The transformation of data towards knowledge in eyes of the Positivist and the Interpretivist

Author: Sean Imamkhan Student number: 11394404 University of Amsterdam - Faculty of Science (FNWI) Thesis Master Information Studies: Business Information Systems (BIS) Final version: 17-08-2018 Supervisor: dhr. ir. A.M. (Loek) Stolwijk Examiner: dhr. drs. A. (Arjan) Vreeken

Abstract. Positivism and interpretivism are respected epistemological standpoints, which are concerned with the question of ‘how to come to knowledge’ regarding society. The positivist uses the natural science approach to come to their knowledge of society, where the interpretivist uses a more humanistic approach of coming to their knowledge of society. In this study, the widely recognized DIKW hierarchy is been applied to the standpoints of the positivist and the interpretivist. The DIKW hierarchy states that data generate information, information generate knowledge and knowledge generate wisdom. Furthermore, as a frame of reference of how the positivist and interpretivist transform data into knowledge, data, information and knowledge are also separately defined as objects from the ontological positions of objectivism and subjectivism. Both ontology and epistemology refer to the definition of knowledge. Ontology is concerned with the question ‘knowledge of the existence of objects in the world’. The objectivist believes objects in the world exist apart from the social actor, where the subjectivist believes objects in world exist interdependent of the social actor. The ontological positions facilitate the transformation process of data towards knowledge, in the eyes of the positivist and the interpretivist. The findings of this study shows that the positivist follows a linear approach in coming to knowledge from information and data, this mean data, information and knowledge are following each other up in the transformation process, which is well in line with the assumption of the DIKW hierarchy. The interpretivist follows a non-linear approach in coming to knowledge from information and data, which mean there is no clear view of the transformation elements; data, information and knowledge in what is following each other up. In this study, the line between data and information is thin or even blurred, in case of the interpretivist.

Keywords. data, information, knowledge, data to knowledge, data transformation to knowledge, positivist, interpretivist, positivism, interpretivism, knowledge perspectives, objectivist, subjectivist objectivism, subjectivism, ontology, epistemology, ontological, epistemological, DIKW hierarchy, DIKW pyramid, DIKW assumption, DIKW, DIK, generation of knowledge, knowledge as input. Table of Contents

1. Introduction 1 2. Literature Review 2 2.1. DIKW 2 2.2. Definition of Knowledge 5 2.2.1. Epistemology (Positivism & Interpretivism) 6 2.2.2. Ontology (Objectivism & Subjectivism) 7 2.3. Bridge to Research Questions (relevance research) 8 2.4. Research Questions 8 2.5. Conceptual Framework 10 3. Methodology 11 4. Results 14 4.1. What is Data? 14 4.2. What is Information? 15 4.3. What is Knowledge? 16 4.4. How is Data been achieved? 18 4.5. How is Information been achieved? 19 4.6. How is Knowledge been achieved? 20 4.7. The transformation of data towards knowledge in eyes of the Positivist and the Interpretivist 21 5. Conclusion 22 6. Discussion, Limitations and Future research 23 6.1. Scientific Implication (contribution of this research) 23 6.2. Practical Implication (contribution of this research) 24 6.3. Counterargument (a side-view) 24 References 26 Annex A: The Information Theory of Shannon 31

1. Introduction

What is knowledge? What is data? And what leads to the development of data to knowledge? Is data really a precursor in the hierarchy towards knowledge (Rowley, 2007)? Or is data founded on the knowledge of the human mind (Tuomi, 1999)? What constitutes knowledge? The definition of knowledge is quite hard to pin down given the different perspectives on ‘what do we consider to be knowledge’ (Henriques, 2013; Rowley, 2007). Henriques (2013) mentioned that the oldest concept of knowledge refers to the theory of: Justified True Belief (JTB), stated by the Greek philosopher Plato. The JTB theory consist of: a mental representation about a state of affairs that corresponds to the actual state of affairs. This mean the actual state is true and the representation can be validated by logical and empirical factors of the believer (Henriques, 2013). But what is data? And how does data relate to information? Data from a computer point-of-view, is something in its digital form, where it is been presented by binary values for transporting and showing the data or information on-screen (Rouse, 2017). This concept is based on the work of the father of information theory: Claude E. Shannon. Shannon prescribes data and information as logistics processes, where splitting the information in the smallest possible chunks of data (i.e. bits having the ability of possessing only two values; 0 or 1), plays a fundamental role in sending and receiving the (total) information (Jha, 2016). Russell Ackoff, a professor in organizational change and a system theorist, describes data as symbols that contain properties of objects and events. Information is something that consist out of processed data (Ackoff, 1999). Ackoff (1999) mention an example of that idea by illustrating the census taking concept. Census takers collect data and convert the results into tables. The converting step is where the data is been processed into information, in this case by presenting it in tables. As stated before, there are different ways in approaching the concept of data, information and knowledge. In this research, the focus is on the scientific perspective of a positivist and an interpretivist, regarding the transformation of data towards knowledge. According to Huizing (2007), the positivist perspective on knowledge is ubiquitous in knowledge and information theories. However, a millennium went over in discussing what knowledge really means, resulting in an unclear definition of knowledge. Therefore, the question of how to come to knowledge has always been a respected branch of philosophy (Huizing, 2007). Positivism and interpretivism are both scientific standpoints, or disciplines of how to come to knowledge (Bryman, 2012). In 2007, Rowley found out that there is a less clear unified concession on the processes which leads to the transformation of data towards knowledge. According to her findings it is not clear if data, information and knowledge can be approached as three distinct concepts (‘objects’). In contrast however, Rowley (2007) mentioned that a certain relationship can be established from data to information, and from information to knowledge, hence at two levels instead of three. Data towards information is explained in terms of structuring data for attaining meaningfulness, usefulness, relevance and value to the data. Information towards knowledge is defined by

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understanding the operationalization or actionability of information (Rowley, 2007), for example, understanding how to put the information in practice (e.g. organizations). In this research, the focus also lies in the transformation of data towards knowledge, but then studied in the eyes of the positivist and the interpretivist. The researcher is curious how those scientific standpoints come to knowledge, when data and information as (distinct) objects are been taking as precursors towards their knowledge claims. How do the positivist and the interpretivist come to know something? When the well-recognized assumption (Rowley, 2007) is taken that data generate information and information generate knowledge (Ackoff, 1989, 1999). In section 2.4. the elaborated research questions can be found. Furthermore, this research is structured as follows: In the next chapter (2. Literature Review) the hierarchical development of data towards knowledge is explained. After that, the notions of positivism and interpretivism are further described, as they are scientific perspectives of how to come to knowledge, and the main subject of this study. Also, the ontology theory is explained, as it acts as a frame of reference and facilitator of how the positivist and interpretivist transform data into knowledge. Finally, the research questions along with the conceptual framework follows from that. In the chapter after the literature review (3. Methodology), the focus is on the methodology that is been used to gather the results of this research. This includes: A literature research is executed to 2 positivism and 2 interpretivism studies, regarding the transformation of data towards knowledge. Also, the ontology definitions of data, information and knowledge are studied in the literature to facilitate that transformation process. In chapter 4 (4. Results), the outcomes of the research questions are construed, which answers how the positivist and interpretivist transform data into knowledge, funded on the ontological definitions of what is data, information and knowledge. Chapter 5 (5. Conclusion), provides a short summary and a conclusion of this study. The conclusion unfolds that the positivist and the interpretivist both have their own way of coming to knowledge, from having information and data as precursors towards that generation of knowledge. The positivist follows a linear approach, which mean data, information and knowledge are following each other up in the transformation. The interpretivist follows a non-linear approach, which mean the line between data and information is thin, or even blurred in coming to generate knowledge. After the conclusion (6. Discussion & Limitations), the focus is on providing a critical (personal) reflection of this study, which include discussing the limitations and implications. Next to it, recommendations are given for future research. Finally, the research concludes with used references and the Annex A.

2. Literature Review

In the first part of this chapter, literature regarding the research area is been analysed and reviewed as building blocks for defining the research questions and the conceptual framework. The conceptual framework emerges in the last section of this chapter.

