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INTEGRATING POSITIVIST AND INTERPRETIVE APPROACHES TO INFORMATION SYSTEMS RESEARCH: A LAKATOSIAN MODEL |
| September 13, 2000 | |
ABSTRACT |
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| While research in
information systems (IS) has traditionally been dominated
by the positivist approach, the interpretive approach to
IS research has in recent times gained significant
attention. Radically different views and assumptions
about the nature of science, and methodologies for doing
science underlie the positivist and interpretive
positions. While descriptions of the two approaches and
their respective roles in shaping IS research have been
discussed elsewhere, no attempts have been made to
present an integrative model that can help reconcile some
of the differences. This paper provides such a synthesis
by adopting the Lakatosian Structured Methodological
Falsification (SMF) model as the basis for the synthesis.
The Lakatosian perspective argues in favor of the
existence and desirability of multiple theoretical
foundations in a discipline, and blends together both the
traditional tenets of positivism (i.e. falsification of
theories) with the more contemporary interpretive notions
of science such as social context. A reconstruction of IS
research using the SMF model could potentially capture
the relative strengths of the positive and interpretive
philosophies, while at the same minimize the limitations
of the two perspectives. |
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CONTENTS
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1. INTRODUCTION |
Ever since Keen (1980) fired the opening salvos by criticizing research in Management Information Systems (MIS) for lacking a core theory, debates about the scientific basis for the discipline have continued unabated (Bariff and Ginzberg 1982; Hamilton and Ives 1982; Benbasat 1984; Culnan 1986, 1987; Hirschheim and Klein 1989; Orlikowski and Baroudi 1991; Benbasat and Zmud 1999; Davenport and Markus 1999). MIS has been described as (a) an applied discipline whose concepts and relationships are derived from the underlying fields of behavioral sciences, economics, and computer and management sciences (Bariff and Ginzberg 1982); (b) as a discipline with a heterogeneous focus that makes it difficult to define the hard core of MIS (Culnan 1986, 1987; Culnan and Swanson 1986; Benbasat and Zmud 1999) and (c) as a field that is in the pre-paradigmatic stage of development (Culnan 1987).
More recently, the debate about scientific status of the discipline has centered around the meta-theoretical underpinnings and the dominant philosophical assumptions that shape the work of researchers in the discipline. While there is strong evidence to suggest that research in MIS has thus far been dominated by the logical positivist model of science, following Weick's (1984) call for intensive research, there has been a growing interest in the interpretive perspective of science (c.f. Hirschheim 1984; Klein and Lyytinen 1984; Vitalari 1984; Kaplan and Duchon 1988; Weill and Olson 1989, Orlikowski and Baroudi 1991; Klein and Myers 1999; Walsham and Sahay 1999; Trauth and Jessup 2000). Many IS researchers now believe that a change from a purely positivist approach to science is not only appropriate but also necessary for growth in information systems research. Early criticisms from IS researchers such as Klein and Lyytinen (1984) who argued against the mindless adoption of the positivist approach to IS research, has been followed by repeated prescriptions for research approaches that are more appropriate for IS, where the characteristics of natural sciences research namely repeatability, reductionism, and verifiability may be inappropriate. Galliers (1984) also noted that the empirical model of science ignores the potential for different interpretations of social phenomena, the impact of the researcher on the social system being studied, and the confounding associated with human judgment. Weill and Olson (1989) in a survey of IS research, also criticized the positivist epistemological perspective of IS as constituting a naive meta-theory which has constrained the development of the field. More than a decade later, while we have seen greater evidence of an interpretive movement in IS research (c.f. Avison and Myers 1995; Harvey 1997; Harvey and Myers 1995; Walsham 1995; Walsham and Sahay 1999; Prasad 1997). Nevertheless, the positivist approach continues to dominate IS research, and moreover no reconciliation of the underlying principles of these approaches has ever been attempted.
