Training experiences and usage intentions: a field study of a graphical user interface

Int . J . Human – Computer Studies (1996) 45 , 215 – 241

Training experiences and usage intentions : a field study of a graphical user interface

R ITU A GARWAL , J AYESH P RASAD AND M ICHAEL C . Z ANINO

Uni y ersity of Dayton , School of Business Administration , Dayton , OH 4 5 4 6 9 - 2 1 3 0 , USA

( Recei y ed 1 6 May 1 9 9 5 and accepted in re y ised form 1 1 March 1 9 9 6 )

User perceptions about the attributes of an information system have been found to be good predictors of system utilization intentions . This paper explores the ef fects of an important intervention , user training , on the development of user perceptions about a target system . The theoretical model underlying the study postulates that two other classes of variables—situational and individual—moderate the relationship between training and user perceptions . Predicted usage behavior , measured through intended use of the system in the future , is , in turn , predicated upon perceptions of the system . We present the results of a field study of 230 users conducted to examine the impacts of training on the development of user perceptions about a graphical user interface , Microsoft Windows , and the relationship between user perceptions and system use . Two dif ferent types of training experiences , formal training and self training , were investigated . Results show that user perceptions are reasonable predictors of usage intentions , and that training experiences moderated by several individual variables play an important role in the development of user perceptions . Recommendations for the design of user training programs as well as for future research are of fered . ÷ 1996 Academic Press Limited

1 . Introduction

In spite of investments in excess of billions of dollars in information technology , several studies have concluded that overall impact on productivity has been low . This phenomenon , termed the Productivity Paradox (Roach , 1992) , suggests that the tools of information technology have not delivered their anticipated benefits to the nation as a whole . According to research conducted by the Gartner Group , ‘‘white-collar productivity in 1987 was exactly at the same level it was in 1967 . This is after all of the huge investments in personal computers and integrated of fice systems . ’’ (Sivula , 1990) . Roach (1992) and other economists have also shown that prior to the introduction of the personal computer , white collar productivity was growing annually at a rate of 3 . 3% . By 1990 , after over one-trillion dollars had been spent on technology based productivity tools , this figure had dropped to 1% .

The perplexing relationship between investment in information technology and gains in productivity has raised concern among the academic and practitioner communities alike . A widely postulated explanation of this relationship is the aphorism that systems that are not used provide little value . Thus , having the technology available is simply not enough ; it must be used appropriately by its target user group in order to realize anticipated productivity gains . Consequently , there is a growing body of academic research focused on examining the determinants of

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R . AGARWAL ET AL . 216

computer technology acceptance and utilization among users (e . g . Davis , 1989 ; Davis , Bagozzi & Warshaw , 1989 , Mathieson , 1991 ; Moore & Benbasat , 1991) . A significant proportion of this research draws its theoretical underpinnings from the adoption and dif fusion of innovations literature , where individuals’ perceptions about using an innovation are said to influence usage (Rogers , 1983 ; Moore & Benbasat , 1991) . Other theoretical models that attempt to explain the relationship between user attitudes , perceptions , beliefs , and eventual system use include the theory of reasoned action , the theory of planned behavior , and the technology acceptance model . Recent work has been focused on empirically testing these models to determine their relative explanatory power (Davis et al . , 1989 ; Mathieson , 1991) .

Although there is now a substantial body of evidence suggesting that user perceptions are indeed important determinants of system use , there is considerably less work done in the area of examining what influences user perceptions in the first place , or more importantly , how user perceptions can be proactively influenced so as to encourage system use . User behavior has been said to be predicated upon user perceptions of the attributes of the target technology (Moore & Benbasat , 1991) , as opposed to the intrinsic characteristics of the technology . There are , however , several external considerations that are determinants of user perceptions ; this study focuses on exploring the impact of one important external intervention—user training—on perceptions of using an information technology innovation .

Training has been identified as a critical factor that can af fect the success or failure of an end-user computing tool in an organization (Cheney , Mann & Amoroso , 1986) . A study by Zmud and Lind (1985) showed that training sessions for end-users were one of the most ef fective mechanisms to ensure success of an end-user computing tool . Despite the importance of training in the successful adoption of information technology innovations , little research has been conducted in this area (Davis & Bostrom , 1993) . One reason hypothesized by researchers for the neglect of end-user training is vendor claims that their software is easy to learn and use . Many employers project this to mean that the software requires little or no formal training (Sein , Bostrom & Olfman 1987) . In times of economic hardship and downsizing , one of the first items often to be cut out of the information systems budget is training . Nelson and Cheney (1987) , in their study of training activities in 20 companies found that ‘‘education / training seems to be taken for granted’’ (p . 556) by management . On the other hand , users overwhelmingly felt that training was crucial to successful system utilization . Although technology vendors constantly strive to provide software that is increasingly user-friendly , the need for training will never disappear completely , especially as vendors are pressured to add more functionality to their software (Graggs & Flynn , 1991) .

Assessing the impacts of training on the development of user perceptions should have both theoretical and practical relevance . Persistent problems with training end-users are evidently associated with limited knowledge about ef fective training (Davis & Bostrom , 1993) . From a theoretical perspective , an understanding of how training influences usage of technology would be invaluable since little is known about the impact of training on various user perceptions that may motivate usage . From a practical perspective , it should convince organizations of the worth of investing in training and education activities for users . Additionally , insights into

STUDY OF GRAPHICAL USER INTERFACE 217

the relative ef fects of various training experiences should help guide the allocation of training resources .

This paper presents the results of a field study of 230 users conducted to examine the impacts of training on the development of user perceptions about a specific technology—Microsoft Corporation’s graphical user interface , Windows—and the relationship between user perceptions and system use . Windows has generated considerable interest in industry in recent times . Its benefits have been highly advertised in not just the information systems journals but in the popular business press , general press and television media . Although technology vendors claim that graphical user interfaces such as Windows are intuitively appealing and easy to learn , others state that ease of learning may simply be a myth (Berst , 1991) . For some users graphical user interfaces are hard to learn simply because they are dif ferent than character-based interfaces . If an end-user has no idea of how to use a mouse or a pull-down menu , a graphical user interface can be a user’s worst nightmare (Berst , 1991) . In fact , a recent study suggests that graphical user interface training is a non-trivial task and highlights the need for further research in this area (Olfman & Mandviwalla , 1994) .

The theoretical model underlying the study postulates that two classes of variables—situational and individual—moderate the relationship between training and user perceptions . Predicted usage behavior , measured through intended use of the system in the future , is , in turn , predicated upon perceptions of the system . Unlike prior research on training ef fectiveness which has focused largely on performance related outcomes , our emphasis here is on the development of user perceptions . Although the existence of moderator variables has been recognized for performance outcomes , less attention has been paid to perceptual outcomes . The use of a field study allows us to examine a larger number of potential moderators and identify which ones might be salient for more controlled studies . Hence , a specific contribution of the study is to empirically identify which variables moderate the relationship between type of training and the development of user perceptions . A more general contribution is to extend prior empirical work that has examined the predictive power of user perceptions in explaining system utilization intentions .

The research model

The conceptual model underlying the study reported here is presented in Figure 1 . Each of the major components and linkages in the model is discussed below .

2 . 1 . USER PERCEPTIONS AND USAGE BEHAVIOR

The relationship depicted on the right-hand side of Figure 1 between user perceptions and future usage behavior derives its conceptual underpinnings from three dominant streams in technology acceptance research—the theory of reasoned action , innovation characteristics research , and the technology acceptance model . These streams of research attempt to explain and predict the determinants of individual behavior toward a system , manifest through system utilization . Underly- ing all three streams is a hypothesized theoretical relationship between user


Situationalvariables

Tool-related

Task-related

Trainingexperiences

Formal trainingSelf training

Perceptions Attitude Futureuse

Individual differencevariables

Voluntariness

.

