مدلسازی رفتار انتخاب برنامه بهداشت مصرف کننده برای بهبود ارزش مشتری و سهم بازار برنامه بهداشت

The ability of health-care providers and insurers to survive in today's highly competitive market requires that they thoroughly understand marketplace needs and use that information to deliver true customer value. The objective of the present study is to illustrate how choice-based conjoint analysis can be used to create health plans that optimize value for consumers and market share for managed care organizations. The use of choice-based conjoint analysis takes the guesswork out of health plan design and promotion. By offering insight into consumer reactions to the range of plan feature choices, the research program presented in this article can help to increase consumer satisfaction while aiding health plans to reach their objectives as well. The elimination of crises and ad hoc decision making raises the effectiveness and efficiency of managed care programs. The ability of health-care providers to survive in today's highly competitive market requires that they thoroughly understand the needs of consumers and then deliver true customer value; that is, managers must effectively use their resources to maximize the perceived value of their product offerings to target customers. Historically, health plans or health insurance programs were designed on the basis of the preconceived notions of medical professionals (Ellsbury and Montano, 1990). Satisfaction delivered by such plans was, therefore, a hit-or-miss proposition. Today, decision support systems play an increasingly important role in management's design of health plans for various consumer groups (Forgionne, 1991). The present study supplements prior research by illustrating how choice-based conjoint analysis can be used by managers to create health plans that optimize value for the consumer and market share for the organization offering them.

During the last several decades, numerous developments have produced enormous changes in the health-care industry. First, health care is one of the fastest growing sectors in the service industry. As Folland, Goodman, and Stano (1993) point out, approximately 12.5% of all dollars spent on final goods and services are spent on health care. This is a significant increase from the estimated 8% spent on health care in 1976. Second, a more relaxed regulatory environment combined with oversupply (Steiber, 1987) have led to sharply increased competitive pressures for health-care providers (Nelson and Goldstein, 1989). Competitive dynamism is not only evident in the increased number of physicians and hospitals, but also in the emergence of such alternative delivery systems as HMOs and walk-in clinics. Third, consumers of health-care services have become more informed and sophisticated buyers of these services Andaleeb 1994 and Nelson and Goldstein 1989. In addition, customer expectations have grown as their knowledge regarding health-care services has expanded (Oliver, 1980). Thus, for a health-care coverage organization to take a pro-active stance in today's dynamic environment, managers must have knowledge regarding consumer reactions to alternative choices (Zeithaml and Zeithaml, 1984). Furthermore, research shows that managers of health-care organizations must continue to monitor customer perceptions over time as the environment changes (Gilbert, Lumpkin, and Dant, 1992). If the environment is neglected, losses of market share and profitability will follow.

The attributes and their associated levels are shown in Table 1. There are 11 attributes identified by means of the process described in the Methodology section. The qualitative exercises used included having participants name the most important plan features or attributes. The focus group tapes were carefully reviewed, and the items mentioned, their frequency of mention, and the discussion of each item were carefully noted. In addition, after completing the open-ended exercise, participants were asked to rate the importance of each of the items mentioned in the discussion on a 10-point scale where 10 was defined as “very important” and 1 was defined as “very unimportant.” The 11 attributes listed in Table 1 emerged as the key drivers of consumer choice among health-care coverage alternatives on the basis of the qualitative and the more structured exercises previously described. In addition, the list was further refined on the basis of the attribute ratings obtained from the telephone survey. Although we used the attribute list developed by Chakraborty, Ettenson, and Gaeth (1994) as a point of departure, our list differs markedly from theirs. First, a number of attributes were eliminated. For example, the attributes waiting time in physician's office and office hours for physicians were dropped, because in an IPA environment, these items are outside the direct control of the health plan. (IPA stands for independent practice association. An IPA is made up of independent physicians that contract with the HMO to provide care to its members. Members of the IPA may contract with other HMOs' managed care plans. Members of the IPA are not employees of the HMO and operate from their own offices.) The attributes travel time to physician and travel time to hospital were dropped based on analysis of distances and travel times for various physician network scenarios. This analysis indicated that the various network configurations for physicians and hospitals would have no significant effect on travel times in comparison to those found in a no network (go to any physician or hospital) situation. The attribute wellness and education programs was dropped because of its very low importance in Chakraborty and colleagues and in the focus groups and telephone interviews we conducted. The same was true for communication with plan participants. Coverage for hospitalization was dropped, not because it is unimportant, but because it is not a point of difference between plans. All health-care coverage programs in the market, indemnity and managed care, provide high levels of coverage for hospitalization.