2.1. DIKW

What is Data? What is Knowledge? And how are those two related to each other in the development? As Rowley (2007) state in her paper: the DIKW (Data-Information- Knowledge-Wisdom) hierarchy is one of the best recognized model in information and

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knowledge literatures that describes the transformation, or the hierarchical development of data towards wisdom (Ackoff, 1989; Zeleny, 1987). It can be therefore agreed that the DIKW is a widely accepted and used model in information sciences and knowledge management (Hey, 2004; Sharma, 2008). According to Russell Ackoff (1989, 1999), which is been generally seen as the initiator of the DIKW hierarchy (Bernstein, 2009; Frické, 2009; Jennex, 2009; Rowley, 2007; Sharma 2008), the DIKW hierarchy is articulated as follows (Rowley, 2007): - Data: symbols that represent objects, events and environmental properties. It is the product of observation by the senses. They are of no use until they can be structured (processed) into a relevant form; - Information: processed data that is become useful (meaningful). They answer the ‘who’, ‘what’, ‘when’, ‘where’, and ‘how many’ questions. Information can be inferred from data. Meaning that raw data which is not been processed (yet), can become information in a later stadium. Technology can support the processing part of (loads of) data, by using its capabilities of generating, storing, retrieving and processing data into information. Not all data have to be used when becoming information. Because, not all the data might me relevant to produce the (desired) information1. As Boisot (1998) distinct data from information, by stating that information is extracted and filtered from data; - Knowledge: answers the ‘(know)-how-to’ questions. This is a further operationalization of information by embracing information in its function of providing instructions to execute a specific task for example in practice; - Understanding: answers and appreciate the ‘why’ questions. Bellinger, Castro, and Mills (2004) elaborate, that it enables the capability to synthesis new knowledge from previous experienced knowledge or information; - Intelligence: focus on increasing the efficiency of previous mentioned stages. Think of minimizing the amount of resources to process the same information, knowledge and understanding; - Wisdom: focus on increasing the effectiveness, which result in adding value to the efficiency. This requires the involvement of the mental structure to assign a judgement to a certain situation. This judgement is inherent to the mental function, which embrace the ethical and aesthetic properties of the individual, and is therefore unique and personal (Rowley, 2007). As Ackoff (1999) defines it in his paper:

The difference between efficiency and effectiveness—that which differentiates wisdom from understanding, knowledge, information, and data—is reflected in the difference between development and growth. Growth does not require an increase in value; development does. Therefore, development requires an increase in wisdom as well as understanding, knowledge, and information. (Ackoff, 1999, pp. 1-2)

1 For example: imagine there are a lot of blocks of wood on the ground. The blocks of wood are considered as not of any use, and is therefore viewed as data. The blocks are structured into a relevant form, in this case into a physical table. By structuring into a relevant form, information is created (Rowley, 2007). On the ground, there are still a lot of blocks of wood (data) remaining, in this example, not all the data has been used to produce the desired table (information).

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Furthermore, Bellinger et al. (2004) believes, wisdom is a unique human state which requires one to have a soul, where Jessup and Valacich (2003) define wisdom as accumulated knowledge, which allows to understand how to apply concepts from one situation to others. Also, wisdom relate having the capability to act critically or practically in any situation, which is connected to an individual’s belief system (Jashapara, 2005). Meacham discuss wisdom as the manner in which knowledge was held, and the way it was put in practice (Sternberg, 1990). Rowley (2006) studied different definitions of wisdom and came up with the following summary: “the capacity to put into action the most appropriate behaviour, taking into account what is known (knowledge) and what does the most good (ethical and social considerations)” (p. 257). Ackoff is not the only one who mentioned the articulation of the DIKW hierarchy (Rowley, 2007). The first mention of the hierarchy stems out of poetry, which was acknowledged by Harlan Cleveland (Sharma, 2008). Cleveland (1982) mentioned poet Thomas Stearns Eliot as the first one who suggested the hierarchy implicitly. In 1934, Eliot wrote for the chorus The Rock: “Where is the life we have lost in living? Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information?”. Another mention of the hierarchy was made by Frank Zappa in 1979, which mention also had a connection to the arts. Zappa (1979) song: “information is not knowledge, knowledge is not wisdom, wisdom is not truth, truth is not beauty, beauty is not love, love is not music and music is the best”. Milan Zeleny mentioned the hierarchy in 1987 (two years before Ackoff mentioned it). He articulated the hierarchy into forms of knowledge metaphors. This contains: ‘know-nothing’ (referring to: data), ‘know-what’ (referring to: information), ‘know-how’ (referring to: knowledge), ‘know-why’ (referring to: wisdom) (Rowley, 2007; Sharma, 2008). Ackoff mentioned ‘intelligence’ and ‘understanding’ as additional layers (Ackoff, 1989). Zeleny (1987) mention ‘enlightenment’ as a layer above wisdom, which is about gaining the sense of truth, defining the sensibility of right and wrong, and getting it accepted and respected on social level. Zeleny linked the ‘enlightenment’ layer to the knowledge metaphor of ‘know-yourself’ (Zeleny, 2011). Choo (1996) mention ‘signals’ as the input for data creation. The transformation of signals into data is been made by sensing, selecting (i.e. physical structuring) the signals. Bellinger et al. (2004) disagree with having understanding as a separate layer. They approach understanding on each level that support the transition of data towards wisdom. In their eyes, data is a fact, event or statement without relations. Information is about attaining understanding of relationships, for example, connecting cause with effect. Knowledge is about understanding patterns, like predicting the next step based on previous experienced connections. For example: if A happen, and B follows from that, the chance is plausible that C happen. The transition of knowledge to wisdom builds on understanding principles, which embodies knowledge into an amalgamation of understanding the knowledge of being what it is. For example: It is A, because it is A. This knowledge claim is based on understanding all the systemically interacting elements which develop in claiming: A is A. A remarkable thing to mention, is that the DIKW hierarchy is not a confirmation of one of the oldest and dominating definition of knowledge by Plato (Mutongi, 2016), which refers to the: justified true belief (JTB) theory (Henriques, 2013). Plato defines the JTB theory by stating something to be knowledge (Lacewing, 2015):

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1. When somebody believes something is true (e.g. subject A believes object B exist); 2. the object exists (i.e. it is true that B exist) and; 3. it can be justified that object B exists (e.g. subject A can touch object B).

Mutongi (2016) explains that the DIKW hierarchy is not a confirmation of Plato’s knowledge theory, because the JTB theory claims that something is considered as knowledge, when it is possible to justify-it-true-belief, and the DIKW hierarchy states that knowledge is always generated by information, whether it is justified or not. Summing this part up, it is outlined that the DIKW hierarchy is articulated by different people in different ways, and different layers or perspectives were stated by different people. However, the most shared known view result into the DIKW model (Rowley, 2007) as displayed in figure 1.

Figure 1. The DIKW hierarchical model

Figure 1 illustrate that there can be no wisdom without knowledge, there can be no knowledge without information, there can be no information without data (Ackoff 1989). Secondly, it is displayed as a pyramid, since transforming into a higher state leads into filtering elements in the lower stages (Rowley, 2007). In other words, there is less information than data, less knowledge than information and less wisdom than knowledge (Jennex, 2009). Also, Ackoff (1989) mention that higher layers include layers that fall below it (p. 3). In the next section, the emphasis is on elaborating the concept of positivism and interpretivism, as this study investigate how the positivist and the interpretivist come to their knowledge claims, when the widely recognized DIKW hierarchy (Rowley, 2007) is been followed, which states that data generate information and information generate knowledge (Ackoff, 1989, 1999).

2.2. Definition of Knowledge

What is knowledge? The definition of knowledge is quite hard to pin down regarding the different perspectives on ‘what do we consider to be knowledge’. According to Gregg Henriques Ph.D., the theory of knowledge refers to two philosophers’ angles:

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epistemological and ontological considerations (Henriques, 2013). In the next subsections, the ontological and epistemological theories are explained, where objectivism and subjectivism relate to ontology, and epistemology to positivism and interpretivism.

