Artificial intelligence in HRM: an experimental study of an expert system

Artificial intelligence in HRM: an experimental study of an expert system – includes appendix

John J. Lawler

Expert systems are artificial intelligence (AI) applications that have shown great promise as decision aids across several functional areas of management (Feigenbaum, McCorduck & Nii, 1988; Ernst, 1988). An expert system is “a computer program which attempts to embody the knowledge and decision-making facilities of a human expert in order to carry out a task…requiring…human expertise” (Beardon, 1989, p. 87). Although less extensively used than in other areas of management, expert systems are now making their way into the human resource management (HRM) field (Kirrane & Kirrane, 1990; Ceriello, 1991; Lawler, 1992). Hannon, Milkovich and Sturman (1990) identified over thirty published expert system applications in HRM and the number has very likely grown considerably since then. Expert system applications have been developed for many different HRM activities, including compensation, benefits administration, staffing, training, and human resource planning. Northcraft, Neale and Huber (1988), Besser and Frank (1989), Chu (1990), and others have argued that expert systems could be more widely utilized in HRM in order to improve the quality of decision making. However, empirical evidence supporting this contention is limited (Lawler, 1992), so it is less than a foregone conclusion that AI technology will have its intended impact on user performance.

Expert Systems

Expert systems, designed to replicate certain abstract reasoning and problem-solving capabilities of humans (Simon & Kaplan, 1989), are most appropriate in helping users cope with semi-structured problems (Simon, 1977). Semi-structured problems are those for which a considerable body of knowledge exists as to the ways in which a given problem ought to be tackled. However, the knowledge base is highly complex and not readily accessible to those without specialized training. Consequently, organizations must rely on problem solvers who have accumulated a track record of generating solutions that, while not necessarily optimal, seem to work well. Expert problem solvers utilize heuristic, rather than algorithmic, methods. In developing an expert system, the heuristic methods of acknowledged experts in a specialized problem domain are incorporated into the program (Buchanan & Smith, 1989).

Expert systems aid non-experts in solving semi-structured problems by giving them, in effect, on-line access to expertise that may be difficult to develop and in short supply. In typical programs, designers of expert systems utilize various behavioral methods (e.g., verbal protocols) to identify the heuristics of recognized experts. Although architectures vary, such heuristics are encapsulated within the expert system, usually as a series of if-then rules. Expert system “consultations” involve the program posing questions to the user at various points. The answers to these questions, along with information stored in various databases, are used to deduce solutions to problems consistent with those that would be generated by an actual expert under similar circumstances.

A good example of an expert system is a program called MYCIN, developed at Stanford to help physicians diagnose certain relatively rare infections (Buchanan & Shortliffe, 1984). The heuristic rules of expert diagnosticians were generated through interviews and other knowledge acquisition methods. The resulting program could then be used by physicians with limited knowledge of the infections in question. In consultations with the program, users would be asked a series of questions regarding the patient. The program would respond by suggesting additional diagnostic steps. When all relevant information had been provided, the program would provide a diagnosis and suggest a course of treatment. MYCIN incorporated uncertainty handling measures and would indicate a degree of confidence in the proposed diagnosis.

Theory and Hypotheses

The principal objective of this study is to discern the impact of expert system utilization on problem-solving outcomes within an HRM context. As others have noted, research dealing in general with the effects of expert systems is both limited and often uninformed by behavioral decision theory (Milkovich, Sturman & Hannon, 1993; Shanteau & Stewart, 1992). Behavioral decision theory is concerned, among other things, with the impact of problem complexity on information processing and problem solving. As uncertainty and/or complexity increase, problems become less structured and the ability of problem solvers to engage in rational decision-making processes become compromised (Simon, 1960). On the other hand, various decision aids, such as expert systems, may mitigate the effects of complexity and uncertainty. We incorporate elements of behavioral decision theory into this study by examining the impact of both problem-solving method (with or without the aid of an expert system) and information-processing difficulty on choices made by experimental subjects.

Outcomes

Prior research related to the impact of computer-based decision aids has examined both task performance and psychological outcomes. A subject’s task performance when employing a particular problem-solving method is normally evaluated in terms of the accuracy of solutions generated and the efficiency of the process (Lamberti & Wallace, 1990; Coll, Coll & Rein, 1991; Sharda, Barr & McDonnell, 1988). In the type of study undertaken here, accuracy can be assessed by comparing a subject’s solution to a problem to that of an expert problem solver. A standard measure of efficiency is the time required by the subject to solve a problem (or reach an impasse).

Considerable research in the information systems field concerns the propensity of users and potential users of an application to employ the program effectively. Such studies are often rooted in cognitive process models of motivation and behavior (Zmud, 1979; Melone, 1990; Doll & Torkzadeh, 1991). Research focuses on a variety of constructs, including user beliefs, attitudes, intended behavior, and actual behavior, as these relate to the application in question (Thompson, Higgins & Howell, 1991). Davis, Bagozzi and Warshaw (1989) evaluated different general models of user attitudes and behavior. One of the models examined, termed the “technology acceptance model,” was especially effective in explaining the voluntary use of a particular piece of software in terms of two underlying user perceptions: the perceived usefulness of the program for accomplishing its objectives and the perceived ease-of-use of the program.(1) These two constructs are increasingly employed by other researchers in the information systems field (Adams, Nelson & Todd, 1992).

A subject’s overall task satisfaction is another psychological outcome extensively analyzed in prior research (Aldag & Power, 1986). Task satisfaction can be viewed as an intermediate outcome, a consequence of the perceived ease-of-use and usefulness of a problem solving method, which, in turn, influences a subject’s proclivity to employ the method in similar circumstances in the future.

