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Publication Number: FHWA-RD-96-143
Date: April 1997

Development of Human Factors Guidelines for Advanced Traveler Information Systems and Commercial Vehicle Operations: Definition and Prioritization of Research Studies

 

APPENDIX A: SUMMARY OF RELEVANT LITERATURE

REASONS FOR RESISTING NEW TECHNOLOGY

TECHNIQUES FOR RESISTING ATIS/CVO TECHNOLOGY

ESTIMATE OF THE PERCENTAGE OF DRIVERS LIKELY TO ADOPT ATIS/CVO

ESTIMATE OF THE PERCENTAGE OF DRIVERS LIKELY TO FOLLOW ATIS/CVO RECOMMENDATIONS

CONDITIONS THAT MAY AFFECT ACCEPTANCE OR REJECTION OF ATIS/CVO ADVICE

POTENTIAL TECHNIQUES FOR PROMOTING THE ACCEPTANCE AND USE OF ATIS/CVO

RELATIONSHIP OF ATTITUDES TO BEHAVIOR: THEORY AND RESEARCH

 

REASONS FOR RESISTING NEW TECHNOLOGY

The content of appendix A represents an attempt to explicitly address the six specific items raised in the Statement of Work. In the first section of appendix A, the literature on technology acceptance, product diffusion, and marketing research was reviewed. Key factors were extracted that were considered relevant to technology resistance. The factors identified in the literature survey are listed in the first column of table 60. An interpretation of how this factor might be expected to affect resistance to ATIS and CVO applications of ITS technology is included in subsequent columns of the table.

The following definitions are given to support the content of table 60. The table is divided into three primary categories of factors: product characteristics, consumer characteristics, and organizational characteristics. Both ATIS and CVO issues are discussed for the first two categories, but for the Organizational Characteristics, only CVO applications are discussed.

Product Characteristic Definitions

Rogers (1971, 1983), Tornatzky and Klein (1982), Feldman and Armstrong (1975), Ram (1989), Holak (1988), and Holak and Lehmann (1990) have proposed 11 product characteristics that influence the acceptance of innovative new products. Rouse and Morris's (1986) assessment of user acceptance of computers in industry suggests that acceptance depends on the perception of the affect on job performance, ease of use, user discretion, and the perception of organizational and peer group attitudes towards automation. Table 60 lists the product characteristics and their expected influence on adoption or resistance of ATIS/CVO technologies.

  • Compatibility: The consistency of an adopter's values or norms and consistency with an adopter's daily activities.
  • Communicability: The ease of perceiving and expressing the product benefits to others.
  • Complexity: The difficulty of understanding and using the new product.
  • Cost: The price of the product.
  • Discretion: The opportunity to exercise skills, judgment, and creativity while using the product. The functional level at which the product operates determines the amount of discretion allowed by users.
  • Divisibility: The ability to try a product without a large initial investment.
  • Observability: The visibility to others of the results of using the innovation.
  • Perceived risk: The product performance or psychosocial risks attributed to the product.
  • Profitability: The level of profit to be gained by adoption of the innovation.
  • Relative advantage: The perceived superiority of the product over those preceding it.
  • Trialability: The ability to experiment with the innovation on a limited basis.

Consumer Characteristic Definitions

Studies on consumer characteristics have been less definitive than those on product characteristics (e.g., Wilton & Pessemier, 1981; Leonard–Barton, 1985; Hill, Smith, & Mann, 1986). Table 60 also includes a list of the consumer characteristics and their expected influence on adoption and rejection of ATIS/CVO technology.

  • Self–efficacy: The perceived ability of oneself to use a product successfully.
  • Product knowledge: Knowledge about the product or similar products.
  • Product class interest: Inherent interest in the product category.

Table 60 also lists the organizational and work environment characteristics that may influence acceptance of CVO. These factors were taken from studies on the acceptance of automation, computers, and information system in factories and offices (e.g., Nelson, 1990; Buchanan & Boddy, 1983; Wall, Corbett, Clegg, Jackson, & Martin, 1990).