2.2.1. Epistemology (Positivism & Interpretivism) Social researcher Professor Bryman (2012) view about epistemological is described as: what is regarded as acceptable knowledge in a discipline2. That consideration can be tackled from positivism and interpretivism. A positivist defines acceptable knowledge studied from a natural science point-of-view. Which can be characteristics by a more objective view of approaching knowledge, testing knowledge according to fundamental laws and principals, and gathering facts that provides the basis for laws to be tested in social reality. This is external from the (personal) values of the researcher, which can be stereotyped with quantitative research methods and deductive (testing theory) research strategies. Auguste Comte (1798-1857) is been seen as the founder of the term Positivism. Auguste was a French philosopher who introduced the term in the 19th century in his books: Course in Positive Philosophy and A General View of Positivism3. Auguste believed that society could be studied in a way the physical world is been studied. As gravity is a truth in the physical world uncovered and explained by natural laws, the same laws and methods could be applied in studying the social world. Also, positivism believes that society should be studied with the senses, which therefore appreciate rigid and linear applied methods (Crossman, 2018). Interpretivism captures a more subjective meaning of defining what acceptable knowledge is, rather than studying them from an objectivism point-of-view, which is preferable as a positivist. An interpretivist sees social sciences separately from studying the natural sciences. Researchers that take the role of an interpretivist are more or less concerned about the (in-depth) empathetic understanding of contextual human behaviour, which include moving themselves in the point views of people’s actions, emotions, thoughts and so on. This can be stereotyped with qualitative research methods and inductive (generating theory) research strategies (Bryman, 2012). Interpretivism roots itself in the philosophical studies of hermeneutics and phenomenology, also the German sociologist Max Weber (1864-1920) is commonly being credited as the central influencer of interpretivism in sociology (Chowdhury, 2014). Weber developed the theory of verstehen (‘understanding’), which is a German term meaning: to understand, to perceive, to know and to comprehend the nature and significance of a phenomenon in question (Elwell, 1996). Weber uses verstehen for understanding both human action and intention in context. Achieving verstehen, can be reached by empathetic understanding the human actions from their points views (Chowdhury, 2014).

2 I.e. the science of how to come to knowledge (Huizing, 2007). 3 Auguste’s books are translated from French to English. The book series Cours de Philosophie Positive, were published between 1830 and 1842 (Barnes & Fletcher, 2017), and freely translated by H. Martineau into the form of The Positive Philosophy of Auguste Comte (Referring to: Course in Positive Philosophy), published in 1853 (The Editors of Encyclopaedia Britannica, 2018). A General View of Positivism by J. H. Bridges, which was published in 1865 (Bridges, 1865), was a translation of Discours sur l'ensemble du positivisme, published in 1848 (Gane, 2006).

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Concluding this part, the interpretivist engages the perspective of the people been studied in defining the multiple perspectives view on reality, instead of accepting one, (natural) reality, as in case of positivism (Greener, 2008). The difference between natural sciences and social sciences, can be approached as the difference between explaining (erklären) and understanding (verstehen), as the German philosopher Wilhelm Dilthey (1833-1911) express this view in understanding Geisteswissenschaften (i.e. understanding the human mind or mental appearances) as opposite of natural science (Bransen, 2001).

2.2.2. Ontology (Objectivism & Subjectivism) Bryman (2012) defines ontological considerations as: studying the existences of objects in the world in relation to their observations by social actors. This can be studied from objectivism and constructivism. As an objectivist, social actors and the objects in the world exist independently from each other. For example: A flower exists without the existence of a human. As Huizing describe objectivism as: “we should view the world as consisting of distinct objects that can and should be separated from their originators and users” (Huizing, 2007, p. 74). In case of constructivism, social actors and objects in the world don’t exist independently from each other. The meaning and existence of objects are socially construed. Applying the example of the flower to a constructivism point-of-view, the flower only exists when it can be captured by the values of the human mind (i.e. it only exists when we can see it with our eyes). Another aspect of the constructivism point of view is that the definition of a flower can change over time. Since, social constructions and their values about objects in the world may also change4 over time (Bryman, 2012). Huizing defines the view of Bryman’s constructivism as subjectivism (Huizing, 2007). He describes subjectivism as: “we should focus on human beings and see them as acting on the world through sensemaking, and in that way modifying the context they live in” (Huizing, 2007, p. 92). Subjectivism emerged itself in the twentieth century after dissatisfaction with objectivism having a central role in science. This does not imply that subjectivism took over the central role of objectivism. Mostly, subjectivism is approached as different thinking with regard to objectivism thinking. However, there is an increasing view on knowledge and information being approached as a social phenomenon, rather than (economic) external objects5, according to Huizing (2007) on subjectivism. One notable definition of the subjectivism view is that knowledge emerge by studying the inherent properties of objects in the world in relation to their interactions with social actors (interactional features), which can result into attaching a (personal/symbolic) meaning to the object in question (Huizing, 2007). In this study, the notion subjectivism and constructivism are merged by using the term subjectivism, as their meanings are closely related to each other. According to the researcher’s conclusion of studying both definitions, the differences is that one pays more attention to the dynamic and the change in social constructions, to be able to observe surrounded objects and linking meaning to them (constructivism). The other

4 E.g. changing beliefs about the meaning of the existence of a flower, due gaining new knowledge and/or undergo changes in the current social construction, which can result in a different (world) view about a flower. 5 An example of an objectivist and subjectivist view: from a subjectivist, a rose (object) can act as a symbol of love (social phenomenon). Where the economic value of the rose (e.g. status of the thorns), is related to the objectivist view (Huizing, 2007), think of selling the rose.

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focus more on the interactional relationship between social actors and the inherent properties of the objects, for attaching (symbolic) meanings to a certain object (subjectivism). The similarity can be found that both notions concern the dependencies of human involvement- and- values on the existence of objects in the world, which also mean that objects do not necessary retain fixed meanings (Bryman, 2012; Huizing 2007).

2.3. Bridge to Research Questions (relevance research)

The previous sections described the DIKW hierarchy, ontology and epistemology, where positivism and interpretivism refer to the latter. In this part, the bridge or relevance of this study towards the research questions is explained. The DIKW hierarchy state that knowledge can only be generated by already having information and data generated. Also, it is discussed that positivism and interpretivism are unique scientific views of how to come to knowledge. As Huizing (2007) state: “epistemology or the science of ‘how people come to know’ is and has always been a respected branch of philosophy” (p. 98). Nevertheless, it is therefore worthy to investigate how the positivist and interpretivist come to knowledge, when the widely recognized DIKW hierarchy is been followed (Rowley, 2007). In other words, this research gives insight in how the positivist and the interpretivist come to knowledge, from information and data as precursors. These insights are relevant, since the positivist and the interpretivist do knowledge claims in their own manner (Bryman, 2012). It is therefore interesting to gain some transparency of how they come, or reach knowledge by applying the DIKW hierarchy. The DIKW hierarchy is chosen, since it is widely recognized and accepted in knowledge and information literature, as mentioned in 2.1. This research test at the same time, if the DIKW is (still) valid, by embracing the hierarchy from the perspective of the positivist and the interpretivist. In the end, this research gives insight or transparency in the generation of knowledge as the positivist and the interpretivist. This is relevant, since those scientific perspectives both come in accepting knowledge from their discipline (Bryman, 2012). The known DIKW hierarchy is applied to enable transparency in reaching knowledge from information and data. At the same time, this research test if the DIKW assumption is valid by connecting the perspective of the positivist and the interpretivist towards it. Concluding this part, the following main research question emerges in this study:

2.4. Research Questions

“What are the differences and similarities in the transformation of data towards knowledge between perspectives of the positivist and the interpretivist?”

What are the differences and similarities between the positivist and the interpretivist, when knowledge is achieved from information and data as precursors. To be more specific, data function as raw material for information and information as raw material for knowledge (Ackoff, 1989, 1999; Rowley, 2007). Differences are expected, since the positivist and the interpretivist have both their unique way in attaining and accepting something to be knowledge (Bryman, 2012). If so, similarities between those two perspectives will be clarified to examine if that manifest in a unified image of reaching knowledge, having data and information as input for that knowledge.