Problem-Solving Method

Huber (1990) provides a general theoretical analysis of the likely impact of information technology on various aspects of organizational structure and performance. He defines computer-based decision aids as applications designed to facilitate an individual’s ability to accomplish a range of information processing and decision making tasks (e.g., storing, retrieving, and reconfiguring information). His typology includes expert systems, along with decision support systems and information retrieval systems. Huber posited that, other things equal, computer-based decision aids will increase both the quality (i.e., accuracy) and efficiency of decision making. That is, the decision aid is assumed to mitigate the impact of complexity and/or uncertainty on the quality of problem-solving activities. From a behavioral decision theory perspective, the extent of improvement in accuracy and efficiency is likely to depend on the problem context. The crucial issue, then, is the interaction of problem solving method and problem complexity/uncertainty. We develop this argument more fully below. However, we first consider issues relating to the main effect of problem-solving method on accuracy and efficiency. Our first two hypotheses follow directly from Huber’s arguments:

H1: Expert system use will increase the accuracy of HRM decisions made by non-experts.

H2: Expert system use will decrease the time required by non-experts to make HRM decisions.

There is an extensive body of empirical literature dealing with impact of computer-based decision aids on problem-solving performance.(2) Some studies concern expert systems, though most focus on other types of decision aids (such as decision support systems).

In a validation study of an expert system designed to help employees allocate credits in a flexible benefits program, Sturman and Milkovich (1992) reported considerable success on the part of employees (i.e., non-experts) in generating allocations consistent with what benefits counselors (i.e., experts) would have recommended. In a related study, Milkovich et al. (1993) found that users of this expert system were apt to change decisions that they had made regarding benefits choices when they received negative feedback from the expert system, thus presumably moving more in the direction of a “correct” decision. The use an expert system to help technicians diagnose problems in a computer system resulted in both an increase in problem-solving accuracy and a reduction in the amount of time required to solve problems (Lamberti & Wallace, 1990). Federowicz (1992) reports an upward shift in the learning curves of novice problem solvers when they are allowed to utilize an expert system.

There are, however, other studies that indicate expert systems may have no impact on, or even decrease, problem-solving performance. Coll, Coll and Rein (1991) studied the impact of an expert system designed to aid managers identify and clarify decision priorities. The program was applied to certain personnel decisions (retain versus discharge a low performer). Use of the expert system both decreased accuracy and substantially increased decision time.

Empirical studies of other types of computer-based decision aids have generated somewhat mixed results regarding performance effects. Sharda et al. (1988) cite several laboratory studies that directly assess the impact of use versus non-use of computer-based decision aids on problem-solving performance. Only about half of the studies demonstrated statistically significant improvements in performance when such decision aids were employed. Examples of studies demonstrating positive performance effects (accuracy or efficiency measures) include those by Bozeman and Olsen (1987), Dickmeyer (1983), and Lucas and Nielsen (1980). Studies failing to discern performance effects include those by Joyner and Tunstall (1970), Cats-Baril and Huber (1987), and Aldag and Power (1986).

In regard to the psychological outcomes under study, most prior research has involved analyses of the impact of subjects’ perceptions and attitudes on their subsequent use, or intention to use, the application in question. Perceptions and attitudes are often treated as exogenous and the impact of application utilization per se on these factors is not explicitly considered. Studies that examine determinants of perceptions and attitudes mostly focus on contextual factors and the personal characteristics of the subjects, rather than exposure or nonexposure to an application (Zmud, 1979; Adams et al., 1992). We know from prior research that people may respond rather negatively to the introduction of a computer-based decision aid (Carey, 1988; Peterson & Peterson, 1988). Computer applications may be viewed as a threat to employee autonomy, status, even job security. This argument is made specifically in regard to the introduction of expert systems by Hauser and Hebert (1992). Yet Milkovich et al. (1993) found that an expert system increased user satisfaction with the quality of the choices he or she made.

Decision aids that improve task performance and pose no immediate threat can still be perceived negatively by users. Prior to the introduction of the decision aid, problems may have been solved by applying rather simple, though inaccurate, heuristics. The decision aid in such circumstances may be disruptive. It may also increase options and thus serve to confuse the user. Thus, we have the paradox of a system that does, in some objective sense, simplify a problem, but nonetheless serves to increase the user’s perceived complexity. This phenomenon is well documented in the literature and research has often shown that subjects will avoid otherwise effective decision aids in favor of what they see to be a simpler, though less accurate, mode of problem solving (Kottermann & Davis, 1991). Since the perceived ease-of-use of an application is argued to be causally related to perceived usefulness (Davis et al., 1989), an adverse reaction to the mechanics of working with an application may also result in a diminished perception of its problem-solving usefulness. However, the reverse argument seems just as reasonable. A decision aid that does not work well may be positively perceived by users, which is precisely what Aldag and Power (1986) found. The response of the user might, in part, depend on the consequences of the decision. If consequences are minor, then he or she may not be as motivated to use the decision aid as in circumstances in which the consequences are great.

As the literature is somewhat ambiguous regarding the likely impact of computer-based decision aids on user perceptions and attitudes, we do not present any formal hypotheses concerning the relation of problem-solving method to the psychological outcomes. We do, however, analyze perceptions and attitudes in terms of problem-solving method and will relate our findings to the issues raised above.

