the existing literature on the expected utility theory. This study focuses on the influence of decisions on performance; from a rational or bounded rational perspective. Opposite of rationality are many theories and paradigms. As written earlier in this study the scope was narrowed to leading authors on bounded rationality, still standing
paradigms and theory. This led to testing bounded rationality according to the heuristics and biases program.
Expected utility hypothesis
The theory predominantly states that rational decision making positively effects performance. Summarizing the literature review and conclusion led to the following hypothesis.
H2: The more decision makers base their investment in GMT on expected utility, the higher organisation performance will be.
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2.3.3 Introduction to heuristics and biases
Winning performance in fast and uncertain environments depends often on quality decisions despite the huge amounts of information and large numbers of alternatives. Making a wrong decision can have catastrophic consequences in a very broad way. Systems and models are often insufficient to improve decision making, organisations also need to counsel humans to improve the ways they think, analyse information and make decisions. This is where the relevance of investment decision making in GMT has its relevance. Only relying on structured models is insufficient, decisions actually have to make about budgets, alternatives and resources. Do decision makers behave rational or they fall back on heuristics and biases in making investment decisions?
Over decades, good decision making has been considered equivalent to a rational choice between decision alternatives that is free of biases and emotions. Heuristics, simple rules of thumbs, which are based on common sense and used to solve problems quickly, were considered inferior decision making techniques that result in irrational behaviour. Over the years this view has changed and psychological research by Gigerenzer (2007), as well as popular management publications by Klein (2003) and Gladwell (2005) stress the usefulness of
heuristics.
The heuristics and biases research program initiated by Tversky and Kahneman’s addressed the question “how people make decisions given their limited resources”. The program was inspired by Herbert Simon’s principle of bounded rationality. In the late 1950s, Simon attempted to oppose the idea of classical rationality. It focused mostly on the formalization of normative solutions to judgment and decision making problems through probability theory and statistics, with the idea of bounded rationality, which addressed the specific constraints faced by agents in their environments. The heuristics and biases program followed the bounded rationality
principle by attempting to identify the specific constraints or biases associated with human judgment and decision making (Wilke and Mata 2012).
The heuristics and biases program is a very influential research program that emerged over the last decades. The program adds value by showing the shortcomings of classical economic approaches and the value of a bounded rationality perspective on judgment and decision making. The heuristics and biases program has been criticized. Some of the major critiques are that it is presenting only vague models of human reasoning. For example, the
representativeness, availability, and anchoring and adjustment heuristics proposed by Tversky and Kahneman do not provide quantitative predictions of people’s judgments and it is often unclear which heuristic is applied under which condition. Also the heuristics and biases program
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has been criticized for focusing on people’s initial responses to judgment problems rather than providing opportunity for learning from experience (Wilke & Mata 2012). Still the heuristics and biases program can be relevantly applied to this study since only the most applicable heuristics and biases will be applied.
The next section focused on the existing literature on heuristics and biases. The most influential researchers, such as Gigerenzer, Tversky and Kahneman have been discussed.
Relevance of heuristics and biases
In many business situations, effective heuristic decision making deliberately ignores information and therefore uses fewer resources. In an uncertain world, less often proves to be more.
Heuristics are tools that are developed by direct learning or over the course of evolution.
Besides their coordinative function, Bingham and Eisenhardt (2014) identify additional value for heuristics: heuristics can be surprisingly effective when experience is limited and information is correlated. They point to a series of studies to underline their argument (e.g., Bingham et al., 2007; Brown & Eisenhardt, 1997; and Davis, Eisenhardt, & Bingham, 2009). Bingham et al. (2007) add that heuristics help to focus attention and, therefore, save time. As a result, it is assumed that shared heuristics can be more efficient compared to other approaches such as long-range planning (Bingham & Eisenhardt, 2014).
From theory at least two answers can be formulated to why heuristics are useful: the accuracy effort trade-off, and the ecological rationality of heuristics. The most commonly used
explanation is that with heuristics people save effort, but at the cost of accuracy. In this view, decision makers rely on heuristics because information search and computation cost time and effort. Thorough search and calculations might not be a valid option in times of uncertainty and fast changing environments. Heuristics trades off some loss in accuracy for faster and more frugal cognition. Two interpretations of this trade-off can be formulised. The first is the rational trade-off. Not every decision is important enough to warrant spending the time to find the best course of action; thus, people choose shortcuts that save effort. In this case relying on heuristics can be rational in the sense that costs of effort are higher than the gain in accuracy. The second is cognitive limitations. Capacity limitations prevent us from acting rationally and force us to rely on heuristics. In business decision making, plenty of information is often available. However, many decisions are finally made based on gut feelings. Managers of large international corporations admit that about half of their professional decisions are gut decisions, based on their experience after having considered all the data available (Gigerenzer, 2014). The study of heuristics as effective strategies in business and other fields is only at the beginning of its
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exploration. Therefore this study focused on the most well-known researchers and their contribution to heuristics.
Most influential authors
The next paragraphs give an overview of the most influential authors regarding heuristics and biases. The most relevant heuristics of these authors are integrated in this study.
Gigerenzer – heuristics and biases in decision making
As Simon (1979, p. 500) stressed in his Nobel Memorial Lecture, the classical model of rationality requires knowledge of all the relevant alternatives, their consequences and
probabilities, and a predictable world without surprises. These conditions, however, are rarely met for the problems that individuals and organisations face (Gigerenzer and Gaissmaier 2011).
The German psychologist Gigerenzer studied the use of bounded rationality and heuristics in decision making. How do humans make inferences about their world with limited time and knowledge? Gigerenzer’ answer is that in an uncertain world people use heuristics. Gigerenzer conceptualizes rational decisions in terms of the “adaptive toolbox”. This is the repertoire of heuristics an individual or organisation has and the ability to choose a good heuristics for the task at hand, such as investment decisions. A heuristic is called “ecologically rational” to the degree that it is adapted to the structure of an environment.
Gigerenzer argues that heuristics are not irrational or always second-best to optimization, as the accuracy-effort trade-off view assumes. In contrast, studies have identified situations where heuristics make more accurate decisions with less effort. This contradicts the traditional view that more information is always better or at least can never hurt if it is free. Less-is-more effects have been shown experimentally, analytically, and by computer simulations. Which of these scenarios are applicable in investment decision making on Google Marketing Tools and results in better performance is where the theory was connected to the research.
Kahneman & Tversky
Two influential researchers in the field of judgment and decision making are Kahneman and Tversky. A large contribution to science lies in the domain of heuristics and decision making, therefore parts of their contribution to science has been integrated in this research.
One of the most important judgmental heuristics is anchoring and adjustment. Judgment involves assessing the probability or possible frequency of an event. In the tradition of bounded rationality developed by Simon (1955), the Kahneman and Tversky research program on judgment presumes that people rely on limited numbers of cognitive shortcuts and/or