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As mentioned before, knowledge also refer to ontology (Henriques, 2013; Huizing, 2007; Bryman, 2012). To gain a whole image of reaching knowledge as a positivist and an interpretivist, the researcher also investigated the ontological questions of ‘what is’ data, information and knowledge. This for defining their existence as (distinct) objects in the world, before diving into ‘how’ the positivist and the interpretivist come to their knowledge. In other words, before knowledge is extracted from information and data, as a positivist and an interpretivist, the existence of data, information and knowledge is defined from an objectivist and a subjectivist, to connect ‘how’ that is achieved, with ‘what’ does exist. In short, the ontology act as a frame of reference for the epistemology part, and at the same, support the transformation process of data towards knowledge in the eyes of the positivist and the interpretivist, which is further explained in the methodology chapter. The following sub questions represent the what is questions to meet the discipline of ontology, as from the perspective of an objectivist and a subjectivist:

1. “What is Data?” 2. “What is Information?” 3. “What is Knowledge?”

How the positivist and interpretivist come to knowledge, builds on the generation of information and data, since this study follows the assumption of the DIKW hierarchy, which result into the following sub questions, from the perspective of a positivist and an interpretivist:

4. “How is Data been achieved?” 5. “How is Information been achieved?” 6. “How is Knowledge been achieved?”

The how is questions refers to how to come to knowledge as a positivist and an interpretivist, which meets the discipline of epistemology. The research questions are captured in a conceptual framework, which is been visualized and explained in the next section.

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2.5. Conceptual Framework

Figure 2. The Conceptual Framework (CF)

Figure 2 shows the conceptual framework (CF) that is been used to gather results of this research study. The CF synthesis different theoretical concepts into one integrated framework to be taken as viewpoint for executing this research (Imenda, 2014). In this framework, data, information and knowledge are placed in the ontological oval-diagram, which refers in defining their existence as (distinct) objects as an objectivist and a subjectivist. The arrows refer to how data leads in achieving information, and how information leads in achieving knowledge, both captured in the eyes of the positivist and interpretivist. The arrows navigate outside the oval-diagram, since its represent the epistemological consideration. Although, the input and output of the arrows are connected to the ontological oval-diagram, to establish a relationship between epistemology and ontology. The relationship is needed, since both refer to the two philosophers’ angles of knowledge (Henriques, 2013). Figure 2 is not presented as a pyramid, as one might would expect. Because the interest is on the transformational aspects, rather to investigate if there is less knowledge, than information and less information than data. Secondly, wisdom is not included in the CF, since positivism and interpretivism regards to knowledge, which already is achieved after generating information (Ackoff, 1989, 1999). In the next chapter, the mechanical operation of the CF is been explained, which include the elaboration of how the results of this research is gathered and analysed.

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3. Methodology

Positivism and interpretivism have both their unique way of coming to knowledge, where positivism believes society should be studied with the same principles as natural science, interpretivism believes society should be studied apart from using natural science (Bryman, 2012). This difference result in both using their own methodologies approaches for studying society, which are outlined in table 1 (Carson, Gilmore, Perry, & Gronhaug, 2001; Jamieson, 2009; Weber, 2004):

Methodology approaches Positivism Interpretivism

Relationship between reality Obtain hard, objective Understand / gain knowledge by and research knowledge perceiving (subjective)

Focus on generalization and Focus on specific and concrete abstraction Trying to understand specific Governed by hypotheses and context stated theories Inductive approach (generating Deductive approach (testing theory) (Bryman, 2012) theory) (Bryman, 2012)

Focus researcher Describing and explanation Understanding (verstehen) and (erklären) interpreting

External, detached Internal, attached

Reaching reliability Reaching validity

Role researcher Distinction between reason and Allow reasoning, but also feeling feeling (no clear distinction)

Use rational, consistent, rigid, Use of pre-understanding (prior logical approaches knowledge)

Distinction between facts and Less distinction between facts and value judgments (personal) value judgements

Distinct between science and Accept personal experience along personal experience with science

Specific methods Prefer Quantitative methods Prefer Qualitative methods

Data is mostly statistical and Data is detailed, like coded direct measurable for interviews to achieve (in-depth or quantitative and analyse rich) verstehen (e.g. understanding purposes. This means it is human perceptions) possible to observer trends, correlations (e.g. erklären laws of human behaviour)

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Variables can be controlled (e.g. Variables are less or not controlled fixed static questions) (e.g. people as variables are unpredictable)

(Online) Surveys, laboratory Case studies, unstructured experiments, field experiments, interviews, ethnographic studies, structured interviews phenomenographic studies, and ethnomethodological studies (e.g. internal observing and participating)

Table 1. Positivism and Interpretivism overall methodology approaches The author did a qualitative research in form of a literature study to 2 scientific selected Master’s theses6 with regard to positivism and 2 with regard to interpretivism. The content of table 1 is used to determine if a study concern to positivism or interpretivism. A literature research is executed, because the methodologies approaches of table 1 is traceable in their research studies, since the methodology is a key part of a research or thesis (SkillsYouNeed, 2018). Worth to mention is, that table 1 doesn’t necessarily imply that positivists can’t never use qualitative methods (Su, 2017), or that interpretivists can never use quantitative methods (Babones, 2016), and/or that each focus area needs to be treated in their studies. The focus in determining the researcher perspective, regards if the researcher tries to reach verstehen (interpretivist) or erklären (positivist) (Bransen, 2001), whether that is achieved with quantitative or qualitative methods. Table 1, therefore only function as a guided framework. Furthermore, the theses are further studied for making the connection with the epistemological arrows of figure 2 (i.e. the transformation of data towards knowledge, in eyes of the positivist and the interpretivist), which include: in the methodology part of a research its written what kind of data will be collected and how (Statistical Training Unit, 2010), this is therefore linked with achieving data as a positivist and an interpretivist. In the results/conclusion part of a research, data is been processed/structured (analysed) into a meaningful form (presented findings of the data) (Monash University, 2018), which is therefore linked with achieving information as a positivist and interpretivist. In the discussion part of a research, the information of the results section is coloured with limitations, personal interpretations, where the researcher’s interpretation can be based on common sense (Swaen, 2014), Boisot (1998) defines common sense as knowledge that is widely diffused, but not codified (p. 58). The discussion section can also be linked with information that is combined with understanding and capability, which lives in the minds of people (Laudon & Laudon, 2006), since the researcher should express his or her thought about the validity, based on the gained insights (Swaen, 2014), thus moving from information to knowledge (Rowley, 2007), achieved as a positivist and an interpretivist7. The definitions of data, information and knowledge are also studied in the literature to have it scientifically argued, where their existence as (distinct) objects is

6 On request of the supervisor, Master’s theses of the University of Amsterdam are selected (where he fulfilled the role of supervisor or second examiner (second reader) regarding the theses), since the structure of those theses are well arranged according to the requirements of an academic research, which also include describing the methodology (University of Amsterdam - Faculty of Science, 2016). 7 The positivist and interpretivist studies are studied in their whole, rather than studying the chapters fragmentally. This for gaining an overall understanding of how data leads to knowledge.

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determined by the ontological tendency of objectivism and subjectivism, as outlined in table 2 (Bryman, 2012; Huizing, 2007):

Objectivism Subjectivism

Objects in the world exist independently from Objects in the world exist interdependent from social actors social actors

Objects have economic values Objects have symbolic values

Objects retain static meanings Objects can have variable meanings

Focus on the inherent properties of objects Focus on the interactional features of objects

Table 2. Objectivism and Subjectivism main criteria’s It is been tried to capture only definitions that could be strongly connected to the criterions of table 2, this for covering the load of objectivism and subjectivism. The existence is justified for each definition regarding ‘what is’ data, information and knowledge. The ontological part act as a frame of reference of serving the transformation process of how data, information and knowledge is achieved as the positivist and the interpretivist, with what does exist as the objectivist and the subjectivist. Furthermore, each epistemological part regarding achieving data, information and knowledge as a positivist and an interpretivist, is also justified by providing some transparency from the studied theses. After the ontological and the epistemological questions are researched, the answers of those considerations are assembled into a coherent framework, which function as answer to the main research question of this study:

“What are the differences and similarities in the transformation of data towards knowledge between perspectives of the positivist and the interpretivist?”