Given the radically different views and assumptions that underlie the positivist and interpretive philosophies of science, it seems appropriate to attempt some sort of a Hegelian synthesis of the two models. The purpose of this paper is to attempt such a synthesis adopting the Lakatosian Structured Methodological Falsification (SMF) model as the basis for the synthesis. The Lakatosian perspective argues in favor of the existence and desirability of multiple theoretical foundations in a discipline, and blends together both the traditional tenets of positivism (i.e. falsification of theories) with the more contemporary interpretive notions of science such as social context (Leong 1985). It is hoped that a reconstruction of IS research using the SMF model would incorporate the relative strengths of the positive and interpretive philosophies, while at the same minimize the relative drawbacks of the two perspectives. This viewpoint is also supported by Lee (1991) who argued that positivist and interpretive approaches to IS research need not be viewed as mutually exclusive. Rather they can be mutually supportive.
We begin by presenting brief sketches of the positivist and interpretive philosophies and their respective roles in shaping IS research. This is followed by a description of the SMF model, and a comparative analysis of the positivist, interpretive and SMF models of science. Finally, we show how the SMF model can provide a useful metatheory for guiding IS research.
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2. THE POSITIVIST PHILOSOPHY AND ITS ROLE IN IS RESEARCH |
The logical positivist (empiricist) philosophy assumes that science is objective and emphasizes rigorous measurement and hypothesis testing. Characterized chiefly by the inductive statistical method of research, the central doctrine of this model is the verification theory of meaning, which states that statements or propositions are meaningful only if they can be empirically, verified (Brown 1977). Subsequently, however, this stance posed a problem to the positivists since they could no longer accept the axioms of the system as unconditional truths (as these could not be empirically tested). The positivists were thus forced to give up the verifiability criterion and tolerate some modifications to their theory.
Carnap (1936) developed a more moderate version of positivism called logical empiricism, which became the 'received view' in the philosophy of science for the next twenty years or so (Suppe 1977). He replaced the concept of verification with the more liberal testability criterion, which addressed the problem that axioms posed to the verifiability principle. While agreeing that that all scientific statements be empirically testable, Carnap proposed replacing verifiability with the notion of "gradually increasing confirmation." The abandonment of verification also meant that the knowledge claims of science could no longer be considered positive, and thus the term logical empiricism came into being (Hunt 1991).
According to the tenets of logical empiricism, scientific progress in any discipline begins with the untainted observation of reality. This is expected to provide the researcher with an image of the real world from which (s)he cognitively generates an a priori model of the process to be investigated. Hypotheses are derived from the model and are subjected to empirical tests and if the data supports the hypotheses, then a confirming instance is recorded. Thus science progresses through the accumulation of multiple confirming instances obtained under a wide variety of circumstances and conditions (Anderson 1983).
The positivist philosophy, suffers from several limitations, especially when applied to social sciences. First, this approach, based on the inductive statistical method, generalizes a universal statement of truth from observations of a certain number of positive instances. The strict inductionist approach is often inappropriate because speculation and creation of an a priori hypothesis are essential for a systematic procedure of theory building (Leong 1985). Second, the empiricist approach is based on the notion of pure observation, which is impossible in research, especially in social sciences, since observations are always subject to measurement errors (Anderson 1983). Finally, this approach assumes that knowledge is derived from an objective interpretation of assumptions, without any of the subjective biases or a priori knowledge of the scientist coming into play. Thus, the empirical approach emphasizes rigor and holds that all sciences must imitate the methods that have led to progress in the mathematical and natural sciences.
A salutary aspect of the positivist approach to information systems research is that it has led to a focus on the need for good tools and methods that could safeguard against the fallibility of the human mind. This is especially important considering the technical complexities of systems analysis and development (Klein and Lyytinen 1984). Substantial contributions to IS research have emerged due to the adoption of this model of science. For example, research on methods of structured programming, programming algorithms, and the formalization of systems analysis and design can be viewed as possible research strategies that safeguard against subjectivity in IS development. These branches of research are largely motivated by the positivist need for clarity and conciseness of representation. Likewise, Trauth and Jessup (2000) argue that much of the IS research in the area of group support systems (GSS) has been conducted within the positivist paradigm with a focus on understanding where and how the technology can be fruitfully applied. The positivist paradigm is reflected in the goals of the research attempting to quantify social reality and subject it to experimental controls and hypotheses testing.
