筆記- CRITICAL REALIST PHILOSOPHY AND RESEARCH METHODS

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TESTING MANAGEMENT THEORIES: CRITICAL REALIST PHILOSOPHY AND RESEARCH METHODS

 

作者
KENT D. MILLER and ERIC W. K. TSANG

 

出處：
Strategic Management Journal Strat. Mgmt. J., 32: 139–158 (2010)

 

OBSTACLES TO TESTING MANAGEMENT THEORIES

為何社會科學的理論很難實證或否証？

作者區分以下點來看

1.      the nature of the social phenomena that management researchers investigate,

2.      imprecise and fragmented theorizing,

3.      inadequate research designs,

4.      and inevitable reliance upon untested assumptions.

 

1. Nature of social phenomena

First, organizations are diverse, complex, and changing social phenomena, with multiple levels of analysis, as well as multiple and contingent causal processes (Astley and Van de Ven, 1983; Fabian, 2000). Owing to our inability to identify all the pertinent variables, we are often unable to state precisely the conditions on which different types of behavior depend or even the contingencies that make particular theories relevant to certain empirical contexts.

組織與社會現象不是封閉的靜態系統，沒有辦法釐清邊界條件與所有相關因素

A second factor that renders theory testing difficult concerns the element of personal volition in human behavior.

Although contextual factors have important influences on organizations, Child (1972) submits that organizational decision makers are not passive. They exercise choice and take actions that enact their organizations’ environments.

當事人有自由意志與決策權(反應不見得完全受制於環境)

Third, researchers’ activities may change the beliefs and practices of managers and thereby undermine the continuity of the phenomena investigated (Numagami, 1998). Self-fulfilling and self-defeating prophecies are not causes for concern in the natural sciences.

Moreover, researchers and managers are related to one another such that researchers themselves can, at times, be direct causal agents in organizational actions (Bradbury and Lichtenstein, 2000).

研究者在一旁觀察可能會改變當事人的行為反應模式

 

2. Theorizing

One of the primary obstacles to testing management theories is their imprecision.

Imprecise wording of hypotheses can make them logically nonfalsifiable.

構念、測量、理論與命題都不明確，所以無法否証

Donaldson (1995) portrays management theories as oriented toward different levels of analysis and different value assessments, as well as having distinct languages and methodologies. For each theory, there is a dedicated group of scholars working on research to test hypotheses derived from their theory. To establish their distinct niches within a research field, groups of researchers emphasize how their theories differ from one another (Mone and McKinley, 1993). Over time, barriers between groups of theorists grow (Aldrich, 1992).

不同階層、對象與方向的研究各自形成領域與障礙，彼此之間的觀點（方法論、本體論、認識論）不同；難以對話與評斷，也不可能進行整合或比較

In the natural sciences, a common view is that there can be only one true theory explaining any particular phenomenon. Thus, researchers espousing competing theories are keen to provide not only evidence that supports their theories but also evidence that challenges rival theories.

針對自然現象，往往只有一種理論

By contrast, among management theorists, it is generally accepted that the same phenomenon can be explained by different theories (Allison, 1971; Ghoshal, 2005). If explanations are not mutually exclusive, greater space is opened up for researchers to create original theories that provide novel explanations.

而在社會科學，某一社會現象則可以包容許多不同的解釋

藉由不同理論的角度，可以更整體的看到大象的全貌，而後遺症在於

By avoiding clearly stating competing hypotheses and digging into the evidence, empirical claims within our theories are never at risk in such debates.

 

3. Research design

Most tests of management theories are not conducted under the conditions of a closed system, which is defined as ‘one in which a constant conjunction of events obtains; i.e., in which an event of type a is invariably accompanied by an event of type b’ (Bhaskar, 2008: 70).

We can learn more from disconfirming cases than from confirming cases, yet our reasoning and sampling tend to have a confirmatory bias (Wason and Johnson-Laird, 1972). The originators of a theory may hold it with such conviction that they—consciously or unconsciously—pay attention primarily to supporting results.

Researchers tend to adopt what Klayman and Ha (1987: 211) call a ‘positive test strategy,’ that is, examining instances in which a theory is expected to hold. ‘A theory-confirming researcher perseveres by modifying procedures until prediction supporting results are obtained’ (Greenwald et al., 1986: 220). Journal review processes reinforce this confirmatory orientation by favoring theory supporting results (Feige, 1975; Pfeffer, 2007)

欸，一些學術文章為求發表，結果往往讓人感覺too good to be true….（paper industry和加防腐劑/調味料的黑心食品，一樣有害身心…）

 

4.Unavoidable assumptions

Because of our unexamined assumptions and human fallibility, and our limited access to data, our conclusions are always tentative and our generalizations are risky; nevertheless, researchers venture bold claims about the relevance of their findings in settings beyond those studied.

Theories are never tested alone. Rather, they are tested together with other untested auxiliary hypotheses, which include background assumptions and rules of inference.

When a finding does not support a theory, one can argue that the problem resides in the failure to control for certain conditions of the theory, rather than in the theory itself (Nooteboom, 1986).

Testing management theories under the conditions of an open, rather than closed, system further aggravates the problems associated with isolating the relations of interest from other confounding effects. Specifying the boundary conditions of a theory is critical to advancing theory testing.

 

 

 

CRITICAL REALISM AND THEORY EVALUATION

科學哲學基礎派別(幼幼版)

 

1.Epistemological stance

Critical realism interrelates ontology and epistemology. On the one hand, it posits a realist ontology, that is, the existence of a world independent of researchers’ knowledge of it. On the other hand, critical realism holds to a fallibilist epistemology in which researchers’ knowledge of the world is socially produced.

