This article presents a systematic approach for reconceptualizing the system usage construct in particular nomological contexts. The approach comprises two stages: definition and selection.

Two concerns that need to reconceptualize system usage: no theory and poor-to-no validation

A staged approach for reconceptualizing system usage:

A two-step method for selecting measures of usage in future research: system usage can be attributed with a precise definition, but the definition refers to a broad range of content, only a subset of which will be relevant in a specific study.

Define stage:

Define the distinguishing characteristics of system usage and state assumptions regarding these characteristics.

==> Selection stage:

Choose the best measures for the part of the usage activity that is of interest.                           

Step 1: Structure. Select the elements of usage that are most relevant for the research model and context.

Step 2: Function. Select measures for the chosen elements that tie to the other constructs in the nomological network.

The staged approach advanced in this paper helps to clarify the meaning of system usage and the range and dimensionality of past usage measures.

 

Research Question

How could we reconceptualize the system usage construct to provide a strong theoretical basis?

Research Objectives

This paper presents a systematic approach for reconceptualizing the system usage construct in particular contexts. The approach consists of two stages, definition and selection, allow researchers to solve issues of the lack of theory and lack of validation in system usage studies.

Why We Need to Reconceptualize System Usage?

Two concerns: 1) No theory. Few studies discuss how theory informs their choice of usage measures. In absence of a theoretical basis, an abundance of measures could be problematical. 2) No validation. Almost no validation of the usage construct has been conducted. After all, past studies have provided mixed conclusions about the link between system usage and individual performance.

A Staged Approach for Reconceptualizing System Usage

1. Definition Stage: Define the distinguishing characteristics of system usage and state assumptions regarding these characteristics.
2. Selection Stage: Choose the best measures for the part of the usage activity that is of interest.

• Step 1: Structure. Select the elements of usage that are most relevant for the research model and context.
• Step 2: Function. Select measures for the chosen elements that tie to the other constructs in the nomological network.

Empirical Investigations of the Approach

Using the two-stage approach, authors develop a model of system usage and individual task performance. The result from the approach is a higher-order construct, exploitive system usage, which is expected to be more beneficial than other less strong constructs such as cognitive absorption and deep structure usage.

Empirical Test of the Approach

To compare performances of measures, authors conduct a lab experiment to university students. The results show that a very rich measure of usage (exploitive system usage) yields almost three times the variance explained by the lean measure (duration) and consistent positive effects on performance.

Contributions

This paper provides a way to explicitly conceptualize and reconceptualize system usage in IS research.

Previous research about system usage on different level

• Different levels:
  • Individual: behavior, cognition and affect
  • Group: an aggregation of individual level behavior, a pattern of individual behaviors, cognitions and affect
  • Organization: an aggregation of individual level behavior, an intra- (and/or inter-) organizational behavior
• Definition of usage: user (subject) + system (IS) + task (performed)

Motivations for multilevel research

• Avoid significant fallacies (cross-level, contextual, ecological and atomistic fallacy) that can occur in single-level studies
• Open up new opportunities for theory and may even help generate new organization-specific theories

Nature of multilevel constructs: application of system usage

• Function of usage:
  • Individual level: use of an IS influences user performance
  • Collective level: users actively reflect on the results of their usage and adjust their actions over time
• Structure of usage:
  • Interaction (interdependencies-in-use)
    • Collective usage exist: strength of interdependencies-in-use are moderate or strong no matter the proportion of members interacting with the IS
    • Guidance: be mediated in several ways; be sure to measure them; whether and why they exist in a given scenario.
    • Three types: collaboration, communication and coordination
  • Development process (form of collective use)
    • Global: level of origin == level of theory
    • Shared: (level of origin < level of theory) & form of homogeneous pattern
    • Configural: (level of origin < level of theory) & form of distinct pattern
  • Context (context of usage)
    • Context-function: contextual factor associated with each element of usage; given structure of collective usage associated with different outcomes due to differences in time-scale
    • Context-structure: nature of each element of usage can facilitate construction of interaction; time is an important contextual factor with collective properties

This article concerns theories about why and how information technology affects organizational life, and examines theories in terms of their structures. Three dimensions of casual structure are considered:

Causal Agent

The analyst’s beliefs about the identity of the causal agent, the nature of causal action and the direction of causal influence among the elements in a theory.