Interaction of Problem-Solving Method and Task Complexity

The complexity of a problem or task is generally seen as a major determinant, other things equal, of the degree that a problem is or is not well-structured. Problems become more complex as the information to be processed increases and as the relationships among separate pieces of information become more interconnected. As the complexity of a problem increases, the perceptual and information-processing requirements for performing that task also increase (Wood, 1986).

We anticipate that the impact of problem-solving method on performance and psychological outcomes will be moderated by problem complexity (Lamberti & Wallace, 1990). For tasks of low complexity, users with some domain knowledge might be expected to perform at least as well without the aid of the expert system as with it. In such cases, the solution to the problem could be rather obvious. In fact, use of an expert system might even reduce performance for straightforward tasks by making them excessively involved. As task complexity increases, then the benefits of the expert system ought to become more pronounced. Yet for extremely complex problems, the accuracy of expert system-aided decisions could again converge with unaided decisions, since an expert system’s limited problem-solving scope may well be exceeded; under conditions of high complexity, non-experts should have very low performance levels regardless of problem-solving method. This relationship for accuracy outcomes is depicted in Figure 1.(3)

H3: As the complexity of a problem increases, the problem-solving performance (both accuracy and efficiency measures) of subjects when utilizing the expert system should improve relative to performance without the expert system. At very high complexity levels, performance with the expert system may deteriorate and the performance levels under the two approaches may again converge.

As we lack a sufficient theoretical base to formulate definite hypotheses regarding the impact of problem-solving method on the psychological outcomes under study, we do not present hypotheses regarding method-complexity interaction effects for the psychological outcomes. However, we examine these relationships and report those results below.

Problem Complexity

We have included complexity as a factor in our study primarily because we are interested in the extent to which it moderates the impact of problem-solving method on both performance and psychological outcomes. However, complexity is also be expected to demonstrate main effects for these outcomes. More complex problems should be more difficult to solve and thus more prone to error, a view consistent with behavioral decision theory (Hogarth, 1987; Kahneman, Slovic & Tversky, 1982; Paquette & Kida, 1988). Similarly, other things equal, complexity should decrease problem-solving efficiency. Both of the following hypotheses are also consistent with the results of an expert system study by Lamberti and Wallace (1990) that concerned the impact of uncertainty on problem-solving accuracy and efficiency:

H4: The accuracy of HRM decisions will decrease as the complexity of the problem increases.

H5: The time required to reach HRM decisions will increase as the complexity of the problem increases.

Problem complexity ought to impact perceived usefulness and ease-of-use in a similar way. As complexity increases, then, other things equal, the problem should be perceived as more challenging. Subjects should also presumably be less confident in the solutions that they generate.

H6: A subject’s perception of the ease-of-use of a problem-solving method will decrease as the complexity of the problem increases.

H7: A subject’s perception of the usefulness of a problem-solving method will decrease as the complexity of the problem increases.

Unfortunately, the likely impact of complexity on task satisfaction is not so immediately clear. Intrinsically motivated individuals may find more challenging tasks to be more satisfying, even though task performance declines. For example, Gardner (1990), arguing from an activation theory perspective, found task satisfaction increased with task complexity (although this study did not involve computer-related tasks). Yet in some circumstances we might expect that extrinsically motivated individuals, absent from higher external rewards, are apt to become less satisfied as task complexity increases. Consequently, the complexity-satisfaction relation is not easily predicted.

Research Methods

This study involves the assessment of an expert system designed to facilitate decision making in a classification-based job evaluation program. The program was developed to aid those responsible for classifying clerical positions in a large, public sector organization operating under a state civil service system. Data on the program’s effectiveness, collected during training sessions for users, are analyzed here.

The Job Classification Task

Clerical positions in this organization are classified according to both skill level and occupational series (e.g., Clerk II, Secretary III, Clerk-Typist II). Skill levels range from entry-level positions to those involving extensive experience and responsibility. Occupational series are differentiated largely in terms of core activities (e.g., filing, typing, dictation, data entry). Classifiers consequently must make two relatively independent decisions. In order to make these decisions, classifiers must analyze information on as many as eight different job factors (e.g., document production, document processing, oral communication, supervisory responsibilities), each of which is composed of several specific tasks. The tasks are associated with both skill level and occupational characteristics.

Job classification decisions were once exclusively the responsibility of professional analysts in the organization’s personnel office. For budgetary reasons, the personnel office extended limited authority to many of the organization’s departments to classify certain clerical positions according to established, though somewhat ambiguous, criteria. Non-HRM supervisors within the departments (designated as “departmental classifiers”) were then trained to make these decisions using a “paper-and-pencil” approach. The paper-and-pencil approach requires the classifiers to work with a variety of documents and instruction manuals, though the classification decision is ultimately a judgment call.

Despite training and monitoring, administrators in the personnel office were concerned about the quality of the decisions made by departmental classifiers. An expert system was seen as a means of exercising better control over actions taken by the classifiers. The expert system studied here was developed over a two year period and involved extensive interaction between the developers and the personnel analysts (who served as the experts upon whose judgmental processes the program was based). The program (see Appendix) was designed to capture both formal classification standards and the heuristic rules of the personnel analysts. It aids classifiers in identifying job tasks and associating those tasks with the appropriate job factor. In the end, the program recommends both a skill level and occupational series, providing a rationale for both. However, the recommendations are not binding and may be overridden by the user.

Pilot studies with expert analysts from the personnel office demonstrated high consistency between the recommendations of the experts and those generated by the program. Such consistency serves to establish an expert system’s construct validity (Sturman & Milkovich, 1992). Once the program worked well in that setting, it was distributed to departmental classifiers.