Enumerating this chapter up into the following research steps to be executed: 1. 2 positivism and 2 interpretivism Master’s theses are selected to be studied, concerning the transformation of data towards knowledge. Table 1 is used as a criterion to determine if a master’s thesis concern to positivism or interpretivism, before processing them; 2. After choosing 2 positivism and 2 interpretivism studies, the chapters of the theses are investigated for making the connection with data, information and knowledge. The method chapter function as achieving data, the results/conclusion chapter as achieving information, and the discussion chapter as achieving knowledge, this as a positivist and an interpretivist; 3. The ontological definitions of data, information and knowledge are studied in the literature, and justified according to the tendency of objectivism and subjectivism, as outlined in table 2. The ontological consideration function as a frame of reference of how the positivist and the interpretivist comes in achieving knowledge, from information and data. The ontological questions are therefore first researched to connect how the positivist and the interpretivist comes in knowledge, with what does exist in terms of data, information and knowledge, as an objectivist and subjectivist;

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4. Concluding, the outcomes of the positivist and the interpretivist (epistemological), and the objectivist and the subjectivist (ontology) are assembled into a coherent framework, which display how the positivist and the interpretivist transform data into knowledge. In this step, the ontological outcomes act as a facilitator for that transformation process. The ontological part has two functions as one might notice. First, it acts as a frame of reference, and secondly it facilitates the transformation process of data towards knowledge, in the eyes of the positivist and the interpretivist.

4. Results

The analysed data concerning the ontological and epistemological research questions are answered in this chapter. The first part regard to ontology, where the existence of the definitions of data, information and knowledge is justified as an objectivist and/or subjectivist. The second part provides results of how the positivist and the interpretivist comes in achieving knowledge, data and information. The epistemological part is also justified. In the last part, the outcomes regarding ontology and epistemology are assembled into a coherent framework, which shows the transformation of data towards knowledge, in eyes of the positivist and interpretivist.

4.1. What is Data?

Definitions (O) / (S) Justification

Data as bits; O Data as binary values (arranged bit values)8 can present texts, Data from a computer point-of-view, is images, sounds or videos on the computer (Rouse, 2017). presented by binary values (i.e. splitted into bits; having the ability of possessing This is connected to an objectivism view, since the data is only two values; 0 or 1) for transporting stored in the computer and not in the human mind. Where the or processing the data (Rouse, 2017). computer (object) exist apart from the human (social actor). However, the input of the data into a computer, could come This concept is based on the father of from the human mind (Tuomi, 1999). information Theory: Claude E. Shannon. See also Annex A for the connection between data (bits) and information (constructed bits).

Data as interpretation; S This is connected to subjectivism since its embracing the Subjectivists emphasize that data can be symbolic meaning of data (object), connected to the social interpreted in various ways. Since people actor believes. In this case data is been interpreted and have unique mental frameworks about the understood by the recipient (Bocij, Chaffey, Greasley, & world (which can be due differences in Hickie, 2003), which also show the thin line of data towards social and cultural values) (Huizing, information, and the relationship of data and information 2007). (Rowley, 2007). This also suggest that meaning of data is subjective. What one (social actor) sees as information (object), the other might see as data with no real significance

8 8 bits is one 1 byte (Rouse, 2017). For example: 00101011 (8 arranged bits) could represent a word (data structured into information).

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(Boddy, Boonstra, & Kennedy, 2005; Jashapara, 2005).

Data as recorded item; O/S Objectivism: Data items are recorded descriptions of If a data item is only recorded in a computer (e.g. IT- events, things, transactions and activities database), then it exists without the human, since the data is (Boddy et al., 2005; Laudon & Laudon, stored in the computer, where the computer (object) exist 2006; Turban, Rainer, & Potter, 2005). apart from the human (social actor). However, the input of the data into a computer, could come from the human mind (Tuomi, 1999).

Subjectivism: If a data item is only recorded in the human mind, where that data is not yet structured into a useful relevant form (Ackoff, 1989; 1999). Than that data only exists, when the holder (in this case the human) of that data exist.

Table 3. Definitions of Data regarding Objectivism = O and Subjectivism = S

4.2. What is Information?

Definitions (O) / (S) Justification

Information as an object; O Information as an object can be processed stored and secured Objectivists sees information as objects, in computer databases to protect their economic value. Which which can be quantified, measured and can be connected to objectivism. Also disembodying traded for attaining economic value. information from people’s mind and converting it into (Huizing, 2007) decontextualized and standardized objects is the goal of the objectivist, as mentioned by Huizing (2007).

Information as a difference; S This is a subjectivism view, as stated by Huizing (2007). Subjectivists sees information as a Including: Information is handled in ways that suit their difference, that makes a difference to social practices (Putnam, 1983). hearer or reader. More specific, it focusses Even how people are dressed can affect how information is on the interpretation and human sense been used (Fiske, 1991). Also, the meaning of information to making for attaining meaning to the a social actor can differ, when he or she arrives in different information in question (Huizing, 2007). contexts, but also the same information can differ to person A in comparison to person B (e.g. due different personal values, beliefs (OpenStax CNX, 2018), experiences and other personal references, which can influence the human sensemaking, individually). Information in this sense, is approached as social phenomenon rather than (economic) objects (Huizing, 2007).

Information as transmission; This approach of information can be connected to an Claude E. Shannon sees information as O objectivism view. Since the focus here lies on the exchange physical quantities, were the focus lies on of the message (information), rather than the person's exchanging information between sender possible biased view on the message. It embraces a and receiver, by deconstructing transactional view of information exchange, unidirectional. information into the smallest chunk of data (i.e. bits) to be able send it over channel (line), were the bits are been reconstructed into information, as

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intended by the transmitter (Shannon, 1948).

Claude E. Shannon is known as the father of information theory (Rouse, 2017). See the Annex A for an extended description about his information theory.

Information as communication: S This is a subjectivist view of information communication, Subjectivists approach information where the symbolic meaning is an aspect of subjectivism. transmission in a form of interaction, non- This also mean that the meaning of a message is not anonymously relationship, bidirectional necessarily fixed, because the meaning of message can where both sender and receiver assign the change during the process of social interactions, which is due same symbolic meaning to the message intersubjective perceptions on a message meaning (Huizing, been exchanged. 2007). (Huizing, 2007).

Table 4. Definitions of Information regarding Objectivism = O and Subjectivism = S

4.3. What is Knowledge?

Definitions (O) / (S) Justification

Explicit Knowledge; O This view is connectable to objectivism, because when there Nonaka & Takeuchi (1995) mention is explicit knowledge (object), it can exist without the explicit knowledge as knowledge that is existence of the human (social actor). For example: (hard) codified, documented, readable, knowledge that’s been documented, exist on its own. Of disembodied and treated as objects course, it's arguable if explicit knowledge regard to (Beynon-Davies, 2002), designed for knowledge, when knowledge is by many authors considered sharing (Rowley, 2007). as the property of human mind (Rowley 2007), where explicit knowledge is disembodied from the human mind (Nonaka & Boisot (1998), and some other researchers Takeuchi, 1995). Although, explicit knowledge fits Ackoff’s (Hedesstrom & Whitley, 2000), point (1989) view on knowledge, which answers (know)-how-to explicit knowledge as codified questions in form of providing instructions. Procedural knowledge. knowledge is a form of knowledge that refer to an instructional approach, as how to do something (Henriques, 2013). For example: how to something in context of a training course, could be knowledge that is been codified (Awad & Ghaziri, 2004).