.

F IGURE 1 . The research model .

perceptions about an information technology innovation and future usage behavior (see Figure 1) . In innovation characteristics research , the constructs comprising user perceptions derive their theoretical roots primarily from prior research in the dif fusion of innovations (Rogers , 1983) and more recent work in the development of an instrument to measure the perceived characteristics of using an innovation (Moore & Benbasat , 1991) . Both the theory of reasoned action (Fishbein & Ajzen , 1975 ; Ajzen & Fishbein , 1980) and the technology acceptance model (Davis , 1989 ; Davis et al . , 1989) also prescribe this relationship between user perceptions and usage behavior . The theory of reasoned action is a general theory developed in social psychology that attempts to explain and predict individual behavior across a variety of domains (Ajzen & Fishbein , 1980) whereas the technology acceptance model has been proposed specifically for the domain of information technology (Davis , 1989 ; Davis et al . , 1989) . See Davis et al . (1989) and Mathieson (1991) for a review of both theories .

In general , all three research streams postulate that beliefs about the target system are antecedent to beha y ioral intent to adopt and use the system . In both the theory of reasoned action and the technology acceptance model usage intention is a predictor of usage while in innovation characteristics research this is not clearly defined . Other dif ferences among the bodies of research center around the operationalization of beliefs . Two of the streams—the theory of reasoned action and the technology acceptance model—further suggest that attitude is an af fective response that mediates between beliefs and intentions to use ; where attitude is an outcome of individuals’ beliefs about the characteristics of the system . The instrumentality of perceptions in the development of attitudes , although not explicitly stated by Moore and Benbasat (1991) , has been acknowledged by them in other work (Moore & Benbasat , 1990) . Thus , in the model guiding this study , beliefs result in attitudes which are a determinant of intentions to use and actual usage . Both the theory of reasoned action and innovation characteristics research (but not the technology acceptance model) posit that an additional factor pertaining to social pressure , subjecti y e norm or y oluntariness , also af fects intentions to use . Specific dif ferences in the operationalization of beliefs and social pressure variables in the three research streams are discussed below .


2 . 2 . BELIEFS ABOUT THE TARGET SYSTEM

According to technology acceptance research , beliefs about using the target system influence usage intentions and behavior via their ef fect on a potential adopter’s attitude . The conceptualization of beliefs in recently articulated innovation characteristics research (Moore & Benbasat , 1990 , 1991) is based upon extensive prior research done by Rogers (1983) and others in the dif fusion of innovations . Rogers (1983) , through a synthesis of several previous studies examining adoption behaviors , identified several attributes of an innovation that determine user acceptance . These included relative advantage , compatibility , complexity , observability , and trialability . Building upon and refining the work of Rogers and several others in the area of dif fusion of innovations , Moore and Benbasat (1990 ; 1991) developed an instrument to measure the perceived characteristics of using an innovation .

According to Moore and Benbasat (1990 ; 1991) , seven constructs comprise the primary user beliefs that can help explain information technology (and other innovations) usage . Relati y e ad y antage captures the extent to which a potential adopter views the innovation as of fering an advantage over previous ways of performing the same task . Moore and Benbasat (1991) claim that this construct is similar to the notion of usefulness in the technology acceptance model (Davis et al . , 1989) , where usefulness is defined as the user’s ‘‘subjective probability that using a specific application system will increase his or her job performance within an organizational context’’ (p . 985) . Empirical studies (e . g . Davis et al . , 1989 ; Moore & Benbasat , 1991 ; Adams , Nelson & Todd , 1992 ; Davis , 1993) support the importance of relative advantage or usefulness in predicting adoption behavior .

A second construct , ease of use , recurs in several studies as a significant determinant of adoption behavior (Davis et al . , 1989 ; Adams et al . , 1992) . Ease of use is similar in definition to Rogers’ notion of complexity (Moore & Benbasat , 1991) and encapsulates the degree to which a potential adopter views usage of the target system to be relatively free of ef fort (Davis et al . , 1989) . Systems that are perceived to be easier to use and less complex have a higher likelihood of being accepted and used by potential users . Both relative advantage or usefulness and ease of use are relative concepts and not innate attributes of the system , and can be perceived dif ferently by dif ferent individuals .

In addition to ease of use and usefulness , Moore and Benbasat (1991) identify five other perceived characteristics of innovating and empirically demonstrate their ef fects on adoption behavior . These include compatibility , image , result demonstrability , visibility , and trialability . Moore and Benbasat (1991) use Rogers’ (1983) notion of compatibility being ‘‘the degree to which an innovation is perceived as being consistent with the existing values , needs , and past experiences of potential adopters’’ (p . 195) . The image construct , subsumed by Rogers as part of relative advantage , was shown by Moore and Benbasat to be an independent predictor of usage . Image captures the perception that using an innovation will contribute to enhancing the social status of a potential adopter . The characteristic of observability identified by Rogers was segregated by Moore and Benbasat as consisting of two separate constructs : result demonstrability —‘‘the tangibility of the results of using an innovation’’ (p . 203) , and y isibility —the extent to which potential adopters see the innovation as being visible in the adoption context . Finally , trialability measures the


extent to which potential adopters perceive that they have an opportunity to experiment with the innovation prior to committing to its usage .

In contrast to innovation characteristics research , perceived usefulness and perceived ease of use are the only two beliefs used by the technology acceptance model to explain acceptance behavior . The technology acceptance model’s conceptualization of attitude views it as being an outcome of two primary beliefs : perceived usefulness and perceived ease of use . In addition , one of the beliefs , usefulness , has a direct ef fect on behavioral intentions over and above its ef fect on attitude .

The theory of reasoned action’s conceptualization of beliefs (Ajzen & Fishbein , 1980) suggests that specific beliefs about a particular behavior , as opposed to the general beliefs of the technology acceptance model and innovation characteristics research help explain user behavior . While both the technology acceptance model and innovation characteristics research recommend the use of standard items and scales for the operationalization of beliefs , in the theory of reasoned action , salient beliefs have to be elicited from target users for each specific context to which the theory is applied . Beliefs about consequences of system use are weighted by the value assigned by users to the outcomes of the behavior . For example , a behavioral belief in the context of user adoption of a word-processing program might be ‘‘Using the software improves my productivity’’ , while an example of the associated outcome evaluation is ‘‘It is important for me to be more productive’’ (Davis et al . , 1989) .

According to these research streams , beliefs can be influenced by external factors . For example , in two equally ‘‘easy to use’’ systems , one that is perceived to add greater functionality (i . e . accuracy , comprehensiveness , etc . ) will have a positive impact on the usefulness criterion (Davis et al . , 1989) . Thus , system design characteristics that add value will positively influence one’s perception of a system’s usefulness . Similarly , perceived ease of use is also expected to be impacted by external variables such as training , documentation , and support .

2 . 3 . SOCIAL PRESSURE TO USE THE TARGET SYSTEM

Innovation characteristics research also postulates that perceived y oluntariness impacts behavioral intentions . Voluntariness is the extent to which potential adopters perceive the adoption decision to be non-mandated ; this construct has been shown to be more than binary , i . e . potential adopters can perceive varying levels of choice in the adoption of an innovation (Moore & Benbasat , 1991) . Although Moore and Benbasat (1991) included voluntariness as a component of the overall construct of user perceptions , in other research (Moore & Benbasat , 1990) , voluntariness was treated as an independent predictor with a direct relationship to usage intentions .

