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

Journal of Management, Spring, 1996 by John J. Lawler, Robin Elliot

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).