Tacit Knowledge; S This form of knowledge can be connected to subjectivism, Tacit knowledge is knowledge that is since tacit knowledge (object) is inherent to the human (social integrated in the beliefs and values of the actor) mind (Bocij et al., 2003). This also implies that tacit individual, which make the knowledge knowledge not necessarily retain static meanings, because intangible (not touchable), and therefore values and beliefs of a person is also not necessary static. difficult to transfer (Nonaka & Takeuchi, 1995; Polanyi, 1962, 1967; Laudon & Values and beliefs can vary over time, as people evaluate and Laudon, 2006). debate their current state, for example when trying to

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Philosopher and scientist Michael integrate to another culture’s values system10 (OpenStax Polanyi9 introduced the idea of tacit CNX, 2018). knowledge in the fifties (Polanyi, 1962, 1967; Hedesstrom & Whitley, 2000), by Tacit knowledge can also be connected to knowledge as an stating: “we know more than we can tell” asset by Boisot (1998); Boisot (1998) describes that (Polanyi, 1966, p. 4). He refers to the knowledge approached as an asset, act differently in aspects of hard to encode (e.g. comparison to physical assets. Where physical assets can be documenting the knowledge) and hard to sensed as materialized products, knowledge assets can be communicate (because the knowledge is approached as dematerialized products. This implies that embedded in the individual’s mind) knowledge assets could exist forever11 from a theoretical (Nonaka & Takeuchi, 1995; Polanyi, point-of-view, since they can be seen as dematerialized 1962, 1967). (intangible) products, which doesn’t rust like a physical (materialized) bicycle for example.

Tacit knowledge can also be linked with personal knowledge, which refers to first-hand experience, autobiographical facts and idiosyncratic preferences (Henriques, 2013).

Knowledge as an object; O People who embrace objectivism believe that knowledge can Objectivists sees knowledge, as tradable be fully captured in objects, and that those objects have objects, like information. This means meanings on themselves (Huizing, 2007). The focus in knowledge is disembodied and codified determining the price or economic value is on the distribution into objects to extract and protect its of knowledge, rather the use / meaningfulness of knowledge economic value (Huizing, 2007). objects. Knowledge and learning consist of representation from practice, where objective information is absorbed and Knowledge as objects can also be linked stored in the mind (Huizing, 2007). This form of learning can to explicit knowledge, since explicit be connected to behaviourism and cognitivism (Abcouwer & knowledge is also disembodied from the Smit, 2007). Where behaviourism focus on the positive human mind into objects (Nonaka & behaviour of learning something, think of processing Takeuchi, 1995; Tuomi, 1999). feedback, and cognitivism focus on obtaining (objective) knowledge, internally focused (Trago & Mulder, 2017).

Knowledge as a social phenomenon; S This can be connected to the subjectivism view of Subjectivist sees knowledge as social knowledge. Since knowledge emerge by interacting with phenomenon’s, which focus on other social actors, which makes the meaning of knowledge interactions and negotiations to attain socially constructed. This also implies that knowledge do not usefulness (Huizing, 2007). necessary retain static values, as their meanings getting

9 Polanyi mentioned an example of the tacit knowledge concept in his book: The Tacit Dimension, where he stated that a human knows to recognize a person across a thousand or even a million persons. Still, it is not possible to completely communicate (transfer of knowledge) how the person’s is been recognized (Polanyi, 1966). 10 An example of differences in culture values and beliefs: two male colleagues holding each other hands in the United States, is been often associated with the symbol of romantic feelings. Where, holding each other hand in Africa, is been considered as a symbol of friendship (OpenStax CNX, 2018). 11 However, the economic value of a certain knowledge asset can lose its value over time (Boisot, 1998), like when using yesterday knowledge to overcome today’s and tomorrow's challenges (Trago & Mulder, 2017). Dr. Chipo Mutongi (2016) provides an explanation in her paper about the value change of knowledge over time: “some years ago a person who could have contracted the HIV virus was least expected to live for more than ten years but with the antiretroviral therapy, today, a person can live for so many years and who knows tomorrow a cure could be found” (p. 68).

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mutually (re-)constructed by gaining new insights due interactions and/or negotiations (Huizing, 2007). This form of knowledge can be linked with the approaches of connectivism, which focus on learning by making connections (Siemens, 2004), being a member of a network is important to achieve knowledge (Trago & Mulder, 2017), making a bridge with other circles or constructions can lead into heterogenic (novel) information or knowledge, due differences of similarities by the other party (Granovetter, 1973). And constructivism, which states that people put meaning in their own way by (re)arranging concepts, based on integrating new knowledge into their current knowledge system, due gaining new insights and/or experiences (Trago & Mulder, 2017).

Table 5. Definitions of Knowledge regarding Objectivism = O and Subjectivism = S

4.4. How is Data been achieved?

The Master’s theses of Chen (2018) and Wesselink (2018) are selected for positivism, and the theses of Boeve (2018) and Ablinger (2018) for interpretivism. How is data been achieved as the positivist? Justification

The positivist achieve data by retrieving and filtering data Chen (2018) gathered data by distributing his survey online from (online) IT-databases, where data-columns are specified on LinkedIn in different groups and communities to cover a to represent the data-attributes name or type of data (e.g. broad geographic area. Wesselink (2018) collects data by time, name, frequencies. Constructed bits values i.e. data as filtering and retrieving data from internal databases of G4S, bits). This can be linked to data as recorded item as an CBS, Rijkswaterstaat (RWS), and Koninklijk Nederlands objectivist, since the data represent ‘things’ regarding the Meteorologisch Instituut (KNMI). The dataset consists out research focus, and the data exist apart from the researcher. of columns and consist of types as: time, location, message, activity and so on). Data is also filtered according to the relevance, or things of the researcher focus (Wesselink, 2018).

Both Chen (2018) and Wesselink (2018) accessed information systems (IT) for the collection of data (Rowley, 2017).

How is data been achieved as the interpretivist? Justification

The interpretivist achieve data by being aware of the context Boeve (2018) state in her paper: “the ecological validity has in which the data should be collected. This mean that the been warranted by conducting all nine interpretivist encounters the perspectives and settings of the interviews in the interviewee’s natural atmosphere, in their participants, during the collection of the data, for increasing office or their homes” (p. 9). Which mean the setting of the its validity and sensemaking (enriching the data). This is data been collected is taken into account. Furthermore, linked to data as interpretation, since the positivist verify the Boeve (2018) describe the focus on the collection of data is interpretations of their collected data, with the participants. on words, which she refers as trying to understand their participants12 different perspectives in the collection

12 In this study, ‘respondent’ refers to researched object(s) with regard to positivism, and ‘participant’ refer to researched object(s) with regard to interpretivism. This terminology is based on how the positivist and the interpretivist approached their researched object(s) in their study. In

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process. Also, the interpretation of the data is validated by providing the outcomes of the collected data, with the participants (Ablinger, 2018; Boeve, 2018). Boeve (2018) state explicitly that she follows an interpretive research approach in her paper.

Table 6. Data achieved as the positivist and the interpretivist

4.5. How is Information been achieved?

How is information been achieved as the positivist? Justification

The positivist transform data into information by applying Chen (2018) and Wesselink (2018) both used statistical statistical methods to the data. The data is then presented into methods (like SmartPLS) to analyse and structure the data a structured form (data becomes information), mostly tables into a structured form. E.g. Wesselink (2018) used (Ackoff, 1999) or visuals (e.g. scatterplots), that present statistical methods to explain (erklären) correlations, an numeric (quantify) values. This can be linked with outcome given from her paper: “H7: There is no correlation information as an object, since its purpose regards to finding between storm has and the number of notifications” relationships (correlations) or patterns measured between (Wesselink, 2018, p. 9). The next example shows structured different data variables, based on quantifiable information, information of a Cronbach’s Alpha (statistical) test by Chen for accepting or rejecting hypothesis. In case of information (2018): exchange (e.g. sending or receiving surveys), it embraces a unidirectional communication between sender (researcher) and receiver (respondent), where the focus lies on exchanging the message, which can be linked with information as transmission.

Figure 3. Data structured into an informational form (Chen, 2018)

The input of the survey (information) are gathered when they are filled in by the respondent, this is after a month of collection, in case of Chen (2018).

How is information been achieved as the interpretivist? Justification

The interpretivist encapsulates different perspectives, beliefs Boeve (2018) describe that she did qualitative research in and values with regard of achieving information, this can be form of interviews to understand (verstehen) her participant linked with information as a difference. The line between data perspectives. Secondly, interviews are executed to align the and information is thin, or even blurred in case of the interpretation of gathered information with the participants interpretivist, since the information is based on enriched data, (Ablinger, 2018). Furthermore, new questions were asked where enriched data is rooted by information of the during the semi-structured interviews for gaining a rich participants views. The information exchange (e.g. image of their participant perspectives. This is referred as unstructured interviews) can be linked with information as in-depth interviews. The interviews were transcribed and communication, since the emphasis is on understanding the coded to make sense of the gathered data, or information participant perspective (bidirectional), when exchanging (Boeve, 2018). messages.