The dominance of the empirical approach to IS research has however led to criticism that IS research has frequently sacrificed relevance for rigor. For example, the single-minded pursuit of rigor in IS research has led to the development of highly formalized models of information processing and decision making with questionable assumptions (e.g. rational decision making, availability of perfect information, etc.). Another danger of the positivist approach when applied to practical problems is the narrowing of the problem scope to those aspects that are researchable by standard quantitative methods. The exclusive reliance on statistical testing of hypotheses has been criticized in the social sciences as providing disastrous effects (Cook and Campbell 1979, p. 92). The simplification and abstraction required for good experimental designs often remove interesting features from the subject of study. Kaplan and Duchon (1988) argue that the "stripping of context buys objectivity and testability at the cost of a deeper understanding of what actually is occurring" (p. 572). By ignoring the subjective and inter-subjective dimensions such as power, politics, and other socially constructed variables, the purely empirical approach fails to produce deep insights into IS phenomena (Klein and Lyytinen 1984; Markus et al. 1986; .
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THE INTERPRETIVE MODEL AND ITS IMPLICATIONS FOR IS RESEARCH |
The interpretive philosophy is based on the belief that science is subjective and therefore allows alternative models of reality. It emphasizes the creative aspects of science, and is in many ways the polar opposite of the positivist philosophy. While the positivist model dismisses factors such as the social interaction and influence among researchers, the idiosyncrasies of the individual researchers, and the researcher's subjective interpretations as being irrelevant to the research process, the interpretive perspective emphasizes the importance of such factors for an understanding of how scientific knowledge develops (Peter and Olson 1983). In fact both Kuhn (1970) and Popper (1972) have pointed out that observations are always interpreted in the context of the researcher's knowledge and mental models. For example, Kuhn explicitly states that "what a man sees depends upon both what he looks at (observations) and also upon what his previous visual-conceptual experience has taught him to see" (Kuhn 1970, p.113).
The interpretive notion contends that science is an ongoing social process and that the full epistemic understanding of scientific theories can only be achieved by observing the dynamics of theory development. Checkland (1981, p. 68-91) summarizes the distinguishing features of social science vis-à-vis natural science as (a) the likelihood of many different interpretations of the social phenomena; (b) the impact of the social scientist on the system being studied and; (c) the difficulties associated with forecasting future events concerned with human societal activity. It is precisely these aspects of research that the interpretive model takes cognizance of. It acknowledges and even encourages diversity in the practice of science. Moreover, interpretive researchers admit that their own knowledge claims are clearly a function of social, cultural, and cognitive factors that impinge on their research (Peter and Olson 1983).
Unlike the empirical orientation, the interpretive orientation conceives many possible realities, each of which is relative to a specific context or frame of reference. The social agreements between researchers about the meanings of the theories and about the empirical observations, provides the necessary guarantee for the theories. This method of theory validation also makes the process of science a social activity governed by interacting researchers. Finally, this model shatters the myth of objectivity of science and asserts that all observations are influenced by a multitude of factors, including past experience and training. The interpretive school of thought takes the position that people, and the physical and social artifacts they create, are fundamentally different from the world of natural science (Lee 1991). This view is pertinent to IS research for several reasons. First, since the human element is inextricably linked with the technological aspect of IS research, it is only appropriate that the underlying philosophical perspective mirrors the links. Second, it effectively overcomes the problems associated with the pure empirical paradigm, which views the construction of information systems as merely technical artifacts (Cooper 1988). Finally, this view has led to the development of several research programs in IS where behavioral research issues abound. These range from (a) issues related to evaluating and motivating system personnel, (b) managing the human aspects of systems development and implementation, (c) human-computer interaction, (d) issues pertaining to group coordination and communication, (e) managing the impact of information technologies on organizations planning and control strategies and (f) adjusting the labor force to a changing mix of skill requirements.