In the theory of reasoned action (Ajzen & Fishbein , 1980) , the other factor besides attitude that determines intentions is subjecti y e norm which is a function of the perceived social pressure to engage in the behavior . Subjective norm is a construct akin to voluntariness and assesses the extent to which individuals value the opinions of referent others who would view system usage in a positive or negative light . Subjective norm is defined as the influence of an overall general referent and is determined by the sum of normative beliefs about dif ferent referent groups (e . g . in


the context of wordprocessing software again , a normative belief is ‘‘I believe that my professors would want me to use the software’’) weighted by a subject’s motivation to comply with the wishes of the referents (e . g . for the normative belief above , the statement ‘‘I generally like to do what my professors want me to do’’ captures a user’s motivation to comply) .

In the technology acceptance model , the relationship between social pressure and behavioral intention to use is not explicitly included , as it is in the theory of reasoned action . The assumption is that subjective norms af fect usage intentions both indirectly via attitudes (due to internalization and identification processes) and directly (via compliance) , and there are no measures that can clearly separate these two (e . g . intentions to use caused by a superior’s mandate versus those caused by an individual adopter’s own feelings and beliefs) . Also , subjective norms may be influenced by attitude , thus confounding the relationships (Davis et al . , 1989) .

The voluntariness construct of innovation characteristics research and subjective norm of the theory of reasoned action both capture the extent to which a potential adopter is influenced by perceived pressure to utilize a target system . Thus , they appear to represent alternate conceptualizations of the same construct ; perhaps with subjective norm being more broadly defined to include all referent groups as sources of social pressure , as opposed to pressure perceived to emanate from ‘‘superiors’’ , as in the case of voluntariness .

In summary , all three bodies of research on technology acceptance upon which the current research model is based posit that behavioral intentions to use a particular system are outcomes of attitudes toward the system and—except in the technology acceptance model—social pressure to use it . Attitude is determined by beliefs about outcomes of using the system . Focusing on intentions as a surrogate for actual usage is appropriate given that both the theoretical formulations of the theory of reasoned action and the technology acceptance model and subsequent empirical studies (e . g . Davis et al . , 1989) provide support for this relationship . Five of the seven beliefs discussed above—usefulness , ease of use , compatibility , image , and result demonstrability , were included as components of user perceptions . It was decided not to include the two remaining constructs—trialability and visibility— because , as explained subsequently , these dimensions were either not relevant to the particular organizational context for the study , or included in the definition of another construct . This belief set was selected because it subsumes the beliefs included in the technology acceptance model and avoids the additional data collection overhead associated with the theory of reasoned action’s beliefs (Davis et al . , 1989) . Further , the use of a standard set of items for the various beliefs developed by Moore and Benbasat (1991) facilitates the cumulative tradition of research and allows for cross-study comparisons . The voluntariness construct was preferred to the theory of reasoned action’s subjective norm for the same reasons .

2 . 4 . TRAINING AND USER PERCEPTIONS

The left hand-side of the research model in Figure 1 indicates that training is a key antecedent to the development of user perceptions about the target system . In addition , the model emphasizes the moderating ef fects of two additional sets of variables—individual dif ference and situational—on the development of user perceptions through training experiences . Situational and individual dif ference


variables possibly influence user perceptions directly as well , but these relationships are not the focus of this study . The theoretical bases for these relationships are discussed below .

Sein et al . (1987) propose that there are two primary outcomes associated with end-user training—the acquisition of an appropriate mental model of the system and a high level of motivation to use the system in the future . While it is evident that knowledge about the to-be-learned system imparted through training may lead to the development of an appropriate mental model , the antecedents of motivation to use are not as clear . The training literature has treated motivation as both an exogeneous variable (i . e . an input to the training process) and as an outcome variable . For example , Noe (1986) posits that training outcomes can be influenced by a variety of factors , including trainee expectations , motivation , and attitudes . A positive relationship between training outcomes and trainee motivation was empirically supported in studies by Tannenbaum , Mathieu , Salas and Cannon-Bowers (1991) and Baldwin , Magjuka and Loher (1991) . In this context , Tannenbaum and Yukl (1992) observe that it is necessary to conduct further research on motivational antecedents as there are dif ferent motivational modalities , including motivation to attend , motivation to learn , and motivation to transfer , with the last category of motivation referring to behavioral intentions (Tubbs & Ekeberg , 1991) .

In addition to the general training literature , recent research in information systems has also emphasized the role of motivation as an outcome of training (Bostrom , Olfman & Sein , 1988) . Indeed , Bostrom et al . (1988) suggest that the technology acceptance model provides a good conceptualization for the outcomes of user training processes , where the perceptions of usefulness and ease of use result in motivation to use . Such a sentiment is echoed by Davis et al . (1989) in the observation that ‘‘external variables . . . provide the bridge between internal beliefs , attitudes and intentions represented in the technology acceptance model and the various individual dif ferences , situational constraints , and managerially controllable interventions . ’’ (p . 988) . In fact , they specifically point to educational programs and training as influences on user perceptions such as usefulness and ease of use . Sein et al . (1987) suggest that learning the rudiments of the system is not an adequate outcome of training ; the training process must induce a high level of motivation in the trainee to continue with system usage in the post-training work environment . This motivation to use is particularly crucial for end-user technologies where there is an alternate method available for task completion , as is the case for the technology being examined here . As Tannenbaum and Yukl (1992) note in their review of the training literature , empirical results suggest that trainee learning is a necessary but not suf ficient condition for behavior change .

The relationship between training experiences and user perceptions as depicted in Figure 1 derives theoretical support from the works cited above . User perceptions are hypothesized to be influenced by training experiences ; positive user perceptions are conceptualized as equivalent to higher levels of motivation , which result in greater system use . Thus , perceptions are the mediating construct through which training af fects attitude and motivation for system utilization . The relationship between one specific user perception—perceived usefulness—and motivation to use has been drawn previously by Olfman and Bostrom (1991) in an experimental study of two training methods and by Bostrom , Olfman and Sein (1990) in an examination


of the ef fects of learning style on training ef fectiveness . The research model guiding this study extends the user perception set to include other perceptions identified by Moore and Benbasat (1990 , 1991) as relevant to user acceptance . The extension should provide a finer-grained analysis of the ef fects of training on motivation to use .

2 . 5 . TRAINING EXPERIENCES

The traditional definition of training characterizes it as ‘‘the formal procedures which a company utilizes to facilitate learning so that the resultant behavior contributes to the attainment of the company’s goals and objectives’’ (McGehee & Thayer , 1961 : p . 10) . Since that early conceptualization the definition of training has been broadened to include a wide range of methods including simulations and games , computer-assisted instruction , and behavior modeling (Tannenbaum & Yukl , 1992) . Although a significant body of prior research has focused on the evaluation of alternate training methods (Tannenbaum & Yukl , 1992) , this research has failed to yield conclusive results about their relative merits . Campbell (1988) observes that the majority of such studies have been experimental in nature and focused on comparing one training method to another or to a control condition . Additional research on the ef fectiveness of training methods is clearly necessary given that employers spend approximately $30 billion on formal training and $180 billion on informal training each year (Carnevale , Gainer & Villet , 1990) .

In the information systems field , several classifications of training methods exist . Olfman and Bostrom (1991) distinguish between applications-based and construct- based training , where the goal of the former training method is to induce a high level of personal relevance for the trainee while the latter contains more generic material about the software system and its use . Davis and Bostrom (1993) experimentally examine the ef fects of two alternate training methods : instruction- based and exploration-based on a user’s learning performance and attitudes toward a system . Instruction-based training connotes a more passive approach to learning while exploration-based training suggests that the training technique encourages experimentation in contexts that are already meaningful for the trainee . In keeping with this view , in the current research model the training construct refers to the availability and use of two training-related experiences : formal training and self-training . The notion of self-training is similar to the trialability construct identified as a component of user perceptions (Moore & Benbasat , 1991) . In this study , this is treated as an exogenous as opposed to endogenous variable .