Table 7. Information achieved as the positivist and the interpretivist

case of positivism: data (e.g. survey) is been unidirectional filled in by the respondent, and in case of interpretivism: data is enriched by letting their researched objects participate in validating the collected data.

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4.6. How is Knowledge been achieved?

How is knowledge been achieved as the positivist? Justification

The positivist achieve knowledge by evaluating the Wesselink (2018) discuss the reliability of the data that information, which include discussing the reliability of the represent her study, which include: that the data might be data been collected, and how representative the data is for outdated over time, also a full data saturation is not been generalization and its use for the future (‘operationalization of reached during several setbacks occurred in the research the information’). They base their claims on their insights, process. where those claims are also linked to their tacit knowledge. An example: a positivist values different personal interest, Chen, (2018) also discuss the reliability of the data been sensed during a semi-structured interview, as biased13 used in his study. He states: “first of all, it was assumed that (Wesselink, 2018). That view is described from her participants provided their answers honestly in the survey. perspective in the discussion section of the research, which To facilitate the honest reply, participants were informed make the knowledge claim at the same time explicit. This can about anonymity and confidentiality before participants of be connected to Polanyi’s view about seeing tacit knowledge the study” (Chen, 2018, p. 12). Chen (2018) also gives as the precondition for explicit knowledge. Where tacit suggestions for future research, which include collecting a knowledge underlines all the explicit knowledge available, by larger sample size to represent a better characterization of making tacit knowledge ‘explicit’ (Tuomi, 1999). the population (generalization aspect).

How is knowledge been achieved as the interpretivist? Justification

The interpretivist achieve knowledge by making ‘sense’ of Boeve (2018) gives different stakeholder (social actors) the information, where that information is built by the perspectives voices by encapsulating their perspectives into perspectives of participants. They make sense of it by the result & discussion section of her research. After that, institutionalizing those perspectives, into their knowledge she creates a line between different perspectives, which claim, by giving the different social actors voices, which is result into a converged knowledge claim. From her paper: connectable with knowledge as a social phenomenon. (1) “the stakeholders agreed upon two points. All involved Also, the interpretivist makes use of tacit knowledge by want a more sustainable alternative for heating.” (2) “two integrating the participants perspectives into their values and stakeholders expressed their concerns with regard to time. beliefs system, when making a knowledge claim. Boeve The intensive collaboration that is needed between the (2018) does this by merging the result and discussion chapter involved stakeholders is time consuming” (Boeve, 2018, into one. This is because there is a less distinction between pp. 15-16). Also, Ablinger (2018) describe the use of social reasoning and feeling regarding the role of an interpretivist as actor perspectives in her knowledge claim. She states that mentioned in table 1. The knowledge becomes in a certain more participants should be interviewed in future, to create degree14 explicit, since the outcome of their knowledge more measurements indicators regarding her research topic; claims is described in their theses (referring to codified process mining. knowledge). Concluding the interpretivist knowledge approach: “I am also grateful to the interviewees for their kind inputs that greatly improved my insights on the energy transition, though the interpretation here remains my own” (Boeve 2018, p. 19).

Table 8. Knowledge achieved as the positivist and the interpretivist

13 Where in the case of interpretivism, a biased (person’s view) could be considered as valuable knowledge (Bryman, 2012). 14 In a certain degree explicit, since some parts of the tacit knowledge becomes explicit (Tuomi, 1999). Furthermore, this is because when knowledge is codified its losing the authentic beliefs, and values (referring to disembodying knowledge from the human mind, as mentioned before).

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4.7. The transformation of data towards knowledge in eyes of the Positivist and the Interpretivist

Based on the previous sections, a framework is created, where the transformation of data towards knowledge in eyes of the positivist and the interpretivist is clarified. Figure 4 display the positivist view, and figure 5 the interpretivist view. Only the ontological aspects that facilitate the transformation process, are absorbed and included in the frameworks.

Figure 4. The transformation of data towards knowledge in the eye of the positivist The positivist view is linkable to the DIKW hierarchy, since the transformation follows a linear path as can be seen in figure 4. Data exist in its unstructured form (e.g. data in columns), where data is retrieved unidirectional (information as transmission), (e.g. one-way sending survey, output, and one-way receiving survey, input15). It becomes information when statistical (rigid) methods structure the gathered data into a relevant form, like tables. After that, the information is discussed regarding their representativeness, which lead in understanding the information capabilities (i.e. moving to ‘knowledge’).

Figure 5. The transformation of data towards knowledge in the eye of the interpretivist

15 One might expect an arrow from information towards data, regarding the information transmission of the respondent towards the researcher (e.g. the respondent sends the filled survey to the researcher). This arrow is left out in figure 4, because the focus in the frameworks are on the transformational aspects, rather than embracing its communication exchanges on its own.

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The interpretivist follows a non-linear path in the transformation process, so therefore it does not fit the assumption of the DIKW hierarchy16, as can be seen in figure 5. Instead, data and information are less to distinguish (broken arrow lines), since data is enriched and validated with information of the participants to increase its validity (bidirectional arrows: information as communication). Because of this not clear distinction, it is unclear what is following each other regarding the transformation process. Therefore, the arrows of figure 5 are bidirectional presented. Data, or information (the two arrows towards to knowledge) becomes knowledge, when different perspectives (information as a difference) of their research focus, for example different social actors’ perspectives, are converged, into their claim of knowledge.

5. Conclusion

In this research, it is stated that the positivist and interpretivist, both have their unique way of coming to knowledge. Where the positivist studies society from the natural science point of view, the interpretivist studies society apart from using the natural science approach. This study investigated how the positivist and the interpretivist come to knowledge, when the widely recognized DIKW hierarchy is been followed. The DIKW hierarchy state that knowledge is generated by information, where information is generated by data. Also, it is mentioned that the definition of knowledge regard to ontological and epistemological considerations. For this reason, an interlinked connection is established between the ontological positions of objectivism and subjectivism, and the epistemological positions of positivism and interpretivism, for answering the following main research question in its fully:

“What are the differences and similarities in the transformation of data towards knowledge between perspectives of the positivist and the interpretivist?”

The positivist and interpretivist differ in their transformation process of coming to knowledge, when information and data are regarded as precursors towards knowledge. The positivist comes to knowledge by evaluating the insights of the information, where information is gathered unidirectional and achieved by structuring data in a relevant form. The positivist follows a linear path in coming to their knowledge claim, which is well in accordance to the assumption of the DIKW hierarchy. The interpretivist comes to knowledge by making sense of the information, which is done by encapsulating different human perspectives into their knowledge claim. The line between information and data is thin, or even blurred, because data is enriched and validated with information of the participants point-of-views. In short, they differ, the positivist follows a linear-path in achieving knowledge from having information and data as precursors, where the interpretivist follows a non- linear path in achieving knowledge from having information or data as precursor(s). The only similarity lies in, that the positivist and interpretivist, in relation to the DIKW assumption, both eventually generate knowledge!

16 Chipo (2016) claimed in her paper: “if the DIKW follows the positivism root, it therefore shows that it is aligned to quantitative approach. Knowledge and its management cannot ignore the qualitative root. Qualitative paradigm should be considered in knowledge management since knowledge is difficult to define and to measure beyond any dispute” (p. 68).