Research Design

To test our hypotheses, an experimental study was conducted in connection with a training program for the intended users of the expert system. All subjects were departmental classifiers who were required to attend the training sessions. None bad been exposed to the expert system prior to training. However, all of the subjects had at least some experience doing classification work via the conventional paper-and-pencil method. This study utilized a 2×3 within-subjects factorial design (two levels for problem-solving method (expert system-aided versus paper-and-pencil) and three levels for complexity (low, medium, and high)). Each of forty-eight subjects completed six job classification exercises (one for each cell in the design), so there was a total of 288 observations. Subjects were randomly assigned to one of twelve training groups. A Latin square approach served to counterbalance the order in which the problem-solving methods and complexity levels were presented to the subjects.

Training involved groups of four individuals who attended two training sessions one week apart, with each session lasting approximately two hours. The same trainers conducted all of the sessions. At various points during training, subjects completed the job classification exercises. They were provided job descriptions that contained information relating to the activities performed by a job incumbent. These forms were similar to those normally used by departmental classifiers to record information obtained while interviewing job incumbents in the first stage of the job classification process. Subjects were asked to classify these positions without guidance from the trainers, using either the expert system or the conventional paper-and-pencil approach. Subjects had access to the same written documentation they used under normal circumstances. At the end of each classification exercise, subjects were asked to recommend both skill level and occupational series classifications for the position. In addition, the subjects completed the battery of questions used to construct perceived usefulness, perceived ease-of-use, task satisfaction, and perceived complexity scales.

We used six different job descriptions in the experiment. All were of actual positions that had already been classified by analysts in the personnel office. The job descriptions were selected by the three skilled analysts in the personnel department. The analysts reviewed a number of different job analyses that had already been classified and that were drawn randomly from the department’s files. They were asked first to select a subsample of cases that all three agreed were correctly classified. They were then asked to choose two job descriptions for each of the three complexity levels. The analysts were told to select cases that reflected, in their subjective opinions, the range of task complexity typically encountered in classification work. Only job descriptions for which the analysts were in general agreement as to complexity level were used. In addition, experts were able to use the program to reproduce the classification decisions for all six job descriptions.

Measures

Problem-solving performance was measured both in terms of accuracy and efficiency. Separate measures were constructed for the skill level and occupational series decisions and each of these two decisions was coded as a dichotomous variable (1 if the subject had correctly classified the position and 0 if not). Although a controlled experiment, subjects were not constrained as to the amount of time they had to perform a classification. For each problem, a subject’s efficiency was measured as the number of minutes elapsing between initiating the task and reaching a decision.

Scales were constructed to measure the three psychological outcomes described above. The subject’s general perception of the ease of completing the task with the problem-solving method utilized (i.e., expert system or paper-and-pencil) was adapted from the an ease-of-use scale developed by Davis (1986). The items used were the same, save for wording changes to reflect differences in the task. The alpha coefficient for this scale was .87.

We did not have a direct measure of usefulness in the study, so we have used a three-item Likert scale to measure a subject’s confidence in the solution reached for a given problem. Subjects were asked their degree of confidence in each of the two aspects of the classification decisions and their overall confidence in the solution. This confidence scale is similar to those used by Lamberti and Wallace (1990) and Aldag and Power (1986). The alpha coefficient for the scale was .95. This confidence scale served as an indicator of a subject’s perceived usefulness of the method employed for solving a given classification problem. That is, the more confident a subject is that she or he has reached a correct classification decision, then, other things equal, the more likely it is that the subject will perceive the problem-solving method employed as useful.

Task satisfaction was measured by means of a semantic differential scale described by Gardner (1990).(4) Four task satisfaction items were used from the Gardner scale. The anchors for these items were: good versus bad, unpleasant versus pleasant, frustrating versus gratifying, and boring versus interesting. The alpha coefficient for the scale was .81. A fourth scale, perceived task complexity, was constructed as a manipulation check. Perceived complexity was measured with a Likert scale derived from a task stimulation scale reported by Gardner (1990).(5) Three items from the Gardner scale measure task complexity. The anchors for these items range from very simple to somewhat complex to very complex and refer to the complexity of the classification decisions, the complexity of thought processes required to perform the task, and the complexity of various subtasks required to complete the classification. The alpha coefficient for this scale was .92. The scale measured perceived complexity for the overall task, not each of the two subtasks.

As a test of construct validity, we intercorrelated the four scales (Table 1). All correlation coefficients are statistically significant at the .001 level. As would be anticipated, the ease-of-use and usefulness scales were negatively correlated with perceived complexity. Consistent with the work of Davis et al. (1989), the perceived ease-of-use and confidence scales are positively correlated. The task attitude variable also behaves in a theoretically reasonable manner [TABULAR DATA FOR TABLE 1 OMITTED] in that it is positively correlated with both ease-of-use and usefulness scales. Task attitude is negatively related to complexity. Though inconsistent with Gardner’s (1990) work, we noted earlier that the task attitude-complexity relationship is conceptually ambiguous. Taken as a whole, then, the pattern of correlations among these variables is suggestive of the construct validity of this set of scales.

Statistical Analysis

The psychological outcome scales (usefulness, ease-of-use, task attitude, and perceived complexity) are all continuous variables and were analyzed using the appropriate ANOVA method for a two-factor within-subjects design (Keppel & Zedick, 1989). Within-subjects ANOVA was also used in the case of the efficiency measure (task time). For each of these variables, statistical tests were performed for the complexity and method main effects and for the complexity-method interaction effect.