The most pervasive form of training is that delivered through a instructor-student relationship in a classroom setting . However , formal training through the use of some type of structured activity with an instructor is not the only method of providing assistance in the use of a new system . Beyond formal training , researchers have also examined the ef fects of self-training through on-line help and tutorials on outcome measures such as accuracy , attitude , and knowledge (Carroll , Mack , Lewis , Grischkowsky & Robertson , 1985 ; Raban , 1988) . Self-training or unassisted exploration has been shown to possess several desirable attributes such as deep user involvement in learning , providing users with excitement and motivation to unveil more of the system’s functions , and allowing individuals generally to engage


themselves more deeply in the cognitive activity of understanding how the software operates (Robert , 1987) .

In summary , the research model seeks to examine the ef fects of two training- related experiences on the development of user perceptions . While these training experiences are neither mutually exclusive nor collectively exhaustive , they are among the ones most frequently utilized in organizational settings . Insights gained about the relative ef fects of these experiences should inform both theory and practice in training .

2 . 6 . MODERATING EFFECTS ON TRAINING : INDIVIDUAL DIFFERENCES AND

SITUATIONAL VARIABLES

In a recent review of training and development in organizations Tannenbaum and Yukl (1992) highlight the need for research in training to move away from studies that show that a particular training method is useful to research examining the contingencies under which a particular training experience is ef fective . The fact that training experiences may not be equally ef ficacious for all individuals—also referred to as apitude-treatment interactions—has been a recurring theme in several studies (see Tannenbaum & Yukl , 1992) . An aptitude is broadly defined as any characteristic of trainees , including knowledge , skills , and previous experiences , that is a determinant of their ability to benefit from training (Cronbach & Snow , 1977) . However , despite the importance of aptitude-treatment interactions to the design of training programs , empirical research examining aptitude-treatment interaction in organizational settings has been limited (Tannenbaum & Yukl , 1992) .

In this study , two sets of variables are examined for their potential moderating ef fects on the relationship between training experiences and user perceptions . Consistent with the aptitude-treatment interaction tradition , one set of variables is termed indi y idual dif ferences . A second set , situational variables , attempts to capture the larger organizational and task context within which the new information system will be utilized .

In a recent study Bostrom et al . (1990) review prior research in individual dif ferences and software learning and classify individual dif ference variables into four categories : states , which include constructs such as attitude toward computers and attitudes toward the job ; structures-strategies , which include memory , reason- ing , and visual abilities ; cognitive traits such as intelligence and locus of control ; and descriptive traits such as educational background , work experience , and experience with specific software . The specific indi y idual dif ference y ariables included in this study are the job class of an individual (e . g . secretarial vs . managerial) , the functional area (information systems vs . non-information systems) , the number of years in the work force , the educational background of the individual as indicated by the highest degree obtained , their self-rated familiarity with computers , and their self-rated prior experience with graphical user interfaces . In terms of the Bostrom et al . (1990) classification , the traits examined here fall into the descriptive category .

In an alternate classification , Sein et al . (1987) posit that each individual brings a certain set of traits and characteristics to the training experience , including cognitive traits , motivational traits , task domain knowledge , or previous experiences with dif ferent tasks or work that an individual performs ; and referent experiences , or


experiences with other information systems that an individual brings to training . Additional support for the specific individual dif ference variables examined here can be found in another recent review of research focused on the acquisition of computer skills (Gattiker , 1992) , where the author notes that significant ef fects were found for age in skill acquisition and retention . The number of years an individual has spent in the work force is treated here as a surrogate for age . The job class of an individual reflects the individual’s task domain knowledge (Sein et al . , 1987) , while an individual’s educational background which may also bear on task domain knowledge is included based on findings reported in Borgman (1984) . The other three individual dif ference variables all fall under the rubric of referent experiences (Sein et al . , 1987 ; Bostrom et al . , 1988) and attempt to capture the experience an individual has with systems and software that are related to the target system .

There are important theoretical and pragmatic reasons for focusing on these particular individual dif ference variables as opposed to others . From a theoretical perspective , the studies cited above and reviewed by Bostrom et al . (1990) have confirmed the ef fects of these variables . From a pragmatic perspective , these variables are relatively easy to operationalize in a field setting as opposed to the cognitive or structure-strategy individual dif ference variables that require extensive data collection and are logistically dif ficult to administer to large samples .

The second set of moderating variables in this study , situational variables , are of two types : a task - related variable that refers to the availability of meaningful work to perform using the new system ; and tool - related variables that include whether or not the system was available to the user prior to attending formal training , and the length of time for which the user has had the system . These variables represent the ‘‘situational constraints and managerially controllable interventions’’ that Davis et al . (1989) suggest are influences on user perceptions .

Training should highlight the relevance of the system to trainees’ jobs (Bostrom et al . , 1988) . If people determine that they can use the system to complete their current job assignments , or are given meaningful new work to perform with the system , they should tend to find the system more useful . Individuals who are given meaningful work have a way to connect their training experiences with the new software environment ; primary involvement with the to-be-learned software has been shown to be a strong predictor of successful training outcomes (Boothman & Feldstein , 1990) . The availability of meaningful work as a determinant of successful training outcomes was also noted in a study of Internal Revenue Service managers by Pentland (1989) where long-term impacts on retention were observed for trainees who were able to practice newly acquired computer skills immediately after returning to the job . Although Rosson and Grischkowsky (1987) found that existing knowledge about systems tended to hinder rather than help the learning of new systems , Davis and Bostrom (1993) present an alternative view . They suggest that , based on assimilation theory , meaningful learning takes place when trainees are able to actively work with and integrate new knowledge acquired in training with existing knowledge . Further , previous knowledge of the product prior to training leads to a better understanding of the product during training and greater use after training (Olfman & Bostrom , 1991) . Potential adopters , thus , need to be given an opportunity to experiment with and develop knowledge about the system prior to attending formal training . Existing knowledge may be a matter of individual


dif ferences or may also be influenced by situational variables of the type included in this study : the prior availability of both the technology and appropriate tasks as a context for exploring the technology . Thus , the two tool related variables included in the study—the availability of Windows prior to training and the total elapsed time since an individual had Windows available to them—although not explicit indicators of prior knowledge about the system , represent a facilitating condition for prior knowledge about the system .

The research model , thus , is focused on two important sets of relationships—one , the linkage between user perceptions and user behavior , manifest in user intention to use the new system in the future ; and two , the relationship between training and user perceptions of a new information system , moderated by the ef fects of individual and situational variables . Consistent with the theory base of the technology acceptance models underlying the study , the focus is on usage intentions which are treated as a surrogate for actual system usage . Each linkage corresponds to a specific research question examined here , viz ., (1) what is the relationship between user perceptions and future use of the information technology innovation? and (2) what are the moderating ef fects of individual and situational variables on the relati y e impact of two training experiences on the development of user perceptions of using an information technology innovation?