Table 60. Factors affecting resistance to ATIS/CVO technology.

product characteristics atis cvo
COMPATIBILITY: Compatibility negatively affects resistance. It is expected that an ATIS system will not be compatible with anything drivers currently use in their cars. The system may be compatible with some drivers use of other public access information systems and personal computer products. Those drivers who currently use computer related products will be less resistant to ATIS. Compatibility for CVO systems may depend on the technological climate within the company. Couriers, such as Federal Express and UPS, and police departments are examples of organizations that utilize the latest in–vehicle technology. These types of companies would be less likely to resist a CVO system.
COMMUNICABILITY: Communicability negatively affects resistance. Drivers should be able to see a benefit from using the ATIS system. If traffic and route information is a primary function of ATIS, the commuting time for the driver with the system should be less than drivers without the system. To the extent that this type of benefit is apparent to the system owner and can be communicated to others, resistance should decrease. To the extent that a CVO system improves job performance and the driver can attribute this improvement to the system, resistance will decrease.
COMPLEXITY: Complexity positively affects resistance. ATIS systems which are difficult to use will increase resistance. CVO systems which are difficult to use will increase resistance.
COST: Cost positively affects resistance. Higher cost systems will increase resistance. Higher cost systems will increase resistance.
DISCRETION: The affect of discretion is dependent on the type of functions performed by the system. A system that performs those tasks over which the driver prefers control will meet with greater resistance than a system that performs less desirable tasks. A system that performs those tasks over which the driver prefers control will meet with greater resistance than a system that performs less desirable tasks.

Table 60. Factors affecting resistance to ATIS/CVO technology (continued).

product characteristics atis cvo
DIVISIBILITY: Divisibility negatively affects resistance. A system that is available to new users at a low cost will reduce resistance. For example, the system could be introduced in stages. The first release could be low cost with limited function. Future releases could increase cost and function. Alternatively, a range of systems could be made available with upgrades as an option. The same concepts will apply to CVO.
OBSERVABILITY: Observability negatively affects resistance. The more easily the benefits can be seen by other drivers, the less likely they are to resist. If resistant companies can see that their competitors are benefiting from CVO use, they will adopt the innovation more quickly.
PERCEIVED RISK: Perceived risk positively affects resistance. An ATIS system whose use increases the probability of automobile accidents, forces a change in driving habits, or appears to be the latest technological fad (a potential loss of investment) will increase resistance. Resistance will be greater if drivers believe that CVO systems will adversely affect their jobs or eliminate them altogether. Resistance also will increase if drivers believe the systems will be used to monitor their activities.
PROFITABILITY: Profitability negatively affects resistance. Drivers will be less likely to resist if ATIS systems decrease the amount of time spent in traffic jams, decrease money spent on gas, decrease time spent navigating in unfamiliar areas. Companies whose profitability increases because of CVO use will adopt the innovation more quickly. Drivers are less likely to be affected by profitability.
RELATIVE ADVANTAGE: Relative advantage negatively affects resistance. The biggest hurdle to overcome will be the perception that, for the most part, ITS only offers traffic information and route planning. Drivers realize that in day–to–day driving, traffic information can be obtained pre–departure by watching the local morning news. After departure traffic information is available on the radio. The value of route planning in the local area is very small. Drivers already know local alternative routes. One way to change the opinion of these drivers will be to inform them of other ITS features that will benefit them directly. Providing the same information that is available through other sources (e.g., maps, billboards, radio traffic reports) will not be enough incentive for drivers to adopt ATIS. The system must offer substantial advantages over current methods of obtaining information to overcome resistance.
SELF EFFICACY: Self efficacy negatively affects resistance. Drivers who believe they are able to use the system will be more likely to adopt the system. People who have had trouble driving or using other information systems, personal computers, etc., will be more resistant. The same concepts will apply to CVO.
LOCATION Drivers in metropolitan areas are expected to be less resistant to ATIS systems. Drivers in rural areas probably do not need ATIS for most of their driving. Approximately 54% of the passengers vehicles in the U.S. are registered in the 50 largest metropolitan areas. As with ATIS, commercial drivers in urban areas probably will need CVO more than drivers in rural areas. In addition, interstate transporters would need CVO.