Three conceptions of causal agency in the literature on information technology and organizational change:

• The technological imperative:

It views technology as an exogenous force which determines or strongly constrains the behavior of individuals and organizations.

• The organizational imperative:

It assumes almost unlimited choice over technological options and almost unlimited control over the consequences.

• The emergent perspective:

The uses and consequences of information technology emerge unpredictably from complex social interactions.

Analysts of different persuasions can often be distinguished as easily by their normative stance as by the specifics of their models.

Logical Structure

The temporal aspect of theory – static versus dynamic – and to the logical relationships between the “causes” and the outcomes.

	Variance Theory	Process Theory
Role of Time	Static	Longitudinal
Definition	The cause is necessary and sufficient for the outcome	Causation consists of necessary conditions in sequence; change and random events play a role
Assumption	The outcome will invariably occur when necessary and sufficient conditions are present	Outcomes may not occur (even when conditions are present)
Elements	Variables	Discrete outcomes
Logical Form	If X, then Y; If more X, then more Y	If not X, then not Y; cannot be extended to “more X” or “more Y”

Level of Analysis

The entities about which the theory poses concepts and relationships

• Macro-level theory: a productive strategy for generating falsifiable, parsimonious, and readily comprehensible explanations for behavior
• Micro-level theory: only people can act
• Mixed-level theory: technologies are neither strictly micro nor macro in character

 

About Theory

• The author discusses theory in terms of the nature of theory in general, generalization, causality, and goals (explanation and prediction).
• Theory, in this study, is “abstract entities that aim to describe, explain, and enhance understanding of the world and, in some cases, to provide predictions of what will happen in the future and to give a basis for intervention and action.”
• Generalization
  • Theories can be classified such as Meta-theory, grand theories, substantive theory, formal theory, and mid-range theory based on their level of generalization and breadth of focus.
• Causality
  • Four prominent approaches to the analysis of causality a) regularity (nomological) analysis (arguably most popular in natural science), b) counterfactual analysis (closely related to regularity analysis), c) probabilistic causal analysis (suited to social science), and d) manipulation or teleological causal analysis
• Explanation and prediction
  • Explanation can refer to two cases: a) a particular phenomenon can be explained through the scientific law; and b) explanation depends not on universal law, but on other causal mechanisms such as teleological causes. This study addresses the second type of explanation.
  • Other than explanation, theories may aim at prediction.

Classifying theory in IS

• A taxonomy of IS theory types includes a) analysis, b) explanation, c) prediction, d) explanation and prediction, and; e) design and action.
• Structural component of theory are a) means of representation, b) constructs, c) statements of relationship, d) scope, e) causal explanations, f) testable propositions, and g) prescriptive statements.

Five types of theory in IS

• Type I: Theory for analyzing
  • This kind of theory suggests “what is” contrary to causality or prediction.
  • It is needed when we have no or very limited understanding of the phenomena in question.
  • This type of theory includes classification, schema, frameworks, or taxonomies.
• Type II: Theory for explaining
  • The theory says what is, how, why, when, and where, but without making testable predictions.
  • Type II theory is identified at high- and low-level:: a) one as enlightenment at a high level and b) one for explaining some particular real-world situation.
  • This kind of theory should be new and interesting and explain previously poorly understood phenomena with plausibility and credibility.
• Type III: THeory for predicting
  • The theory says what is and what will be, but without a why part remaining a ‘black box’.
  • This is because a) prediction is of particular interest to theories and b) reasons to justify the empirical results have not yet been discovered, and c) a certain theoretical view (e.g., logical positivist) leans toward not including causality.
• Type IV: Theory for explaining and predicting (EP Theory)
  • The theory says what is, how, why, when, where, and what will be.
  • Example: Technology Acceptance Model (TAM) which aims to both explain and predict.
• Type V: Theory for design and action
  • The theory says how to do something with explicit prescriptions.
  • Example: Design science in IS
  • Design theory includes methodologies and prescriptions for developing specific applications.
  • The criteria for evaluating contribution of Type V research include utility to users, the novelty of an artifact, and persuasiveness.
• Interrelationships among theory types
  • Type I theory is the basis for all other theories by providing phenomena in question.
  • Type V theory, design science, can be informed by all other theories.
  • Particularly, type IV and V are closely related to each other in that knowledge informs the design of IT artifacts and such artifacts can be explained through EP theory.