3 . Methodology

3 . 1 . OPERATIONALIZATION OF VARIABLES

3 . 1 . 1 . User perceptions , attitude , y oluntariness , and future use The scales for user perceptions were derived from the instrument developed by Moore and Benbasat (1991) by rephrasing items slightly to reflect the specific innovation examined here—the Windows interface and Windows-based software— and by selecting a subset of items due to constraints on the length of the questionnaire . Each scale , except for compatibility , consisted of a minimum of three items—statements regarding Windows and Windows-based software that respondents scored on a 7-point Likert-type scale with the end points being ‘‘strongly disagree’’ and ‘‘strongly agree’’ . Recall that five user perceptions were included in the research model—usefulness , ease of use , compatibility , image , and result demonstrability . In their instrument development procedures , Moore and Benbasat (1991) subjected the user perception scales to an intensive validation procedure to determine both reliability and validity and the scales were , hence , deemed satisfactory for the present study . The use of standard items and scales has been frequently recommended as it facilitates the cumulative tradition of research (e . g . Adams et al . , 1992) . The construct of voluntariness was similarly measured through the four-item scale suggested by Moore and Benbasat . Furthermore , two additional scales were developed for the attitude and future use constructs which included four and two items , respectively . In keeping with the definition of attitude as an af fective response , the items comprising this scale were ‘‘I like using Microsoft Windows and Windows-based software’’ , ‘‘Microsoft Windows and Windows-based software is fun to use’’ , ‘‘I dislike using Microsoft Windows and Windows-based software’’ , and


T ABLE 1 Research y ariables and definitions

Variable class Label Definition

Outcomes

User perceptions

Training experiences

Individual dif ference variables

Situational variables

FUTRUSE VOLUNT ATTITUDE

USEFUL EASEUSE COMPAT IMAGE RESULT

FORMTRAN SELFTRAN

YRSINWF FAMRCOMP PRIORGUI DEGREE JOBCLASS ISCLASS

MEANWORK HADWINMT WINPRIT

Intended future use Perception of innovation usage being voluntary Af fective perceptions

Perceptions of relative advantage Perceptions of ease of use Perceptions of compatibility Perceptions of high-status image Perceptions of result demonstrability

Formal training Self-training

Number of years in work force Familiarity with computers Prior GUI experience Highest degree obtained Respondent’s job class Respondent’s functional area (IS / Non-IS)

Availability of meaningful work to perform with Windows Number of months Windows available Availability of Windows prior to training

‘‘Microsoft Windows and Windows-based software provides an attractive working environment’’ . The items included in the future use scale were ‘‘I intend to increase my use of Microsoft Windows and Windows-based software in the future’’ , and ‘‘I intend to completely switch over to Microsoft Windows and Windows-based software’’ .

Table 1 lists the various constructs and the labels used for them in the subsequent results . Table 2 presents the number of items comprising each scale , and Cronbach’s Alpha for scale reliability (Cronbach , 1970) . Although the reliability coef ficients of three scales (compatibility , voluntariness , and future use) are less than the minimum value of 0 . 70 often deemed to be satisfactory (Nunnally , 1967) , none are lower than

T ABLE 2 Scale reliabilities

Scale Number of items Reliability*

USEFUL EASEUSE COMPAT IMAGE RESULT ATTITUDE VOLUNT FUTRUSE

9 7 2 3 3 4 4 2

0 . 95 0 . 86 0 . 67 0 . 93 0 . 77 0 . 83 0 . 64 0 . 60

* Reported reliability is Cronbach’s Alpha .


0 . 60 . In spite of their less than ideal reliabilities , these scales should be usable given the exploratory nature of this research .

3 . 2 . TRAINING EXPERIENCES , INDIVIDUAL AND SITUATIONAL VARIABLES

As stated previously , training experiences were conceptually of two types—formal training and self training . Each of these was operationalized as a binary variable and solicited a yes / no answer from respondents .

Individual variables assessed through the instrument included job class ; highest degree obtained ; number of years spent in the work force ; familiarity with computers measured on a seven-point interval scale with ‘‘never used’’ and ‘‘very familiar’’ as the end points and ‘‘somewhat familiar’’ as the midpoint ; prior graphical user interface experience measured on a 7-point interval scale with ‘‘never used one’’ to ‘‘used one a lot’’ as the end-points and ‘‘used one occasionally’’ as the mid-point . For the job class variable , respondents were asked to select one of 17 job titles ranging from secretary , accounting specialist , to vice-president and senior vice-president . These 17 job titles encompassed all occupational levels in the respondents’ organization . Prior to data analysis , two recodes of job class were done . In one recoding the sample was split into three categories representing the organizational hierarchy in increasing order—secretarial and administrative , technical-professional including information systems and financial specialists , and managerial . In the second recoding the sample was split into information systems and non-information systems respondents . The degree variable was also recoded for conceptual simplicity into two categories , those without 4-year college degrees (High School and Associate degrees) and those with 4-year college degrees or better (Bachelor’s and Master’s degrees) .

The class of situational variables in the research model included both tool- and task-related constructs . In the task category , the availability of meaningful work to perform using Windows and Windows-based software was a yes / no variable . In the tool category , the variables included were the availability of Windows to respondents prior to their attending formal training (a yes / no variable) , and the total elapsed time in months that the respondent had Windows and Windows-based software available for use .

A survey instrument was developed that included all the variables and constructs described above . A pilot test with 20 users from the same organization where the data were eventually collected led to a few minor modifications—primarily modest changes in certain statements to more accurately reflect their intended meaning , and some alteration to the order in which the questions were asked . The users from the pilot test were excluded from the final sample . The final instrument occupied five single-sided pages .

3 . 3 . THE STUDY CONTEXT AND SAMPLE

This study was conducted at a Fortune 100 corporation in the Midwest . The firm is an information technology vendor , ranks in the top five US providers of information technology and related services , and operates in over 120 nations . Two specific divisions within the corporation were targeted for the survey ; Corporate Finance and Administration , which is responsible for financial and administrative support


corporation-wide ; and the Controller’s Division responsible for United States financial operations . Departments within these two divisions include Information Systems and Services , Accounts Receivable / Payables , US Payroll Administration and Compensation , and World-Wide Financial Planning . A total of 468 surveys were distributed with 230 usable responses being returned for a response rate of 49% .

By virtue of its high technology business , the corporation has a better than average technology awareness among its personnel . This awareness has been created , in part , by the physical presence of information technology throughout the corporation and by corporate culture that automation will be employed wherever possible . Thus , the user population is fairly technology literate and the sample perhaps more technologically sophisticated than user populations in general . As discussed later , this organizational milieu must be taken into account when interpreting the results . However , the bulk of technology related experience of the users has been with large systems including minis and mainframes ; the personal computer was still fairly new to the organization . The two divisions surveyed had personal computers available to everybody for a period ranging from 2 – 5 years , and prior to that , used terminal-mainframe access extensively . During the first few years of personal computer use , the operating system used was DOS . Within the last year and a half , both divisions were provided with a standard Microsoft Windows-based environment which minimally included the Windows-based software packages Excel (spreadsheet) , Word (word processing) , and Powerpoint (presentation graphics) on all their personal computers . Since all users had access to this new environment , visibility of the innovation was not an issue in this study because no variability in perceived visibility was expected . However , users had the option of using either Windows or DOS (with appropriate software packages) on their workstations ; both environments were equally easily accessible . The basic functionality of fered in the two environments , e . g . word processing , spreadsheets , graphics , etc . — was the same ; the major dif ference was in the interface .

The corporation contracted the training on Windows and Windows-based software to an external training provider . Since the functionality of fered by the new environment was largely the same as before , training was focused on familiarizing individuals with the new interface . All formal training was conducted in a classroom setting where each participant had individual access to a personal computer loaded with Windows and the application software packages . Formal training consisted primarily of classes that were one day in length with sessions incorporating an even mixture of lecture and hands-on exercises . Participation in the training programs was voluntary .

All individuals were also given access to various self-training options . These included interactive tutorials from vendors and third party interactive training packages . People were also encouraged to perform self-directed training through self exploration . Everybody had access to vendor-provided manuals as well as third party ‘‘how to’’ books .