Lacking an indubitable basis for science, we can, nevertheless, reasonably assert the veracity or falsity of scientific theories—albeit, not definitively. A critical realist perspective affirms the possibility of truthful knowing but acknowledges that human limitations undermine claims to indubitable or objective knowledge.

 

2. Mechanisms as explanations

The real domain consists of generative mechanisms, which refer to ‘the ways of acting of things’ (Bhaskar, 2008: 14).

Through enabling or preventing change, mechanisms give rise to events in the actual domain.

Some events are experienced, and some are not. The empirical domain is made up of events experienced through direct or indirect observation, such as detection through instruments.

Mechanisms can be—but need not be—stable, whereas events and empirical outcomes are always contingent. Only to the extent that mechanisms are intransient can they serve as explanations across cases and time.

Bhaskar (2008) notes that the openness of social systems has both extrinsic and intrinsic sources. Extrinsic closure refers to the standard notion of controlling for extraneous variables. Intrinsic closure inhibits individuals from altering their behavioral responses to circumstances. Recognizing system openness, critical realists (Archer, 1998; Sayer, 1992) generally have dismissed conclusive falsification as unattainable within social science research.

由於存在開放(控制)性的問題(能否控制各項外部變數與當事人對於外部情境反應的一致性，因此社會科學不太可能得到結論性的否証)

Rather than focusing solely on empirical outcomes, critical realists seek explanations for contingent relations, understood in terms of causal mechanisms. As such, critical realism rejects Hume’s (2000 [1739]) conception of causality in terms of universal empirical regularities. A science concerned with potentialities rather than actualities, proposes and examines real causal mechanisms. Hence, critical realists seek to test explanations, not just correlations between observable antecedents and consequences.

要測試的是背後的解釋機制，而非各項因素(現象)出現的關連性
就社會科學而言，理論的對於現象的解釋比預測來的重要，預測要精準，必須在許多條件不變的情況下，但由於存在開放(控制)性的問題，所以一個理論能否解釋現象比能否準確預測來得更重要

 

Strata within reality

真實的層次

Mechanisms should not be reduced to their components if doing so strips them of essential properties. In adopting a stratified view of nature, Bhaskar (1998) rejects reductionist approaches to social science such as methodological individualist accounts of social phenomena. Critical realists reject conflation of levels and reductionism.

不是像拆解機器看個別零件，而是看零件組合在一起的運作機制

 

 

CRITICAL REALIST METHODS

批判實存論者的實證方法

Testing mechanisms

用統計迴歸來進行實證的問題

In particular, they neglect the requirement to attend directly to testing causal mechanisms as part of the process of verifying or falsifying a theory.

所謂的底層機制，不見得可以被直接觀察與測量=>只能依賴邏輯(歸納、推論)來建構

 

Step 1.

The initial step involves identifying the causal mechanisms believed to account for the hypothesized relations in a study. This step requires interpretive work at the interface of theory and the empirical context of interest. Researchers must resolve ambiguities in their theories and derive their implications for particular settings. The goal is a contextualized specification of the explanatory properties and processes that underlie hypothesized causal relations. Because a theory may propose different mechanisms to explain different phenomena, researchers need to select those mechanisms that they believe operate in their particular research setting. More than one mechanism may be relevant to a given causal relation; furthermore, mechanisms may have complementary or conflicting implications for a hypothesized relation.

 

Step 2.

The next step in testing this theory is to consider whether the proposed mechanisms are indeed present in the empirical setting. Step 2 of Figure 1 conveys the emphasis on substantiating the existence of the causal mechanisms. Failure to validate the presence or the nature of postulated mechanisms provides compelling evidence to reject a theory’s arguments.
In this case, multiple observable indicators provide indirect support for the presence of an unobserved mechanism. The reasoning here parallels the use of multiple indicators for latent constructs in structural equation modeling but, in this case, the construct of interest is a causal mechanism.

 

Step 3.

If the available evidence affirms the presence of the theorized mechanisms, we move to testing their causal effects. Prior to attempting to verify or falsify an entire theoretical system in an open context, we advocate testing binary or more complex subsets of relations under controlled circumstances.
For the hypothetical example shown in Figure 1, we seek tests of the component x1-y and x2-y relations in contexts that isolate each relation from other effects.
要做到這點的研究手法有laboratory experiments, behavioral simulation, Quasi-experiments,

 

Step 4.

If empirical data corroborate a theory’s mechanisms and their effects, then the next step to take in evaluating the theory is to examine the implications of its mechanisms jointly. This step moves the analysis from isolated mechanisms to the entire theoretical system, thereby adding complexity to the evaluative procedure. At this stage, we are interested in whether all of the theory’s mechanisms are necessary and whether they are jointly sufficient to explain the outcome.
The openness of social systems complicates testing jointly a theory’s hypotheses. Although the theoretical system is closed (see step 1), the empirical contexts of organizations and industries are not. Step 4 of Figure 1 depicts the open theoretical system in which outside influences give rise to unexplained variance that is relegated to the error term, ε. Here, the error term results from omitting variables relevant to the empirical context but outside the scope of the theory itself. Failure to account for some of the relevant mechanisms diminishes the proportion of variance explained by a model and potentially biases the estimated effects of the theoretical variables.
要做到這點的手法有computer simulation modeling,
如果因素間的反應不是線性，則不適用多變量回歸的方法

Intensive designs. Intensive designs complement extensive designs by addressing the differences across cases that one would expect if empirical outcomes result from conjunctions of multiple mechanisms in open systems. The purpose behind intensive designs is to identify and describe the generative mechanisms operating in particular cases, which is often not feasible for extensive designs.

An intensive design emphasizes the collection of detailed data within one or more cases. The data are often qualitative, as are the analytic methods.