As the two task performance variables (skill level accuracy and occupational series accuracy) are dichotomous, logit analysis was used rather than ANOVA. Logit is based on a function of the form:

Prob(DV = 1) = 1 / (1 + [e.sup.-Z]) (1.1)

where: Prob(DV = 1) = probability dependent variable = 1 and e = natural base. Z is a linear combination of the independent variables in the analysis, which here include dummy variables representing the main effects, along with interaction terms. The dummy variables for the main effects were scored to assure orthogonal contrasts. Hence, the analysis is quite similar to ANOVA via multiple regression. Z can be viewed as the propensity of subjects to classify a position correctly and is written as:

Z = a + [b.sub.1](ES) + [c.sub.1] (CA) + [c.sub.2](CB) + [d.sub.1](ES x CA) + [d.sub.2](ES x CB) (1.2)

where: Prob(DV = 1) = probability dependent variable = 1; a, b, c., d., f = parameters; ES = dummy variable indicating expert system use; CA = dummy variable for moderate versus low complexity contrast; CB = dummy variable for high complexity versus moderate/low complexity contrast; ES x CA, ES x CB = interaction terms.

A maximum likelihood approach was utilized to estimate the parameters for Equation 1.2. Statistical tests of significance for individual parameters and groups of parameters are conducted very much as in regression analysis.(6) One problem with conventional logit estimates in repeated measures designs is that unobserved subject-specific effects may be correlated with the independent variables in the analysis, thus biasing parameter estimates (Chamberlain, 1984). While there are estimation techniques to handle this complication, random assignment and counterbalancing eliminated the problem in this study. Hence, conventional logit estimates are reported.(7)

Results

Manipulation Check

Within-subjects ANOVA was used to determine if there was a relationship between the subject’s perceived complexity and the assigned complexity level. We found a strong and significant complexity main effect (Table 2), with perceived complexity increasing as a function of the assigned complexity level. Hence the complexity manipulation appears to be valid. However, we also found that perceived complexity was higher for problems solved using the expert system rather than the pencil-and-paper method. The implications of this finding are considered below. There was no statistically significant method x complexity interaction effect.

[TABULAR DATA FOR TABLE 2 OMITTED][TABULAR DATA FOR TABLE 3 OMITTED]

Task Performance

Logit results for the skill level and occupational series decisions are reported in Table 3 (supplementary logit analysis, described in the discussion section, is reported in Table 4).

Skill level decisions. Expert system utilization exerted neither a significant nor positive effect on the accuracy of skill level decisions, thus contradicting H1. On the other hand, the task complexity and the method x complexity interaction effects are statistically significant at the .01 level. Assessing these results in relation to H3 and H4 requires further interpretation of the coding scheme.

The expert system variable contrasts expert system use (greater value) with the paper-and-pencil approach (lesser value). CA contrasts moderate complexity (greater value) to low complexity (lesser value), while CB contrasts high complexity (greater value) to the average effect of moderate and low complexity (lesser value). Thus the negative coefficients associated with CA and CB (both significant) indicate a general decline in the accuracy of skill level decisions with increasing complexity, which supports H4.

Hypothesis 3 posits expert system use might dampen the adverse impact of complexity on accuracy in the mid-range of complexity, with the expert system and paper-and-pencil approaches converging at higher levels of [TABULAR DATA FOR TABLE 4 OMITTED] complexity [ILLUSTRATION FOR FIGURE 1 OMITTED]. For this to be the case, the CA x ES coefficient should be positive, while the CB x ES coefficient should be negative. Although the ability of users to make accurate level classification decisions varied depending upon whether or not they used the expert system, the coefficients for the interaction terms are both negative, so that the interaction pattern for accuracy of skill level decisions [ILLUSTRATION FOR FIGURE 2 OMITTED] is clearly different from the hypothetical pattern.

A possible explanation for this is that complexity is measured in relative, rather than absolute, terms. That is, both the analysts who initially judged task complexity and the classifiers who responded to the complexity questions were evaluating this factor in relation to their experiences. Hence, what they might have viewed as classification tasks of low complexity may, in some objective sense, be quite involved. In other words, “true” complexity may be truncated in the lower range for skill level decisions. Consequently, the interaction effect depicted in Figure 2 could be seen as partially consistent with H3 (in that Figure 2 corresponds somewhat to the right half of Figure 1).

Another anomalous finding depicted in Figure 2 is the relatively flat relationship between complexity and accuracy for the paper-and-pencil solutions (despite a negative and significant main effect). Perhaps subjects invoked personal heuristics to cope with increasing complexity which were not incorporated into the expert system and were only warranted at higher levels of complexity. While our subjects were not experts in the sense that the analysts were, they were not completely naive users either. All had some experience doing basic classification work and had probably learned problem-solving techniques that might not have been familiar to the personnel office analysts. This interpretation is consistent with prior research on the effects of increasing complexity on decision-making accuracy (Paquette & Kida, 1988). The results obtained for the efficiency measure provide additional evidence as to what might have been occurring here.

Occupational series decisions. As with skill level decisions, the main effect for expert system use is negative and insignificant for occupational series decisions. Both task complexity main effect and the method x complexity interaction are significant (at the .01 and. 10 levels, respectively).

An anomalous result here is that the main effect for complexity is positive [ILLUSTRATION FOR FIGURE 3 OMITTED], though H4 anticipates a negative relationship. A possible explanation may be that, at least for occupational series decisions, classifiers have learned that relatively complex problems often fall into particular categories and use this as a problem-solving heuristic. This heuristic, though not obvious, could have been incorporated into the expert system, thus explaining why complexity seems to impact accuracy in the same way under both the expert system and paper-and-pencil conditions.