Application of the taxonomy

• The authors applied the theory types to issues of MISQ and ISR.
• The results show that type IV is the most frequent and type III is the least.

Questions for discussion

• One theory type does not represent a particular paradigm. They are not clearly connected.
• Each theory type has its own values and contributions. We cannot rank them.
• There is controversy over whether each theory type can be called theory. The authors argue that labeling various forms of theory as a theory has a number of advantages such as complementarity among theory types and motivating researchers to think clearly about theory.
• One type of theory (e.g., Type I) can precede the other.
• The taxonomy proposed might be different in other fields because of IT artifacts which those fields lack.

Outline

IS discipline seems to be making little progress in the sense of building a cumulative tradition and taking on the appearance of science.  There may be several reasons 1. a preoccupation with research frameworks rather than theory; 2. a continued focus on new technology ;3. the lure of applied design and construction efforts rather than a quest for pure ideas. 

Progress in the IS filed

• A lack of paradigm is recognized as the main reason for the slow development of IS filed. 
• Examines the nature of scientific progress – According to Kuhn, science only progresses when it has a paradigm because a body of shared beliefs cannot exist without a paradigm. The theory underlies the notion that progress in the field can be assessed via the pattern of literature citations in the filed. (Based on these two measures, there is stagnation in the IS filed)

Factors Inhibiting Progress

• There are few research framework paradigmatically based.
• Always focus on new technology.  Many IS researches are driven by the events of the day. The end result is a field devoid of theory and general principles.
• IS articles tend to describe how to design and build some type of computer system, but these researches make no significant contribution to progress in the IS discipline in terms of lacking a theoretical foundation.

The Need for A Paradigm

• There are two approaches to pursue paradigms 1. develop a paradigmin or to borrow a paradigm from another discipline and to articulate this paradigm in their own discipline; 2.researchers can pursue the development and articulation of a paradigm based in their own discipline.
• The notion of paradigm “ownership” must first be established. A particular group of scientists who claim membership in a particular discipline has in some way made a substantive contribution to knowledge.
• For researchers, a major concern would be the need for discipline to transcend the boundaries of an applied discipline and to enter the realm of a pure discipline, which is motivated by the relative stability of pure disciplines versus applied disciplines

The Pursuit of A Discrete Artifacts Paradigm

• The three fundamental conditions should hold when searching for a paradigm:
  • The members of the discipline must perceive some set of objects where they have a comparative advantage;
  • They must perceive that the set of objects exhibit behavior about which they can theorize;
  • Order must underlie the behavior of the objects that members of the discipline choose to investigate.
• The value of studying the phenomena of interest from three perspectives:
  • Statics – the structure of the phenomena of interest in equilibrium; 
  • Comparative statics – the structure of the phenomena of interest in their new equilibrium position;
  • Dynamics – the characteristics of the time series of changes as the phenomena move between equilibrium positions

 

Research Question: How IS researchers have dealt with information technology? What implications and research directions can be suggested?

Research Objective: The objective of this commentary is to present evidence that IS research has not seriously considered its core subject matter: IT artifact.

Research Methods: A full set of 188 articles published in Information Systems Research are reviewed.

Research Findings: 14 conceptualizations of the IT are clustered into following five broad metacategories.

• From the tool view of technology, IT is the engineered artifact, computing resource. It is represented in four ways: as a tool for labor substitution, a tool for enhancing productivity, a tool for information processing, and a tool for changing social relations. These conceptualizations consider such technology as the independent variable.
• From the proxy view of technology, IT can be captured through three surrogate measures: individual perceptions, diffusion rates, and dollars spent. The first proxy logic concentrates on users’ perceptions of the technology. The second logic focuses on the diffusion of IT within firms, industries, and economies. The third logic uses monetary measures as an indicator of the value of IT.
• From the ensemble view of technology, IT is the dynamic interactions between people and technology. The first conceptualization is considering IT as development project, social processes of developing technical artifact. The second one is considering IT as production network, focusing on the supply sides at the levels of industry and nation-state. The third one is considering IT as embedded system, concentrating on its use in a complex social context. The fourth one is considering IT as structure, the technology embodying social structures.
• From the computational view of technology, IT is the computational power to deal with information. Here, IS research regards IT as algorithm, enhancing existing systems or supporting human activity. In addition, such research regards IT as model which represents social phenomena.
• From the nominal view of technology, IT is either incidental or used as background information. Here, there is no reference to any technology.