Formal training was very structured and instructor-driven whereas the self- training materials provided the opportunity for trainee-directed learning . As is evident from the description above , in terms of the classification of training methods used by Davis and Bostrom (1993) , both instruction- and exploration-based training experiences were available .


4 . Results and discussion

4 . 1 . DATA ANALYSIS AND RESULTS

Consistent with the two research questions guiding the study , the following analyses were performed . The relationships between attitude and user perceptions , and between intended future use and attitude and the perception of voluntariness were investigated through multiple regression procedures . The relative impacts of training experiences on the development of perceptions moderated by individual and situational variables were examined through multivariate methods including multivariate analysis of variance (MANOVA) and multivariate multiple regression analyses .

Descriptive statistics for all research variables are provided in Tables 3 and 4 . Table 5 presents the regression results for the relationship between user perceptions and user behavior . User perceptions accounted for 70% of the variance in attitude , while attitude and voluntariness together explained 27% of the variance in future use . The strength of the former relationship clearly indicates that user perceptions are good predictors of attitudes toward use of the technology , a finding consistent with prior research (e . g . Moore & Benbasat , 1991) . Although not as compelling , the strength of the latter relationship is also consistent with results obtained in prior research (e . g . Davis et al . , 1989) .

Four of the five user perceptions had highly significant coef ficients in the user perceptions-attitude relationship ( p , 0 . 01) . The image construct was not significant in the regression equation and was hence , eliminated from all further analyses .

From Table 3 it is evident that many respondents availed themselves of both training experiences . In order to assess the impact of moderating variables on the

T ABLE 3 Descripti y e statistics for categorical y ariables

Distribution

Variable class Variable Yes No Missing


Training experiences


WINPRIT MEANWORK

FORMTRAN SELFTRAN

GENDER

DEGREE

JOBCLASS

ISCLASS

163 220

182 151

Male Female

High School Associate Bachelor’s Master’s

Sec . / Admin . Tech . -Prof . Managerial

IS Non-IS

49 9

48 69

93 111

57 32 80 53

64 122 40

35 191

18 1

0 10

26

7

4

4


T ABLE 4 Descripti y e statistics for continuous y ariables

Variable class Variable Mean Standard derivation Valid cases

Outcomes

User perceptions



FUTRUSE VOLUNT ATTITUDE USEFUL EASEUSE COMPAT IMAGE RESULT YRSINWF FAMRCOMP PRIORGUI HADWINMT

5 . 53 5 . 15 5 . 78 5 . 44 5 . 25 5 . 39 3 . 65 5 . 36

12 . 50 5 . 17 2 . 31

12 . 38

1 . 02 1 . 03 0 . 86 1 . 01 0 . 93 1 . 08 1 . 47 1 . 05 9 . 99 1 . 35 1 . 87 8 . 73

227 230 230 230 230 230 228 229 223 230 224 221

YRSINWF is number of years . HADWINMT is number of months . All other variables are measured on a 7-point scale .

relative ef fects of the two training methods , two mutually exclusive groups of respondents were formed—those who had only formal training (59 respondents) and those who had only self-training (28 respondents) . A training experience variable , TRAINING , was created and coded to record this information .

The direct ef fects of training experiences on user perceptions are not relevant because of the assumption of moderating ef fects as suggested by the research model . However , to gain insight into the combined ef fects of the two training experiences , perceptions of those who used both training methods (123 respondents) were compared to the mutually exclusive groups using MANOVA procedures . The overall MANOVA was not significant .

Correlations among the dependent variables constituting user perceptions are shown in Table 6 . As might be expected , all user perceptions were strongly correlated with each other . (An examination of the variance inflation factors to

T ABLE 5 Regression results

Regression equation ( n 5 226) R-square Beta t -value

ATTITUDE 5

FUTRUSE 5

USEFUL 1 EASEUSE 1 COMPAT 1 IMAGE 1 RESULT ATTITUDE 1 VOLUNT

0 ? 70

0 ? 27

0 . 296 0 . 151 0 . 305 0 . 049 0 . 204 0 . 477

2 0 . 148

4 . 460** 2 . 954** 5 . 414** 1 . 289 ns 3 . 551** 8 . 286** 2 . 573*

* : significant at p , 0 . 05 . ** : significant at p , 0 . 01 ns : not significant .


T ABLE 6 Correlations among user perceptions

USEFUL EASEUSE COMPAT RESULT

USEFUL EASEUSE COMPAT RESULT

1 . 000 0 . 661** 1 . 000

0 . 737** 0 . 590** 1 . 000

0 . 744** 0 ? 610** 0 ? 643** 1 . 000

** : significant at p , 0 . 01 .

detect multicollinearity as recommended by Neter , Wasserman , and Kutner (1985) indicated that multicollinearity did not af fect the results in Table 5 when these correlated variables were used as independent variables . ) However , using these as correlated dependent variables necessitated the use of appropriate analytic procedures to test for moderating ef fects as described below .

Table 7 presents , in summary form , the results of analyses exploring the moderating ef fects of individual and situational variables on the relationship between training experiences and user perceptions . These results were derived from the following analysis procedures . For continuous variables , a multivariate multiple regression procedure with all four user perceptions as dependent variables and including an interaction term as an independent variable in addition to the training variable and a moderator variable (Baron & Kenny , 1986) was used first to test for moderating ef fects . If the overall F -statistic using Pillai’s criterion was significant , univariate regression procedures were used to determine which dependent variables accounted for the significant result . For example , Table 7 shows that the number of

T ABLE 7 Significant interaction ef fects

Variable class

Moderating variable

User perception

F / t - value 1 , 2 p -value

Individual YRSINWF YRSINWF YRSINWF YRSINWF FAMRCOMP DEGREE DEGREE DEGREE

USEFUL EASEUSE COMPAT RESULT COMPAT USEFUL COMPAT RESULT

2 2 . 796 2 1 . 998 2 3 . 195 2 2 . 475

1 . 943 8 . 135 4 . 176 9 . 321

0 . 006 0 . 049 0 . 002 0 . 015 0 . 055 0 . 006 0 . 044 0 . 003

1 . ANOVA F -values are reported for categorical variables , regression t -values for continuous variables

2 . A positive F / t -value implies that the moderator variable enhanced the ef fects of self-training relative to formal training on the specific user perception . For example , the more advanced the educational level of an individual , the more beneficial self-training is as compared with formal training in developing perceptions of usefulness . Negative F / t values are similarly interpreted , e . g ., the greater the number of years spent in the work force , the more beneficial formal training is as compared with self-training in developing perceptions of usefulness .


years an individual has spent in the work force moderates the relationship between training experiences and perceived usefulness . This result was obtained by first running a multivariate multiple regression (i . e . a canonical analysis utilizing the MANOVA procedure in SPSSX) between the user perceptions as the set of dependent variables and training , years in the workforce , and (training * years in the workforce) , as the independent variables . Because the overall F -test was significant ( p , 0 . 05) , univariate regressions were run with each user perception as the dependent variable and the same independent variables . If the F -value for the univariate regression was significant , a significant ( p , 0 . 05) coef ficient for the product term in the independent variable list was interpreted as indicative of a moderating ef fect (Baron & Kenny , 1986) .

Similarly , for categorical variables , moderating ef fects were investigated by a two-way MANOVA procedure for each combination of training with individual and situational variables on the four user perceptions . If the overall MANOVA was significant ( p , 0 . 05) (again using Pillai’s criterion since it is robust and accounts for unequal cell sizes (Marascuilo & Levin , 1983)) , a follow-up ANOVA was used to isolate the ef fects . For example , Table 7 shows that highest degree obtained exhibits a moderating ef fect on the relationship between training experience and perceived usefulness . This result was obtained by first running a two-way MANOVA with all four user perceptions as dependent variables and training and highest degree obtained as the two treatments . Since the overall F -test was significant , univariate two-way ANOVAs with each user perception as a criterion variable and the same two treatments were run . As suggested by Baron and Kenny (1986) , significant ( p , 0 . 05) interaction ef fects were interpreted as indicative of moderating ef fects .

