The objective of this paper is to provide a basic framework for researchers interested in reporting the results of their PLS analyses. Since the dominant paradigm in reporting Structural Equation Modeling results is covariance based, this paper begins by providing a discussion of key differences and rationale that researchers can use to support their use of PLS. This is followed by two examples from the discipline of Information Systems. The first consists of constructs with reflective indicators (mode A). This is followed up with a model that includes a construct with formative indicators (mode B).

How to Write Up and Report PLS Analyses

Chapter 28 How to Write Up and Report PLS Analyses

While researchers go to great lengths to justify and prove theoretical links between constructs, the relationship between measurement items and constructs is often ignored. By default, the relationship between construct and item is assumed to be reflective, meaning that the measurement items are a reflection of the construct. Many times, though, the nature of the construct is not reflective, but rather formative. Formative constructs occur when the items describe and define the construct rather than vice versa.

In this research, we examine whether formative constructs are indeed being mistaken for reflective constructs by information systems researchers. By examining complete volumes of MIS Quarterly and Information Systems Research over the last 3 years, we discovered that a significant number of articles have indeed misspecified formative constructs. For scientific results to be valid, we argue that researchers must properly specify formative constructs. This paper discusses the implications of different patterns of common misspecifications of formative constructs on both Type I and Type II errors. To avoid these errors, the paper provides a roadmap to researchers to properly specify formative constructs. We also discuss how to address formative constructs within a research model after they are specified.

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Specifying formative constructs in information systems research

In general, perceptions of information systems (IS) success have been investigated within two primary research streams--the user satisfaction literature and the technology acceptance literature. These two approaches have been developed in parallel and have not been reconciled or integrated. This paper develops an integrated research model that distinguishes beliefs and attitudes about the system (i.e., object-based beliefs and attitudes) from beliefs and attitudes about using the system (i.e., behavioral beliefs and attitudes) to build the theoretical logic that links the user satisfaction and technology acceptance literature. The model is then tested using a sample of 465 users from seven different organizations who completed a survey regarding their use of data warehousing software. The proposed model was supported, providing preliminary evidence that the two perspectives can and should be integrated. The integrated model helps build the bridge from design and implementation decisions to system characteristics (a core strength of the user satisfaction literature) to the prediction of usage (a core strength of the technology acceptance literature).

A Theoretical Integration of User Satisfaction and Technology Acceptance

While the technology acceptance model (TAM), introduced in 1986, continues to be the most widely applied theoretical model in the IS field, few previous efforts examined its accomplishments and limitations. This study traces TAM’s history, investigates its findings, and cautiously predicts its future trajectory. One hundred and one articles published by leading IS journals and conferences in the past eighteen years are examined and summarized. An openended survey of thirty-two leading IS researchers assisted in critically examining TAM and specifying future directions.

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The Technology Acceptance Model: Past, Present, and Future

There has been considerable research on the factors that predict whether individuals will accept and voluntarily use information systems The technology acceptance model (TAM) has a base in psychological research, is parsimonious, explains usage behavior quite well, and can be operationalized with valid and reliable instruments A limitation of TAM is that it assumes usage is volitional, that is, there are no barriers that would prevent an individual from using an IS if he or she chose to do so This research extends TAM by adding perceived user resources to the model, with careful attention to placing the construct in TAM's existing nomological structure In contrast to measures of self-efficacy and perceived behavioral control that concentrate on how well individuals perceive they can execute specific courses of action, this paper examines perceptions of adequate resources that can facilitate or inhibit such behaviors The inclusion of both a formative and reflective set of measures provides the opportunity for the researcher and manager to decide whether to evaluate only the overall perceptions of adequate resources or also the specific underlying causes The extended model incorporating these measures was then tested in the field The results confirmed that perceived user resources is a valuable addition to the model

Extending the technology acceptance model: the influence of perceived user resources

Mechanisms That Generate Questions

Presents a description of the implementation of a supplier source integration (SSI) program at a large manufacturing company, and the challenges faced in the pursuit of new processes, methodologies and techniques. Discusses issues pertaining to the management of interfaces with the supplier over the entire product development life cycle. There is particular focus on the choice of the supplier and overcoming problems arising from the in‐house design team. Previous experiences of the company with SSI have given the current design team a number of positive and negative results together with a set of recommendations that can be used in the current implementation of SSI for an exhaust system. These can also provide the basis for determining a future strategy.

Supplier source integration in a large manufacturing company

This paper evaluates our current Accounting Information Systems (AIS) curriculum, developed using a Model-Oriented, Tool-Enhanced (MOTE) framework, by comparing it to the recommendations of the International Federation of Accountants' (IFAC) Guideline No. 11. The IFAC recommendations offer guidance on the information technology content in accounting curricula and are supported by the AICPA. The evaluation resulted in suggestions for improvements to our existing curriculum. An analysis of the guideline was also completed.

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Assessment of an Accounting Information Systems Curriculum: An Analysis of the International Federation of Accountants' International Education Guideline No. 11

Within the cognitive sciences, two taxonomies for classifying questions have been proposed (Graesser, Lang, & Horgan,1988; Lehnert, 1978). Both have neglected to include a category for comparison questions. We provide a definition of comparison questions and show how they related to the semantic categories of the two taxonomies. We present examples of comparison questions posed by auditors who are attempting to learn about a company during the initial stages of an audit. Comparison questions are particularly important to auditors because they uncover discrepencies and they focus the auditors’ search for information. Auditors are concerned with diagnosing the financial health of a company on the basis of symptoms and financial indicators of the company's performance. These critical comparisons involve comparison in performance of a company over time and comparison between a company and industrial averages. The auditor's use of comparison questions is closely linked to the diagnostic process.

Eileen Peacock

Papers

Extending the technology acceptance model: the influence of perceived user resources

Mechanisms That Generate Questions

Supplier source integration in a large manufacturing company

Assessment of an Accounting Information Systems Curriculum: An Analysis of the International Federation of Accountants' International Education Guideline No. 11

An analysis of comparison questions in the context of auditing