Implications from the review: Many articles are relying on received notions of technology. Here, the implication is that we need to move toward broader and deeper interdisciplinary conceptualizations of IT artifacts.

Research directions: We need to re-conceptualize the IT artifact. However, given the complexity of its contexts, IT artifact cannot be one-size-fits-all conceptualization. More attempts are required to all members of IS community.

This article concerns theories about why and how information technology affects organizational life by examining theories in terms of their causal structures. Three dimensions of causal structure are considered: causal agency, logical structure, and level of analysis.

Causal agency

The beliefs about the nature of causality: whether external forces cause change, whether people act purposefully to accomplish intended objectives, or whether changes emerge unpredictably from the interaction of people and events.

Three conceptions of causal agency in the literature on information technology and organizational change.

• Technological imperative

The information technology constrains or determines human and organizational behavior.

• Organizational imperative

Human actors design information systems to satisfy organizational needs for information.

• The emergent perspective

The uses and consequences of information technology emerge unpredictably from complex social interactions. Central concepts in the emergent perspective are the role of the computing infrastructure, the interplay of conflicting objectives and preferences, and the operation o nonrational objectives and choice processes.

Logical structure

The temporal aspect of theory – static versus dynamic – and to the logical relationships between the “causes” and the outcomes.

• Variance and Process Theories

	Variance theory	Process theory
Role of time	Static	Longitudinal
Definition	The cause is necessary and sufficient for the outcome	Causation consists of necessary conditions in sequence; change and random events play a role
Assumption	The outcome will invariably occur when necessary and sufficient conditions are present	Outcomes may not occur (even when conditions are present)
Elements	Variables	Discrete outcomes
Logical Form	If X, then Y; If more X, then more Y	If not X, then not Y; cannot be extended to “more X” or “more Y”

• Advantages of process theories
  • Captures the variant relationship between antecedents and outcomes
  • Prediction of patterned regularities over time
  • The predictions may correspond more faithfully to actual events in organizations than do the typical predictions of variance formulations

Level of analysis

The entities about which the theory poses concepts and relationships – individuals, groups, organizations, and society. The customary division of levels of analysis into “macro-level” and “micro-level” theories reflect disciplinary boundaries, each with its favored research questions, acceptable methodologies, and conventions for reporting results.

• Macro-level theories
  • The concepts are properties of large-scale collectives (organizations, populations, societies)
  • The level of analysis is favored by macro-sociologists, macroeconomists, and evolutionary theorists
• Micro-level theories
  • The concepts are properties of individuals and small groups
  • The level of analysis is favored by social psychologists and microeconomist
• Mixing levels of analysis
  • Preserves macro-level concepts while it grounds these concepts in individual purposes and behavior and so remains “methodologically individualist”

This editor’s comment mainly contends that “no native theories” assertion doesn’t hold because:

• the assertion seldom provides qualifications or evidences
• native theories are created regularly throughout the history of the profession (the main focus of this paper)

Clarification of native theory scope discussed in this comment

• Concept of theory:
  • the fact of theory creation (theory testing, theory influence, or anything else are excluded)
• Distinguish between native theory and imported theory:
  • Native theory: theory specifically developed to describe, explain, predict, or design IS phenomena
  • Imported theory: theory borrowed from external (reference) discipline to describe, explain, predict or design IS phenomena (for example: social network analysis)

Proof that MIS values native theory

• ICIS workshop held by JAIS
• “Theory and Review” section in MISQ

Proof of regular development of native theories throughout the history (Sampling of MIS native theories)

• 1966-1974: early years of theory-building:
  • Mathematical theory of communication + theoretical elements à socio-technical theory
• 1970s-1980s: paradigm for the further development of IS theories:
  • Theory of discrete IT artifacts
  • Media richness theory
• Late 1980s
  • TAM (technology acceptance model)
  • UTAUT (unified theory of acceptance and use of technology)
• Early 1990s
  • information success model
  • theory of advanced information technologies
• Modern Era: Pre- and Post-Millennium Native Theories
  • Theory of task closure
  • “the move to the middle” hypothesis
  • adaptive structuration theory
  • task-technology fit theory
  • theory of net-enabled innovation cycles
  • media synchronicity theory
  • virtual space and place theory