The interpretive perspective also advocates the use of multiple methodologies for conducting research. The methodological singularism of the empiricists has been criticized as a tendency to "force all problems into the molds of one or two routine techniques, insufficient thought being given to the real objectives of the investigation or to the relevance of the assumptions implied by the imposed method" (Box 1976, p.797). The significance of the interpretive model lies in its call for doing away with the notion of a single approach for research. In spite of the dramatic and salubrious shifts that the interpretive perspective brings to the MIS research, researchers have been cautioned against blindly adopting the principles of interpretive thought and methodological pluralism without a deeper examination of the limitations, assumptions, and relevance of the methodologies to their research. For example, Bjorn-Anderson (1984, p.275) cautions IS researchers from falling into the trap of bad pluralism which could lead to a form of corrosive relativism, by noting that "bad pluralism is when we degenerate into absolute relativism, while good pluralism opens up new perspectives for research."
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THE SMF MODEL: A SYNTHESIS OF THE POSITIVIST AND INTERPRETIVE APPROACHES |
If the philosophy of science models are placed in a continuum where the positivist and interpretive approaches represent the two extremes, the sophisticated methodological falsification model (SMF) would appear somewhere near the middle (Leong 1985). The positivist approach makes the claim that the methods used in natural science are the only true scientific ones, while the interpretive researchers make the counterclaim that the study of people and their institutions call for methods that are very different from those of natural science (Lee 1991). It appears that the SMF model proposed by Lakatos (1978) could serve as a Hegelian synthesis of the two extremes and offer a reconciliation of the apparently irreconcilable positions of the empiricist and interpretive approaches.
The Lakatosian SMF model introduces the notion of a research program, much like the Kuhnian notion of a paradigm. The research program consists of the hard core of fundamental assumptions and theoretical propositions widely accepted by the scientists within that research program, and is similar to Guba's (1990) description of the basic set of beliefs that guide action. Surrounding the hard core is a protective belt of auxiliary hypothesis and mid-range theories. This protective belt has to bear the brunt of tests and get re-adjusted or completely replaced. Lakatos emphasized that every research program needs a positive heuristic and a negative heuristic. The positive heuristic is a set of partially articulated methodological rules or hints on how to change or develop the research program, while the negative heuristic suggests paths of research that should be avoided and is generally used to defend the hard core.
In a Lakatosian research program, successor theories are formed with the aid of the positive heuristic by adding additional clauses to the predecessor theories. The research programs are evaluated in terms of their progressivity. A research program is theoretically progressive if modifications to the program lead to novel findings and is empirically progressive if at least some of the novel predictions are corroborated by data. A research program is degenerative if its positive heuristic no longer helps generate novel findings. Abandonment of a research program takes place when a rival research program supersedes it. This occurs if (a) the rival program accounts for all the truths of the former program, (b) the rival program offers excess corroborated empirical evidence over the former and (c) the former program is degenerative.
Leong (1985) provides a comparative analysis of the scientific bases and methodological positions adopted under the positivist, interpretive, and SMF models. Following Chua (1986) and drawing from the work of Leong (1985) we provide a comparison of the three perspectives on the basis of their (a) beliefs about physical and social reality; (b) beliefs about knowledge and; (c) beliefs about the relationship between theory and practice.
Beliefs about physical and social reality
The positivists contend that science is objective, and can be understood without considering cultural, social, political, and economic factors. Science will eventually discover the "true" nature of reality and that only the logic of justification is needed to understand science. On the other hand the interpretive researchers hold that science is subjective, that science creates many realities, and that the social process by which knowledge is created influences what is learned. Consequently, science can only be understood in terms of the cultural, social, economic and political processes that shaped its development. The SMF model effectively combines these perspectives by arguing that the demarcation of science from pseudoscience is objective in the world of ideas and propositions. However, theoretical commitment to ideas is influenced by the scientist's mental states, beliefs, and consequences. Thus while criticism of scientific theories may be based on empirical evidence, the abandonment of a research program involves the a coalition of ideas and beliefs of the community of scholars that is involved in the research program.