As Figure 3 indicates, accuracy under the expert system condition is less than under the paper-and-pencil condition for the low complexity condition. As complexity increases, accuracy under the two conditions converges, with expert system-aided problem solving improving markedly in comparison to the paper-and-pencil approach. This could be explained by a variant on the argument presented above regarding the method x complexity interaction for skill level decisions. Again, if we think of the complexity range as relative, then job series decisions could be at the lower (rather than higher) end of some absolute continuum of complexity, thus generating an interaction pattern that corresponds to the left half of Figure 1.

Given this conjecture, the results are more consistent with H3. That is, at low complexity levels, the expert system approach may be excessively difficult to use compared to the task at hand, so it is outperformed by the paper-and-pencil approach. As complexity increases, expert system accuracy gains in relation to paper-and-pencil accuracy. H3 also suggests that the expert system method overtakes the paper-and-pencil approach at some point. Although this is not demonstrated to occur within the complexity range considered here, the ES x CB interaction term is both significant and positive. Thus, the slope for the expert system graph exceeds that of the paper-and-pencil graph in the higher complexity region.

Efficiency. Task completion time was used to measure the efficiency dimension of task performance. Both of the main effects were found to be statistically significant (Table 2). As expected, task completion time increased with complexity (H5). Completion time was greater when the expert system was used, which runs counter to the anticipated relationship (H2). Though the complexity x method interaction effect is statistically significant, the pattern of the interaction effect [ILLUSTRATION FOR FIGURE 4 OMITTED] was not as anticipated (H3). For those problems solved using the expert system, completion time is almost a linear function of complexity level. However, for the paper-and-pencil approach, the complexity-completion time relationship is kinked. Completion time for tasks of moderate complexity is around twice what it is for low complexity tasks, yet completion time rises only slightly between the moderate and high complexity conditions.

The results obtained for the efficiency measure, though at odds with theoretical expectation, lend support to our explanation of the anomalous findings for accuracy measures, particularly the job level decisions. The expert system utilizes the same, relatively rigorous analytical method across all complexity levels. Suppose classifiers had learned that a rigorous approach does not work as well as some short-cut technique for more complex classification problems. That is, the more rigorous approach might become unwieldy as the information required increases (as when problem complexity increases). Simpler methods may then be substituted, which achieve about the same degree of accuracy for more complex decisions, but require relatively less time. Such a process would be consistent with the relationships depicted in Figure 2 and Figure 4.

Psychological Outcomes

The results for the three principal psychological outcomes considered in this study (confidence, perceived ease-of-use, and task attitude) are somewhat similar (Table 2). All three decrease in value as complexity increases. These findings support H6 and H7. Competing theoretical arguments meant that it was not possible to specify the direction of the complexity effect in the case of task attitude. Our results indicate that subjects became less satisfied with the task as complexity increased.

Conflicting findings in prior research made it difficult to specify unequivocal hypotheses regarding the impact of problem-solving method on psychological outcomes. A significant (and negative) effect is observed only in the case of user confidence. Thus the subjects seemed to sense that the expert system approach was generally less accurate than the paper-and-pencil approach. However, the absence of a significant method x complexity interaction effect for user confidence also indicates that subjects did not sense that the accura1cy of the expert system method was contingent on problem complexity.

Subjects did not seem to find the expert system more difficult to use than the paper-and-pencil approach, even though expert system use did increase perceived complexity. And their attitude toward the problem-solving task tended to be more favorable in the expert system condition though the relationship is not significant.

The method x complexity interaction effect was insignificant in the case of user confidence, but was found to be weakly significant (p [less than] .10) for perceived ease-of-use and task attitude. In the case of perceived ease-of-use [ILLUSTRATION FOR FIGURE 5 OMITTED], the paper-and-pencil method apparently begins to diverge from the expert system method at higher problem complexity levels. Although the relationship is weak, it is still consistent with the argument presented above concerning subjects possibly compensating for the greater difficulty of more complex problems by substituting personal heuristics that would tend to ease decision making under the paper-and-pencil approach. Likewise, subjects had a somewhat more favorable attitude to the overall task under the expert system condition for problems of low and medium complexity, but this difference is eliminated for problems of high complexity [ILLUSTRATION FOR FIGURE 6 OMITTED].

Discussion

We anticipate that the human resource management field will increasingly depend upon sophisticated information technology applications. Expert systems and other artificial intelligence programs, still relatively rare in HRM, are apt to become more commonplace. However, we require further research on the efficacy of such computer-based decision aids in addressing typical HRM problems.

The study undertaken here involved a setting in which experimental subjects had some prior knowledge of the issue. Even though many expert systems are designed to be used by complete novices, this is not always the case. Expert systems may be intended for users with some understanding of a particular area, though not at the level of an acknowledged expert. Some of the first successful implementations of expert systems were of this type. Examples include programs to aid physicians diagnose certain diseases (e.g., MYCIN) and engineers uncover petroleum deposits (Waterman, 1986).

We have posited relationships between various performance and psychological outcomes of an HRM problem-solving exercise and both the complexity of the problem and the use of an expert system as a decision aid. The hypothesized main effects of the complexity factor were, for the most part, supported. However, hypotheses relating to the main effects associated with expert system use were not confirmed. The main effects for expert system use were not significant for any of the psychological outcomes nor for the accuracy outcomes. The main effect for the efficiency outcome, while significant, was opposite the predicted direction.