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6. Discussion, Limitations and Future research

In this research, it is unfolded that the positivist and the interpretivist both have their own way of reaching knowledge, from having information and data as precursors, towards that generation of knowledge. However, this conclusion did not emerge without limitations occurred in this research, which is discussed in this chapter along with side-views, implications and suggestions for doing future research in this area. The first limitation regard to the amount of research studies been researched. Due the time constraints of three months to finish this study, where much time is dedicated in building the conceptual framework (CF), 4 Master’s theses could be researched. This amount may affected the reliability of this study, which mean it is difficult to state if the (exact) same results would apply, if more studies would been investigated. Also, due this amount limitation, it is difficult to generalize this study to a wider population. This lead demographic & geographic shortcomings being the second limitation. In this study, only Master’s theses of the University of Amsterdam are investigated with relation to positivism and interpretivism, which make this study difficult to generalize beyond other researchers, cities and countries, then the Netherlands. Future research to a wider (more), and diverse population is recommendable to increase the validity and reliability of these research outcomes. On contrast, the reliability in this study is to a certain extent grounded, because the transformation of data towards knowledge in eyes of the positivist and interpretivist, builds on the widely accepted DIKW hierarchy. Where data, information and knowledge are also separately defined from the ontological theory of objectivism and subjectivism, to let it act as frame of reference (validation), and at the same time facilitate the process of how the positivist and interpretivist comes in achieving knowledge from information and data as precursors. Although, one has to keep in mind that ontology is a discipline on its own, therefore the researcher only abstracted the ontological aspects what was believed would facilitate and fit the transformation process of data towards knowledge, in eyes of the positivist and the interpretivist. Furthermore, Field research is advisable to validate if data, information and (tacit) knowledge is indeed perceived as data, information or (tacit) knowledge. Also, it would be interesting to discover where the line lies between data and information, and if there is a line in case of the interpretivist. In case of the positivist, it could be interesting to validate if IT-databases are indeed used for the ease of data retrieve, thanks to capabilities of technology, as mentioned by Rowley (2007) in her paper. In the next two sections, the focus is on the contribution of the results of this research, with regard to the scientific and practical area.

6.1. Scientific Implication (contribution of this research)

This research showed the DIKW hierarchy is applicable in case of the positivist, but not in case of the interpretivist. It is therefore questionable if the known DIKW hierarchy cover different standpoints or perspectives. Maybe, the validation of the DIKW articulation needs to be (re) re-investigated according to the findings of this research? However, this research provided some insights of how the positivist and interpretivist comes in achieving knowledge from information and/or data. Those insights may create some awareness in achieving knowledge as a positivist or interpretivist. Having awareness might be useful when articulating ‘your’ knowledge claim as a positivist or interpretivist. Where does your knowledge claim come from? Is your knowledge based

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by previous steps, and if so, are those steps in line with your knowledge claim? And what does those steps mean to your definition of knowledge? Does it mean anything?

Figure 6. “The art and science of asking questions is the source of all knowledge.” ―Thomas Berger (Miles, 2018)

6.2. Practical Implication (contribution of this research)

Translating the outcomes of this research to a practical setting, it can be argued that the positivist transformation route is well aligned with the frame of business intelligence (BI). BI transform data into representable information for the ease of organizational decision-making process. BI tools gather, extract and analyse data, where its findings are presented into (structured forms) dashboards, graphs or other related graphical forms. The outcome of the structured form (information) represent the state of the business, where that information can support managers in making business decisions (Pratt, 2017). Having this knowledge, business decision makers should (always) keep mind to which extent the presented information is funded on reliable and/or representative data, to secure the validation aspect of the business decision (knowledge act) in question. Moving to interpretivism: fake news is a trending topic nowadays, especially with relation to the elections (Hunt, 2016). The goal of fake news is to distribute disinformation in form of news to influence the public opinion or obscure the truth (Library Research Guides, 2018). Translating fake news to an organizational form, then the interpretivist manager should be aware of their employees world view when facilitating a business decision based on that view. The manager can question himself: ‘are the needs of my employees founded on true information?’ However, what is true is always questionable and can differ due multiple view on reality, in case of interpretivism (Greener, 2008). The point here is to be aware as a manager when making business decisions based on ‘your’ employees’ voices or perspectives. The interpretivist manager is linkable to the red-print change agent, which state: “the foremost consideration of the red-print change agent is that the human factor plays a vital role” in the change (De Caluwé & Vermaak, 2004, p. 13).

6.3. Counterargument (a side-view)

Returning back to a sentence of the introduction: “Is data really a precursor in the hierarchy towards knowledge? Or is data founded on the knowledge of the human mind?” In this research, the assumption of the widely recognized DIKW hierarchy (Rowley, 2007) is been followed, which state that data generate information, and information generate knowledge (Ackoff, 1989). However, a counterargument is been

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made by Tuomi (1999), where knowledge is been seen as the input for data creation. Tuomi uses an example of a computer database entry, to describe his reversed view: (1) knowledge is been decontextualized from the human mind and structured; (2) into digital documents for example, here knowledge becomes information; (3) then, the information is splitted into ‘atoms’ and put into separate data-columns in databases, where the atoms have no meanings on their own, now data is created (Tuomi, 1999). Jennex (2009) state that Ackoff (1989) and Tuomi (1999) are both right and wrong. Jennex (2009) writes that sometimes data is collected without a specific reason, in his paper he provides an example of data collection through a video-monitoring. In a situation, data of video-monitoring was collected continuously, and only retrieved when something unexpected happened in the range of what could be captured by the video-monitoring. The collected data could provide some interesting insights, what might cause the unexpectedness. Here, data becomes (after later analysis) information or knowledge. A second example by Jennex (2009) relates to data-mining in finding patterns in the data to reveal their (hidden) information. Transforming it into information can be based on the influence of making use of prior (current) knowledge. Concluding, these two examples showed that the flow creation is in both directions (Jennex, 2009). Having the reversed hierarchy discussed, it might be interesting to discover if the positivist and the interpretivist make use of prior knowledge in coming to their data collection and filtering. And what does this mean for achieving knowledge as a positivist and interpretivist? Is knowledge then not already achieved before it is achieved? A suggesting think model, which can be used for future research, is provided in figure 7:

Figure 7. Suggesting think model for investigation

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Annex A: The Information Theory of Shannon

Claude E. Shannon, known as ‘the father of information theory’, views information as physical quantities which he elaborates furthermore in his paper: A Mathematical Theory of Communication (Shannon, 1948). He defines his concept by explaining a general communication system, which focus on the transmission of information. This general communication system is shown in figure 8.

Figure 8. General communication system of Shannon’s paper (Shannon, 1948) In short, the system starts with generating a message, for example a letter (information source), that has to be transmitted. The message then reaches the transmitter stage where the message is been deconstructed17 to become ready to be sent. Shannon clarifies this stage by providing an example of a telephone, where the voice sound of the telephone is been converted into an electrical current (signal) to be able to be sent over the channel (black lines with arrows). The channel is the medium the signal travels from the transmitter towards the receiver. The channel can be an internet cable for example, or a telephone line and such. The deconstructed message (as is it is been deconstructed by the transmitter), will be reconstructed by the receiver for the destination. Finally, the message reaches its destination as predetermined by the information source. The destination can be a person (Shannon, 1948). One stage is not been discussed, which is the noise source stage present in the bottom of figure 8. Shannon (1948), describes two kind of noises in his paper. The first one is predictable noise and the other one is white (gaussian) noise (unpredictable noise), the last one could increase the uncertainty18 a message (information) reach its destination as intended by the information source. Noises can interrupt the meaning of the original message, as it attacks the original message with its own information. This is information that is unwanted by the transmitter and the receiver. “This means that the received signal is not necessarily the same as that sent out by the transmitter” (Shannon, 1948, p. 19). Boisot (1998) describe noise in relation to signal strength in his book Knowledge Assets, Securing Competitive Advantage in the Information Economy as: “one way of measuring the efficiency of information transmission is to compare a signal’s strength with that of the background noise that accompanies it. If one adds more noise to a fixed signal this will usually lower the signal-to-noise ratio” (p. 10).

17 Splitted into smallest ‘chunks’ of data, i.e. bits having the ability of possessing only two values; 0 or 1 (e.g. 0 = OFF or 1 = ON). Which can be further associated with the Boolean data type: ‘true’ or ‘false’ (Rouse, 2017), or the lowest level of the question hierarchy by Vogt, Brown, and Isaacs (2003), which question: ‘yes’ or ‘no’. 18 Shannon calls uncertainty ‘entropy’ (Shannon, 1948).

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