Ronald T. Cenfetelli

Interpretation of formative measurement in information systems research

Within the Information Systems literature, there has been an emerging interest in the use of formative measurement in structural equation modeling (SEM). This interest is exemplified by descriptions of the nature of formative measurement (e.g., Chin 1998a), and more recently the proper specification of formatively measured constructs (Petter et al. 2007) as well as application of such constructs (e.g., Barki et al. 2007). Formative measurement is a useful alternative to reflective measurement. However, there has been little guidance on interpreting the results when formative measures are employed. Our goal is to provide guidance relevant to the interpretation of formative measurement results through the examination of the following six issues: multicollinearity; the number of indicators specified for a formatively measured construct; the possible co-occurrence of negative and positive indicator weights; the absolute versus relative contributions made by a formative indicator; nomological network effects; and the possible effects of using partial least squares (PLS) versus covariance-based SEM techniques. We provide prescriptions for researchers to consider when interpreting the results of formative measures as well as an example to illustrate these prescriptions.

Addressing the What and How of Online Services: Positioning Supporting-Services Functionality and Service Quality for Business-to-Consumer Success

With the continued growth of business-to-consumer (B2C) e-business, online vendors are providing an increasing array of services that support and enhance their core products or services. For example, Amazon.com does not just sell books; it also enhances that core product with automated product recommendations, “wish list” tracking, order status updates, customer reviews, and many other valuable supporting services. These supporting services are made possible exclusively through the design and deployment of information technology (IT) to provide website supporting services functionality (SSF). In this paper, we define and develop the concept of B2C SSF and investigate how IT can support core products or services. We theorize the role that SSF plays in an environment where individuals who visit B2C websites are not only customers but also technology users. Given the unique online environment that amalgamates vendor services with information systems (IS), our model integrates theories from both services marketing and technology acceptance to help explain the behavior of these customers/users. In doing so, we investigate the role of the extensively researched concept of service quality in relation to SSF. Although service quality provides guidance for how supporting services should be provided (e.g., responsively and reliably), it does not address what those services are (e.g., product recommendations). SSF addresses this deficiency, thus providing both theoretical and practical benefits through a focus on IT design and deployment. The results of a field study support that SSF is an important predictor of customer beliefs and behavior, beyond that predicted by service quality alone. SSF is an important concept to consider---theoretically and practically---in IT-mediated B2C service.

Integrating service quality with system and information quality: an empirical test in the e-service context

Wixom and Todd (2005) integrated the user satisfaction and the technology acceptance literatures to theorize about and account for the influence of the information technology artifact on usage. Based on Wixom and Todds integrated model of technology usage, we propose the 3Q model by investigating the role of service quality (SQ), in addition to system quality (SysQ) and information quality (IQ), in website adoption. Attention to SQ is critical, as consumer websites have increasingly become the target of SQ assessment made by consumers, not just traditional SysQ and IQ evaluations. As part of our study, we further theorize and empirically test the relationships among these three types of quality constructs and hypothesize that perceived SysQ influences perceived IQ and perceived SQ, and perceived IQ influences perceived SQ. Our study extends the Wixom and Todd model in the e-service context and is the first of its kind to empirically examine the combined impact of perceived SQ, perceived SysQ, and perceived IQ on usage intention. Our study advances the theoretical understanding of SQ and the relationships among perceptions of SysQ, IQ, and SQ in the e-service context. The results also inform practitioners that high IQ and SysQ can directly or indirectly improve SQ in the e-service context.

Identifying and Testing the Inhibitors of Technology Usage Intentions

An important area of information systems (IS) research has been the identification of the individual-level beliefs that enable technology acceptance such as the usefulness, reliability, and flexibility of a system. This study posits the existence of additional beliefs that inhibit usage intentions and thus foster technology rejection rather than acceptance. We theorize that these inhibitors are more than just the antipoles of enablers (e.g., the opposite of usefulness or reliability) and so are distinct constructs worthy of their own investigation. Inhibitors are proposed to have effects on usage intentions beyond that of enablers as well as effects on enablers themselves. We report on a series of empirical studies designed to test the existence and effects of inhibitors. A candidate set of six inhibitors is shown to be distinct from enablers. These inhibitors are subsequently tested in a field study of 387 individuals nested within 32 different websites. Effects at both individual and website unit levels of analysis are tested using multilevel modeling. We find that inhibitors have negative effects on usage intentions, as well as on enablers, and these effects vary contingent upon individual or website unit levels of analysis. The overall results support the existence and importance of inhibitors in explaining individual intent to use---or not use---technology.