These findings, though somewhat divergent from our theoretical position, are not inconsistent with prior research on computer-based decision aids in other contexts. But unlike many of those studies, our work examines the interaction of expert system use and complexity, which is an important feature of the problem context. The interaction effects observed suggest a more complicated explanation, at least in the case of the performance outcomes. We have noted how these interaction effects are, in certain ways, consistent with H3.

There are some alternative interpretations of our findings that need to be addressed. First, it might be that the expert system developed for this study is defective, thus raising questions regarding the study’s internal validity. Yet the program clearly generated consistent results when used by expert personnel analysts. It had first gone through several revisions prior to implementation and had also been pretested by some departmental classifiers (who did not serve as experimental subjects). As noted above, this process is strongly suggestive of the program’s construct validity. Consequently, we can rule that out as an explanation for our findings.

A second issue concerns the departmental classifiers, who served as experimental subjects. Our results may be the consequence of the confounding impact of their prior training and experience with the paper-and-pencil approach. H1, for example, anticipated greater performance under the expert system than the paper-and-pencil condition. This was not found to be the case, so perhaps the subjects were just used to the paper-and-pencil approach and had not yet gained sufficient proficiency with the expert system.

We are inclined to reject such an explanation for a couple of reasons. The subjects did not find the paper-and-pencil approach easier to use than the expert system approach. Moreover, we conducted supplementary analysis in which we included a variable in the logit analysis indicating the classifying experience each subject had prior to the experiment. This varied considerably across subjects: nearly half of the subjects had previously conducted five or fewer classifications, although several classifiers had completed over thirty classifications. By including the number of prior classifications and the interaction of prior classifications with problem-solving method, we were able to control for this effect. That is, we would anticipate a significant experience x method interaction effect such that expert system and paper-and-pencil differences in accuracy would be more consistent with H1 for the less experienced classifiers (who were mostly near novices). The results of this analysis are reported in Table 4. This interaction effect was not found to be significant for either accuracy measure. Consequently, the experience explanation for our findings must be rejected.

We have discussed the possibility that the subjects may have utilized judgmental techniques not known by the personnel analysts and not incorporated into the expert system. Indeed, there are certain types of problems contexts in which somewhat informed novice problem solvers may outperform experts (Adelson, 1984). The reason for this is that an expert (and perhaps an expert system) may be burdened by excessive and extraneous information, thus not able to “see the forest for the trees.” As most expert systems in HRM are likely to be used by subjects like ours rather than total neophytes, research using subjects somewhere between experts and neophytes consequently seems quite appropriate.

If subjects were not required to accept the program’s recommendations and if they were using personal heuristic rules in solving the classification problems, then perhaps there ought to have been no difference between choices made with the expert system and the paper-and-pencil approach. Yet the method x complexity interaction was significant for the accuracy and efficiency measures. In fact, the subjects seemed wedded to the expert system’s recommendations when the program was used, only rarely disagreeing with the program. This aspect of subject behavior when utilizing an expert system warrants further study.

Our discussion of the findings also suggests the possibility that the results could be consistent with theoretical expectation if the skill level and occupational series were on opposite sides of some absolute complexity scale. We speculated that the results might suggest the skill level decisions are at the upper end of such a distribution and the occupational series decisions are at the lower end. If this were reversed, then our conjecture would not hold. However, based on discussions with analysts from the personnel department, we feel that our interpretation of the complexity dimension is reasonable. Analysts suggested that occupational series decisions are usually easier to make than skill level decisions. The reason for this is that much more information must be processed and assimilated for the latter than the former type of decision. Occupational series decisions generally rest on establishing whether the job incumbent performs certain very specific tasks and usually only a few pieces of information are required (see Appendix for a more extensive discussion of the classification task). For example, if the job incumbent does any more than a very limited amount of typing, the job will be classified as a secretary. If any dictation work is at all required, then the job will be classified as a stenographer. In contrast, skill level decisions require the acquisition and analysis of extensive amounts of information relating to all aspects of the jobs. Skill level decisions are more quantitative in character, comparing the skill levels of the various tasks performed by an incumbent in order to ascertain the preponderant skill level for the job as a whole. Occupational series decisions are more qualitative and also less critical (as salary depends on skill level alone) and presumably less stressful to make. It would, however, make sense in further research to utilize a design that differentiated between the complexity levels of each facet of the task at hand.

What policy implications might we draw from this study? For one thing, expert systems cannot be viewed as panaceas for managerial problems. We are certainly a long way from being able to create automatons which can replace humans as problem solvers in the HRM field. Managers may see expert systems as a means of economizing on labor costs, much as robotic systems are used in manufacturing. Expert system development costs may be substantial and the resulting product may not be sufficiently accurate to justify the investment. Yet it is clear that, under certain circumstances, an expert system can exceed, or at least equal, the accuracy of a conventional problem-solving approach. Thus, despite somewhat ambivalent results, this study does indicate that is feasible to develop expert systems that replicate some nontrivial problem-solving competencies in the HRM field.

Clearly, more research is needed in HRM, both on the impact of expert system use on outcomes and on the efficacy of various features of expert system applications (e.g., graphical versus textual displays). Future research should explore other dimensions of the problem context and the interaction of context and expert system use. Another significant area of inquiry is the impact of the personal characteristics of users on performance and psychological outcomes. The technology of expert system development also is a potentially fruitful area. There is now considerable experimentation with computer learning systems, so we might envision a generation of artificial intelligence applications that both apply heuristic rules and learn from the consequences of decisions. Finally, the experimental method used here might be enhanced by the use of verbal protocols, which could be used to assess the impact of different problem-solving techniques on the manner in which subjects approach and analyze a problem. For research just employing an expert system, it would be possible to build routines into the program that would capture the manner in which each subject worked through each problem (e.g., the number of steps used to reach a decision, the order in which elements of the program were accessed). Such data could be utilized to develop and analyze a typology of problem-solving styles (including analysis of how such styles vary as a function of, say, task complexity).