The distinction between criticism of a research program and its abandonment can be illustrated with an example from IS research. Over the years, considerable attention has focused upon cognitive style as a basis for DSS design. Huber (1983) in a stinging criticism of the research program on cognitive styles concluded that cognitive styles should play hardly any role in IS design. He suggested that since there was little empirical evidence that the inclusion of the cognitive style variable led to better IS design and use and that the research resources allocated for these programs were a misallocation. Huber, in fact, went so far as to advocate the abandonment of this stream of research. However, in a subsequent study, it was shown that by changing the focus to cognitive processes rather than styles, useful operational guidelines for DSS design could be obtained (Ramaprasad 1987). It is arguable if researchers today would advocate the abandonment of cognitive styles/process research program, as the community of scholars engaged in this stream of research continue to make positive contributions. Interpreted in the SMF context, it suggests that the positive heuristic of this research program is still able to generate novel findings.
Beliefs about knowledge
It is the positivist belief that science is a rational process, since it follows the formal rules of logic and that scientific theories come closer and closer to the absolute truth. Furthermore, strict scientific rules should govern the processes of knowledge creation and theory development. The interpretive position on knowledge holds that truth is subjective and cannot be properly inferred outside of the context provided by theory. It is rational only to the extent that it seeks to improve individual and societal well-being by following whatever means are useful for doing so, and that there are many ways of doing science validly, that are appropriate in different contexts. The Lakatosian SMF framework suggests that while absolute truth may not be achievable by science, scientific research programs should in the long run lead to ever more true and fewer false consequences, and thus have increasing plausibility. In the development of science, researchers must aim at maintaining consistency as an important regulative principle, and treat inconsistencies as problems that must be addressed. While strict rules on methodology may be unnecessarily restrictive, norms for doing good science must be established. Research programs must be evaluated by their heuristic power - how many new facts do they produce and how capable are they in explaining refutations during their development.
An example of a Lakatosian research program in IS can be found in the research stream that has focused on the "business value" of information technology investments. Over the years, numerous studies have focused on the economic advantages accruing to businesses that invest in information technologies (IT). This research stream, however, has been dogged by confounding empirical results. While some studies have found that investing in IT augments firm performance, others have found that it mitigates performance, while still others have found that it is unrelated to business performance (see Kaufmann and Weill 1989; Attewell 1993; Brynjolfsson 1993; Wilson 1993 for reviews). Furthermore, articles in the popular press, citing the studies that found weak or no returns to IT spending, dubbed the whole phenomenon as the "productivity paradox" and called for a reassessment of investments in technology. Continued theoretical work in this area has however led to more integrative models of IT-firm performance (Bharadwaj et al. 1995) and better explanations for the seemingly contradictory findings (Bharadwaj et al. 1999; Bharadwaj 2000). For example, Hitt and Brynjolfsson (1994) show that the productivity paradox can be resolved by focusing on two different measures of firm performance - output and profitability. Using a variety of dependent variables, they show that while investments in computer capital have no effect on profitability measures such as return on assets and return on equity, they are positively correlated with firm productivity and consumer value. Thus, while it may be fair to conclude that we do not know what the "absolute truth" is with respect to the question of business value of IT investments, this research program has exhibited cumulativity and has progressively given rise to better models with greater validity and predictive powers. The heuristic power of this research program can be described as "high", as it has led to the development of new facts (e.g. IT investments have served to increase consumer value) and has helped generate explanations for the contradictory findings.
Beliefs about relationship between theory and practice
The relationship between theory and practice in the positivist philosophy is purely technical. Positivists generally believe that scientific inquiry is value-free and that researchers are impartial observers of phenomena. In general, the positivists believe in (a) the empirical testability of theories (b) that data provide objective independent benchmarks for testing theories and (c) that measurement procedures do not influence what is measured. This is in sharp contrast to the interpretive position that holds that researchers can never assume a value neutral stance, and may even influence the phenomenon being studied. Thus, the interpretive position holds that (a) science grows by the accumulation of supportive, confirmatory evidences (b) data are created and interpreted by scientists in terms of their own subjective beliefs and are thus value-laden and (c) the process of measuring a phenomenon often ends up changing the phenomenon. The SMF model combines these divergent perspectives by suggesting that although "absolute truth" may not be achievable by science, scientific research should in the long run yield ever more true and fewer false consequences (Leong 1985). Furthermore, a theory is scientific only if it is has corroborated excess empirical content over its rival, i.e. only if it leads to the discovery of "novel" facts. While "subjective experience" may serve as an important arbiter in research, good methodological decisions are needed for conducting proper science, and norms for doing good science must be clearly laid out. Empirical testing of a theory provides the necessary but not the sufficient condition for refutation of theories. In other words current theories cannot be abandoned merely by empirical falsifying, but need the emergence of newer and better theories that will ultimately displace the weaker theory.