IT-mediated customer service content and delivery in electronic governments: an empirical investigation of the antecedents of service quality

Despite extensive deliberations in contemporary literature, the design of citizen-centric e-government websites remains an unresolved theoretical and pragmatic conundrum. Operationalizing e-government service quality to investigate and improve the design of e-government websites has been a much sought-after objective. Yet, there is a lack of actionable guidance on how to develop e-government websites that exhibit high levels of service quality. Drawing from marketing literature, we undertake a goal approach to this problem by delineating e-government service quality into aspects of IT-mediated service content and service delivery. Whereas service content describes the functions available on an e-government website that assist citizens in completing their transactional goals, service delivery defines the manner by which these functions are made accessible via the web interface as a delivery channel. We construct and empirically test a research model that depicts a comprehensive collection of web-enabled service content functions and delivery dimensions desirable by citizens. Empirical findings from an online survey of 647 respondents attest to the value of distinguishing between service content functions and delivery dimensions in designing e-government websites. Both service content and delivery are found to be significant contributors to achieving e-government service quality. These IT-mediated service content functions and delivery dimensions represent core areas of e-government website design where the application of technology makes a difference, especially when considered in tandem with the type of transactional activity. A split sample analysis of the data further demonstrates our models robustness when applied to e-government transactions of varying frequency.

Research Note---The Influences of Online Service Technologies and Task Complexity on Efficiency and Personalization

Online retailers are increasingly providing service technologies, such as technology-based and human-based services, to assist customers with their shopping. Despite the prevalence of these service technologies and the scholarly recognition of their importance, surprisingly little empirical research has examined the fundamental differences among them. Consequently, little is known about the factors that may favor the use of one type of service technology over another. In this paper, we propose the Model of Online Service Technologies MOST to theorize that the capacity of a service provider to accommodate the variability of customer inputs into the service process is the key difference among various types of service technologies. We posit two types of input variability: Service Provider-Elicited Variability SPEV, where variability is determined in advance by the service provider; and User-Initiated Variability UIV, where customers determine variability in the service process. We also theorize about the role of task complexity in changing the effectiveness of service technologies. We then empirically investigate the impact of service technologies that possess different capacities to accommodate input variability on efficiency and personalization, the two competing goals of service adoption. Our empirical approach attempts to capture both the perspective of the vendor who may deploy such technologies, as well as the perspective of customers who might choose among service technology alternatives. Our findings reveal that SPEV technologies i.e., technologies that can accommodate SPEV are more efficient, but less personalized, than SPEUIV technologies i.e., technologies that can accommodate both SPEV and UIV. However, when task complexity is high vs. low, the superior efficiency of SPEV technologies is less prominent, while both SPEV and SPEUIV technologies have higher personalization. We also find that when given a choice, a majority of customers tend to choose to use both types of technologies. The results of this study further our understanding of the differences in efficiency and personalization experienced by customers when using various types of online service technologies. The results also inform practitioners when and how to implement these technologies in the online shopping environment to improve efficiency and personalization for customers.