The results in Table 7 show that while three individual variables interacted with training experience to produce dif ferent ef fects on user perceptions , no situational variables showed similar ef fects .

4 . 2 . DISCUSSION

Two research questions were posed at the outset of this study : (1) what is the relationship between user perceptions and future use of an information technology innovation? and (2) what are the moderating ef fects of individual and situational variables on the relative impact of two training experiences on the development of user perceptions of using an information technology innovation? These questions are addressed below in light of the results of the data analyses .

4 . 2 . 1 . User perceptions , attitude and future use The results in Table 5 suggest that af fective outcomes or overall attitude and the perception of voluntariness both play a role in determining individual intention to use an information technology innovation in the future . These results provide further validation and replication of prior research investigating the attitude- intention relationship (e . g . Ajzen & Fishbein , 1980 ; Mathieson , 1991) . Attitude and voluntariness account for about a quarter of the variance in future use intentions , indicating that intentions are impacted by other factors as well . In the context of this particular study , a speculative explanation for the unexplained variance in intentions could be the inherent uncertainty in the technological environment . Because the


organization is at the leading edge of information technology , there is a constant flow of information to users about new developments in interface technologies such as developments in voice-activated or touch-based interfaces . Given this environment , users may be unwilling to commit to a particular platform if they expect significant changes in the near future . One would not expect this to be true if the technology in question was a fairly stable and entrenched one and change was not as imminent—for example , DOS in the mid 1980s . Another factor that could possibly influence usage intentions is the idea of ‘‘personal innovativeness’’ (Leonard-Barton & Deschamps , 1988) . Personal innovativeness refers to an individual’s predisposition to try new systems . The unexplained variance , however , needs further investigation .

The strength of the relationship between perceptions of the attributes of a system and overall attitude toward a system is compelling evidence of the role that perceptions play in determining attitude . Again , beyond further supporting and validating prior research that has examined similar relationships (e . g . Rogers , 1983 ; Moore & Benbasat , 1991) , the results provide some interesting insights into the relative importance of various perceptions in determining attitude . Of the five perceptions included in the model here , usefulness and compatibility emerged as the most significant determinants of attitude , result demonstrability and ease of use were next , and the perception of image did not appear in the relationship at all .

The fact that perceptions of compatibility were significantly related to attitude is consistent with the nature of the target system . The Windows interface provides a radically dif ferent environment from the traditional , character-based interface (Davis & Bostrom , 1993) that the user population was accustomed to , necessitating a significant change in work behaviors . To the extent that users feel that this new environment is compatible with their particular style of working , they ought to have more positive attitudes toward it . Result demonstrability , according to Moore and Benbasat (1991) , measures the tangibility of an innovation—the extent to which it is observable and the results from its use can be communicated to others . The individual items comprising the construct—e . g . ‘‘The results of using Microsoft Windows and Windows-based software are apparent to me’’—suggest that result demonstrability is also an indicator of user understanding and the perception of being in control while interacting with and using the system .

Given the nature of the innovation studied here , an interesting finding in examining the relationship between perceptions and attitude is that while usefulness emerged as a strong determinant of attitude , ease of use played a considerably less significant role . This finding is consistent with those of Davis (1989) who suggested that the ef fect of ease of use is weaker because it is mediated to some extent by usefulness . However Adams et al . (1992) , in their comparison of attitudes toward three software packages , found inconsistencies in terms of the relative importance of ease of use and usefulness . In our study , it is somewhat perplexing that ease of use should be less important than usefulness in determining attitude . This is particularly so as the system being examined (Windows and Windows-based software) is generally perceived and marketed as being easy to use ; leading one to believe that ease of use should af fect attitude to a large extent . One possible explanation could be that the system is inherently easy to use and therefore , ease of use does not play an important role in determining people’s attitudes because they do not consider it to be a major issue . Such a conclusion , however , is not supported by the data—the


mean score and standard deviation for ease of use is roughly comparable to the other perceptions . An explanation more consistent with the context of the study here is that the user population is technically literate ; their overarching concern in the use of a new system is likely to be the utility it provides them , as measured through usefulness , as opposed to the ease of use of the system .

The perception of image did not appear in the final regression relationship as a predictor of attitude , although it has done so in other studies (Moore & Benbasat , 1991) . This result , however , is consistent with Rogers (1983) , who did not identify image as a separate construct , but included it as a part of relative advantage . The fact that image did not appear to af fect attitude might also be somewhat idiosyncratic of the user population in this study—technology per se is not perceived in the organization as a high-status item ; all personnel have virtually unlimited access to it . In particular , by organizational mandate , all workstations are equipped with Windows . The overall sample mean for image was toward the middle of the scale (3 . 65) , again , indicating that users do not perceive system usage as either enhancing or detracting from their image in the organization .

Two perceptions identified by Moore and Benbasat (1991) , visibility and trialability , were excluded from this study . Visibility did not appear relevant in the context of this study ; the visibility of the innovation was not in question as information technology , in general , and Windows in particular is pervasive in the organizational environment and thus , everybody has equal opportunity to see the technology . This might also be a possible reason why image did not appear in the perceptions-attitude relationship . Concepts similar to the construct of trialability were included as independent variables in a separate set of relationships , discussed below .

4 . 2 . 2 . Training Impacts on User Perceptions The research model underlying this study conceptualized that individual and situational variables would moderate the relationship between training experiences and user perceptions . The results obtained support this conceptualization only for the case of individual dif ference variables ; training experiences may not be universally beneficial and may have dif ferent impacts for dif ferent groups of users . This conclusion is further supported by the lack of significant dif ferences in user perceptions among those who had either one or both training experiences which also suggests that having everyone go through more than one type of training is not necessarily productive . Thus , consistent with the research model and these prelimi- nary analyses , training approaches need to be tailored to individuals , as discussed below .

Table 7 shows the statistically significant interactions between training experiences and moderator variables . The results show that structured learning experiences , of the kind provided through formal training are more beneficial than self-training for those individuals with less education (without 4-year college degrees) . This finding is intuitively appealing ; individuals who have had less opportunity to develop related computer knowledge through their formal education would benefit from having new information presented to them in an organized , structured , instructor-driven format . In fact , one might speculate that unstructured experiences such as self-training could be more frustrating rather than educational for such individuals because they are


less likely to have learned ‘‘how to learn’’ which is a benefit of the experience of advanced education .

Recent research in training has alluded to the importance of prior , related knowledge in the assimilation and absorption of new concepts (Davis & Bostrom , 1993) . This relationship finds some support in our results ; the more familiarity individuals have with computing technology , the more positi y e the impact of self-training relative to formal training on the perception of compatibility of Windows . Through more proficient self-directed exploration , individuals are better able to discern the match between the interface and their work . One might have expected prior graphical user interface experience to similarly facilitate self-training more than formal training . However , this was not observed . One possible explanation for this is the oft-cited intuitive appeal of the Windows interface ; lack of prior graphical user interface experience did not pose a barrier to self-trainers’ exploration .

The number of years individuals have spent in the work force is a useful , albeit somewhat imperfect , surrogate for their age . The greater the number of years an individual has been part of the work force , the more positive the impacts of formal training relative to self-training on the development of all perceptions . This result is easily interpreted by noting the relative infancy of the technology being studied here ; graphical user interfaces have been commercially available for under a decade , while Windows has been available for less than that . The long passage of time since these individuals received formal education in school or college makes it highly improbable that they would have been exposed to such technologies as part of their education . Hence , formal training is of particular value to this group .