Acknowledgment: The authors acknowledge the helpful comments of the reviewers. In addition, suggestions relating to this study were provided by Harry Triandis, Jerry Ferris, and Joe Martocchio. Support for this project came, in part, from grants by IBM Corporation and the University of Illinois Campus Research Board.

APPENDIX

Description of Task and Expert System

This appendix provides a more detailed description of the classification task and the problem-solving methods used by the experimental subjects.

Paper-and-Pencil Approach

As described in the body of the paper, the personnel department of the organization studied had recruited a number of administrative staff to serve as “departmental classifiers,” who were given the authority to make job classification decisions for certain clerical job categories. Classifiers undergo periodic training in which they are given examples of job classification problems and shown strategies for making those decisions. They also have documentation to which they can refer in order to aid in the classification task. There are eight principal task categories that are analyzed in the process of completing a classification problem: document production, written communication, oral communication, scheduling/coordination/liaison work, filing and records management, calculations and fiscal record keeping, document processing, and supervision of others. These general categories are defined in the guidebooks given to the departmental classifiers. In addition, each general task is divided into several illustrative subtasks. For example, document production has such subgroup categories as transcription, dictation, text entry, proofreading, copying, and so forth. Such subtasks might be further subdivided into very specific activities. In the case of text entry, the employee may have to follow detailed instructions regarding formatting of the document, may have considerable discretion choosing from among standard formats, or may be able to devise his or her own formats for the document. These different approaches subtasks are of varying (increasing) levels of responsibility and, therefore, skill level.

The departmental classifier must, based on a detailed written job description, ascertain the skill level and occupational series for the job in question. The strategy is to compare the tasks listed in the job description to sample activities in each of the major categories. Example tasks are associated with skill level designations. The job description also contains information on the proportion of work time the employee typically spends on each major work activity. Skill level determination involves ascertaining the level in which the preponderance of job activities fall. Thus, it is necessary to have a comprehensive listing of activities pursued in connection with the job. Occupational series decisions require discerning the extent to which certain key tasks are pursued, which differentiate, for example, clerks from secretaries. If the classifier is unsure of the appropriate job classification, or if the occupational series or skill level is outside the limits within which classifiers are allowed to make classification decisions, then the job is referred to a personnel analyst.

Expert System Approach

The expert system used in this study was designed to simplify the process by which classifiers assemble and evaluate job information. The paper-and-pencil approach is complicated because there are more than a hundred different subtasks that a classifier might have to consider in order to make a proper job classification decision. The program was written in an artificial intelligence program called PROLOG and runs on DOS-based personal computers. It has two principal sections. The first part of the program helps the classifier develop a detailed job description that can then be analyzed by the system’s inference system.

In the job description section of the program, the user is first presented with a menu listing the eight major task categories described above. Having chosen a category, the user is then presented with a tree-like diagram listing the major subtasks under that major task. Figure A.1 shows such a screen for the document production section. The user moves the cursor to different boxes and can display information associated with the boxes (including a detailed description of the task). When an item is chosen as a task identical or similar to one performed by the job incumbent, questions are asked regarding the amount of time the incumbent spends on the job (additional questions help the user frame the time question in terms of the incumbent’s typical workday, workweek, or other time period). The program indicates the skill level associated with the job and maintains a running tally of the tasks performed and the amount of time devoted to each task.

The job description section of the program largely mechanizes the information gathering process. However, unlike the paper-and-pencil approach, the expert system provides position classification recommendations. The second major part of the program does this. Once the job description phase is completed, the user requests a classification recommendation. Figure A.2 illustrates the screen displays provided in this section of the program. Note that both skill level decision and occupational series recommendations are given. The user is also provided an explanation of the decision and a breakdown of job activities related to each decision.

Users are free to return to the job description section and modify their responses. In addition to making recommendations, the program also indicates when a decision is not possible (because of insufficient information or because the job classification is outside of the program’s domain) and then refers the user to a personnel analyst. Finally, the program contains a context-sensitive help system.

Notes

1. The usefulness of an application refers to its instrumentality as a problem-solving method, while ease-of-use relates to the extent to which a user felt comfortable working with the application. Ease-of-use and usefulness are not unrelated constructs, as Davis et al. (1989) posit that increased perceived ease-of-use leads to increased perceived usefulness.

2. Decision aids, of course, consist of a range of techniques, including many that are not necessarily computer based (e.g., diagrams, check lists). Although there is an extensive literature dealing with decision aids that are not computer based, research dealing with computer-based decision aids is more relevant to the question at hand.

3. Since efficiency is measured by the time required to complete the problem-solving task, the relationships in Figure 1 would be inverted for that variable.

4. See Appendix B of Gardner’s (1990) paper.

5. See Appendix A of Gardner’s (1990) paper.

6. A t-test is used to assess the significance of individual parameters. The overall significance of the equation and significance tests for groups of variables are based on the logarithm of the likelihood ratio and are analogous to F-ratio tests in analysis of variance and regression.

7. Both conventional and conditional logit (Chamberlain, 1984) estimates were obtained in the initial analysis. However, the two sets of parameter estimates were identical.

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