Do different kinds of trust matter? An examination of the three trusting beliefs on satisfaction and purchase behavior in the buyer-seller context

The three trusting beliefs have different effects on satisfaction and purchase.Benevolence belief is a stronger predictor of satisfaction than competence belief.Competence is a stronger predictor of purchase than integrity and benevolence.Future trust research should include both satisfaction and purchase behavior. Trust has been extensively studied in the buyer-seller context and typically operationalized according to the McKnight tripartite conception of trusting beliefs. The McKnight model identifies three beliefs (integrity, benevolence, and competence) as the key components of trust. However, limited research has examined the relative effect of these three individual trusting beliefs on satisfaction and purchase behavior in the buyer-seller context. To address this gap, we posit that a buyers beliefs in a sellers integrity and benevolence have stronger influences on satisfaction than a belief in a sellers competence. In contrast, a buyers belief in a sellers competence has a stronger influence on purchase behavior as compared to beliefs in a sellers integrity and benevolence. The results from a buyer-broker simulation study support that (1) a buyers belief in a sellers benevolence is a stronger predictor of satisfaction than the belief in a sellers competence; (2) a buyers belief in a sellers competence is a stronger predictor of purchase behavior than are beliefs in a sellers integrity and benevolence. This research enhances our theoretical understanding about which dimensions of trust play more important roles in influencing satisfaction and purchase behavior, respectively. This research also provides guidance to practitioners enabling them to focus on the development and training foci that best prepare customer relationship employees on the diverse aspects of trust most salient to customer needs, such as, emphasizing competence over benevolence for infrequent purchases, or emphasizing benevolence for potentially frequent purchases.

The outcomes and the mediating role of the functional triad: The users' perspective

B.J. Foggs Functional Triad shows the manner in which computing technologies can persuade people by playing 3 different functional roles, namely, as tools, media, or social actors. However, the effects of user perceptions of these 3 functional roles are largely unknown. We advance Foggs framework by developing a conceptual model to explain how a feature of a computing technology (ie, the trade‐off transparency feature of a recommendation agent [RA], which interactively demonstrates the trade‐offs among product attribute values) can result in certain outcomes by shaping the beliefs of individuals regarding the 3 functional roles. We examine the effects of the perceived Functional Triad on the following 3 outcomes: (1) persuading users to use an RA (intention to use), (2) persuading users to follow the advice of the RA (recommendation adherence), and (3) persuading users to recommend the RA to others (recommendation to friends). We conducted a laboratory experiment to manipulate 4 levels of trade‐off transparency, thereby creating an adequate amount of variations for the perceived Functional Triad. A total of 160 participants were recruited from a large university in North America. Although designers could control the technology design aspects, these designs may not accomplish the intended effects on users, who have their own perceptions. This study contributes to existing literature by simultaneously evaluating the 3 different outcomes of the Functional Triad from the perspective of users.

Understanding e-service failures : formation, impact and recovery

Despite the ‘dangers’ posed by e-service failures, there has not been a study to-date that explores how failures emerge within an online transactional environment and what can be done to address them. An integrated model of e-service failure and recovery is constructed together with testable propositions. Essentially, the model serve to inform both academics and practitioners on: (1) how different types of e-service failure manifest on ecommerce websites; (2) the impact of these failures on consumers’ expectations about transactional outcome, process and cost, and; (3) what kind of e-service recovery technology would be beneficial in alleviating negative failure consequences.

A Two-Stage Model of Generating Product Advice: Proposing and Testing the Complementarity Principle

Abstract Most extant research into product recommendations focuses on how advice from recommendation agents (RAs), consumers, or experts facilitates an initial (or single-stage) screening of available products and provides relevant product recommendations. The literature has largely overlooked the possibility and effects of the second stage of product advice using a recommendation improvement (RI) functionality, during which users can refine and improve the accuracy of the first-stage product recommendations. Thus, our understanding of how users make product choices is incomplete. To rectify this, we propose a two-stage model of generating product advice, and we use it to test what we propose as the complementarity principle. This principle posits that the first-stage recommendations (personalized or nonpersonalized) influence the impact of different types of second-stage RI functionality, which augment the first stage by facilitating either alternative-based or attribute-based processing. Results show that the complementary synergies between the two stages result in higher perceived decision quality, but at the expense of higher perceived decision effort. We contribute to the literature by helping researchers better understand users’ adoption of the second-stage RI functionality in conjunction with first-stage recommendations. In addition, e-commerce designers are advised to provide different and complementary types of recommendation sources and RI functionalities to facilitate online consumers’ decision making.