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CONCLUSION |
Analysis of IS research based on the SMF approach requires a delineation of the central core of the IS discipline, and the protective belt of research programs that surround the core. A useful starting point for determining the basic tenets of IS research was provided by Mason and Mitroff, when they defined information systems as:
An information system consists of at least one person of a certain psychological type who faces a problem within some organizational context for which he needs evidence to arrive at a solution and that evidence is made available to him through some mode of presentation (Mason and Mitroff 1973, p. 475)
This definition helped delineate the focus of research in IS, establish the boundaries and interface between IS research and other academic disciplines and, identify the relevant variables for investigation. It established that an understanding of computer and communication technologies needs to be combined with an understanding of organizations and management, as well as cognitive and behavioral aspects of human psychology. Consequently, IS research over the years has largely focused on aspects pertaining to (a) individual approaches to IS design and use; (b) organizational approaches to IS design and use and; (c) IT management (Orlikowski and Baroudi 1991).
Information systems research is increasingly viewed as a social science that seeks to explain the effects of information technology on individuals, groups, and organizations, and establish the criteria for effective development, deployment, and use of such technologies. Positive heuristics for guiding research are very important for a rapidly evolving field like IS. While such heuristics should not be too restrictive thereby limiting the scope of research, a total lack of positive heuristics would result in unfocussed and fragmented research. Recent articles and editorials providing prescriptions for valid and relevant IS research (e.g. Benbasat and Zmud March 1999; Davenport and Markus 1999; Lee 2000) and panel discussions on the status of the discipline (e.g. AIS and ICIS conferences) have been useful for structuring and guiding research programs in information systems. The various intellectual sub-fields that characterize IS research have also been identified through bibliometric studies (Culnan 1986, 1987; Culnan and Swanson 1986). These studies have stressed the importance of a variety of research paradigms for IS. This conception is very much in line with the SMF framework, which also urges the existence and desirability of multiple theoretical foundations within a discipline.
Finally, debates on the proper methodologies for IS research have led to a growing recognition that IS research must emulate research in other social sciences and deploy a wide variety of methodological approaches. Research in identifying non-traditional methodologies for IS have been carried out. For example, Galliers (1984) lists the qualitative methodologies appropriate for IS as futures research (Barnett et. al. 1981), phenomenological research (Boland 1985), and action-research (Wood-Harper et. al. 1985). More recent attention to intensive research and special issues in journals such as MIS Quarterly (March 1999 and March 2000) devoted to studies that employ intensive research techniques bear further testimony to the growing acceptance and popularity of methodologies that augment the pure empirical approach. The insights gained by adding an interpretive lens to IS research focuses on the need to include new variables in IS research such as trust, social capital and collaborative relationships (Kumar et al. 1998). The SMF perspective, which argues against methodological singularism, but at the same cautions against methodological anarchy, can serve as a useful model for guiding research. It emphasizes the need for selecting the appropriate methodology from a variety of techniques after assessing the problem-solving efficiency, advantages, and limitations of the research methods. It suggests that the decision to employ qualitative methods should be based on whether these methods can provide additional insights, beyond those furnished by quantitative methodologies. As IS research moves into more exciting and challenging domains such as ecommerce, virtual organizations, and other emerging and emergent technologies, a mix of methods is not only appropriate but also necessary for the proper understanding of the interactive system effects between the technological and organizational aspects of information systems.
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