Job class did not significantly moderate the impact of type of training on any user perceptions . A surprising finding from the study was that the functional area to which a user belongs did not exhibit any moderating ef fects on the relationship between training and perceptions . The two-way MANOVA with functional area and training did not yield any significant results . The data seem to suggest that the ef fects of training methods on the development of perceptions are similar for information systems and non-information systems users ; possibly because the Windows environment is radically dif ferent from prior environments for both groups .

Prior research has suggested that the benefits of training are maximized when users have an opportunity to learn in an unstructured fashion for a significant length of time (Davis & Bostrom , 1993) . Such unstructured training can be invaluable in exposing trainees to the mechanical operations needed to utilize a new system , allowing formal training to focus on deep as opposed to rote learning . Furthermore , as noted in our discussion of the research model , trainees need an opportunity to experiment with the system prior to attending formal training in order to develop an initial , albeit incomplete , mental model of the system . Any knowledge received through training thereafter can then be related to the existing model , and used to improve it . In addition , experimentation tends to generate questions about system features ; the availability of the system prior to training , coupled with a means of support to address the questions generated in a non-frustrating , satisfactory manner should result in more positive perceptions about the system . However , none of the situational variables exhibited any significant moderating ef fects suggesting that their presence is equally beneficial for exploiting the benefits of both types of training .


In summary , our results reinforce the importance of user attitude and the perception of voluntariness in the development of intentions to use a new information technology in the future , consistent with what the literature has repeatedly started to emphasize . We also find that user perceptions about the attributes of a new technology are significant determinants of attitude toward the new technology . Key elements in the development of user perceptions are the type of training experiences they have had ; the impact of training experiences , however , is not uniform across user populations . Several individual variables exhibit an important moderating influence on the ef fects of training experiences on user perceptions .

5 . Implications and future research

What are the implications of our study for practice and for future research? In so far as user attitude is a function of user perceptions and a predictor of usage intentions (and hence actual use) , in order to maximize the benefits of investments in information technology , it is critical that careful attention be paid to how user perceptions are developed . Although mandating the use of an innovation can also contribute to future use , this overall ef fect is considerably less important than that of attitude . In fact , caution must be exercised in limiting individuals’ choices about usage , as research in other domains points to the potential negative consequences of forced adoption of innovations (Ram & Jung , 1991) . In this paper we argued that training experiences can influence user perceptions ; these arguments were supported by our results .

An implicit assumption underlying this research was that training in the use of computing technology is important and should not be the item that is eliminated from information systems budgets . This is particularly true in contemporary business contexts where users , with little or no formal background in computing , are being asked to assume a significant responsibility for organizational computing . Vendor claims and media hype that new software is easy to use and requires no formal training must be treated with scepticism and carefully evaluated . How then , can the impacts of training on the development of user perceptions be maximized?

The presence of interaction ef fects shows that training needs to be tailored to meet the unique needs of individuals . The individual dif ferences that are particularly salient in this context are prior formal education , years in the work force , and prior related knowledge about computers . Failure to customize training to specific needs can be a waste of training dollars ; some users may feel frustrated and therefore not learn from the training , others may be bored , not gain any incremental new knowledge , and develop negative attitudes . This lack of user understanding and comprehension will only exacerbate the problem of justifying investments in training . As discussed previously , both structured and unstructured training methods have inherent value , but for dif ferent types of people . An important recommenda- tion that emerges from this finding is that the notion of ‘‘one size fits all’’ training programs must be re-evaluated . The insights gained from our examination of the dif ferential ef fects of various training experiences should help in the allocation of training resources .

If one is unable to customize training to individuals due to resource and other


pragmatic limitations , it might be possible to enhance the y alue of across-the-board training through appropriate management initiatives . For example , given that self-training requires time which an individual might rather expend on more pressing work-related matters , the most crucial issue to be addressed here by management in the provision of appropriate incenti y es to engage in self-learning ; experimentation requires the creation of an ‘‘organic’’ environment (Burns & Stalker , 1961) or a climate where a user finds many opportunities and incentives for technology exploration (Zmud , 1982) . Self-training needs to be encouraged by explicitly incorporating it into the performance expectations of the employee . Managers can further facilitate self-training by assigning tasks which require the use of the to-be-learned system ; thereby increasing the individual’s motivation to explore . In this context , a specific initiative under consideration at the organization studied here was to let all individuals engage in self-training , administer an exam in the fundamentals of the software after the self-training was concluded , and for those employees who performed acceptably on the exam , provide the costs of formal training as a bonus payment .

Other types of infrastructural support can also be provided to encourage self-training and to maximize the returns from formal training . Providing the software to individuals prior to the delivery of any type of formal instruction may allow them , through self-training , to construct initial mental models of the system . Although such spontaneously formed mental models may be inaccurate or incorrect (Norman , 1983) , the models may serve as a base to be progressively refined through structured learning experiences .

Beyond the practical recommendations that emerge from the study , there are also some specific contributions to research . We have replicated empirical work examining the perceptions – attitude and attitude – intentions relationships , thus contributing to the emerging literature . Further , most of the empirical work in the area of training has been experimental in nature ; the field study approach used by us provides a realistic context for an examination of the same issues . The model underlying the study allowed for the exploration of the moderating ef fects of a sizeable number of variables .

Our study suf fers from some limitations which need to be acknowledged while interpreting the results . It was exploratory in nature , guided by a research model which was grounded in prior research , but without the specification of any a priori hypotheses about the direction of the interaction ef fects . The objective was to identify specifically what factors moderate the relative ef fects of training experiences ; the design of the study and methods of analysis were consistent with that objective . The fact that this study was conducted in the field was both a strength and a weakness . The strength lies in the realism of the sample and the study context ; the weakness is the lack of controls inherent in a field study . Perhaps some relationships could not be detected because of possible measurement error as indicated by the lower than ideal reliabilities of a few scales . This may have also dampened the amount of variance explained in the regression analysis but should not diminish the results that were significant . The sample exhibited some unique characteristics which may not be completely representative of broad-based user samples in business ; because of the nature of the organization , the sample was probably more technologically sophisticated than the business population at large .


Several avenues for research remain . The relationship between attitude and future use intentions needs further investigation to determine what other factors af fect intentions ; slightly over a quarter of the variance was accounted for in our results . Our results , when compared with those obtained by Davis et al . (1989) and more recently , Adams et al . (1992) , also indicate that the relative importance of usefulness and ease of use in determining user intentions is still in question . Understanding the contribution of these two critical perceptions to usage intentions will allow management and software vendors to focus attention on those aspects of the product that will result in the greatest user acceptance . The individual dif ference variables included in this study , e . g . job class , number of years spent in the work force , and highest degree obtained , are all directly observable and can be easily measured . Consequently , the results obtained by us have direct , practical , relevance to guide management decision making . A variable that we did not include , learning style , is not so directly observable and requires measurement before it can be used to make decisions about training . Others have studied the relationship between learning style and training experiences in experimental settings (e . g . Raban , 1988 ; Bostrom et al . , 1990) and further work in the field may be desirable . We have identified several variables that moderate the relative ef fects of dif ferent training experiences . Future work may also wish to test the relationships identified here more rigorously with greater emphasis on measurement and control issues . For example , experimental studies that measure training outcomes pre- and post-training delivery could be conducted . Similar studies could also be conducted using dif ferent types of populations to sample from in order to extend the generalizability of the results .

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Paper accepted for publication by Editor B . Gaines

Training experiences and usage intentions: a field study of a graphical user interface

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