Table 60. Factors affecting resistance to ATIS/CVO technology (continued).

consumer characteristics atis cvo
PRODUCT KNOWLEDGE Drivers who are more knowledgeable about ATIS systems are more likely to adopt the technology (assuming that the product is worthwhile and more knowledge means more knowledge about product benefits). However, negative information will be more influential than positive information. In addition to knowledge of system function and benefits, commercial drivers will resist less if they understand why CVO is being used by their company.
SOURCE OF PRODUCT KNOWLEDGE: Source of knowledge interacts with other consumer characteristics. Drivers who are less knowledgeable about ATIS will be influenced by "experts". These experts may be trade journalists, celebrities, or friends. Additional sources of information are managers, coworkers, and drivers from other companies.
EDUCATION Drivers with more education will adopt ATIS more quickly. The same concepts will apply to CVO. Education level may be interact with organizational level. That is, resistance may be greatest at the lower levels of an organization where educational levels also are lowest.
INCOME Drivers with higher incomes will adopt ATIS more quickly. Personal income should not be a factor in resistance to CVO systems. Company profitability or cash flow status may show a relationship with resistance similar to income.
AGE Older drivers will be more likely to resist ATIS. This resistance will be due to a number of factors: decaying cognitive and perceptual abilities, restricted driving range and frequency, greater knowledge of local driving environment, flexibility to avoid driving during rush hours. Initial resistance to CVO is expected to be higher among older drivers.
PRODUCT CLASS INTEREST Interest in computers, information systems, automobiles, and electronics will decrease driver resistance. The same concepts will apply to CVO.
organizational characteristics cvo
WORK GROUP MORALE Higher group morale should decrease

resistance to CVO.

SUPPORT OF MANAGEMENT/

CONFIDENCE IN MANAGEMENT

Drivers who support management objectives (e.g., company wide technological change) will be less resistant to CVO. Companies with a history of failed innovation adoption will be more resistant to CVO.
JOB EXPERIENCE More experienced drivers will be more resistant towards CVO. However, they will be more productive than newer drivers once they adopt the system.

Table 60. Factors affecting resistance to ATIS/CVO technology (continued).

organizational characteristics cvo
DECISION–MAKING PRACTICES Greater participation in decision–making by all organizational levels, with regard to the use of CVO within the company and use of the information on the job, will decrease resistance.
JOB PERFORMANCE The better drivers in a company will be more resistant to CVO because they have achieved their level of performance without the system. The new technology may require adjusting their behaviors and threaten their status as top employees. Those drivers with minimally acceptable job performance may adopt CVO more quickly as a means of improving performance.
JOB CLASS Low skill workers will be resistant to CVO if they perceive it as a threat to their job security. High skill workers will be less resistant and will see it as a means of enriching their jobs.
ORGANIZATIONAL CONTROL Monitoring of driver location and activities through CVO will increase resistance.
TRAINING AND SUPPORT The availability of driver training will reduce resistance. Introduction of CVO without training will increase resistance.

 

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TECHNIQUES FOR RESISTING ATIS/CVO TECHNOLOGY

The following table lists some of the possible techniques that may be used by drivers to resist ITS in–vehicle technology in ATIS and CVO applications. These techniques were derived from the literature on resistance to automation in factories and computers in offices (e.g., Shaiken, 1985). The projected behaviors are simply interpretations of how those factors might apply to the ATIS and CVO environments.

Table 61. Possible techniques to resist ATIS/CVO technology.

atis
SYSTEM PURCHASE Drivers may choose not to purchase the system.
SYSTEM USE: Refusal to initiate use. Drivers may purchase a system but choose not to use it.
SYSTEM USE: Decision not to continue use. Drivers may purchase a system, use it for a while, and decide that they no longer need or want to use it.
NEGATIVE OPINION: Word–of–mouth. Some people (they need not be drivers who have experience with the system) may say to friends, coworkers, and family that the system is worthless or even dangerous.
NEGATIVE OPINION: Organized. Groups of consumers could unite in support of common concerns about ATIS (e.g., safety, government control, anti–technology). Organized resistance could take the form of boycotts, law suits, or advertising campaigns.

Table 61. Possible techniques to resist ATIS/CVO technology (continued).

cvo
SYSTEM PURCHASE Refuse to purchase a CVO system for any of the following reasons: the technology is a fad or gimmick, first generation systems are always prone to errors, the system is not cost effective and needs more study, first generation systems are too expensive and the costs will fall over time.
JOB PERFORMANCE: Intentional mistakes. Drivers may purposely make mistakes when entering information into the system in an attempt to diminish the utility of the system.
JOB PERFORMANCE: Intentional suboptimal performance. If drivers perceive the system as a means of management measuring job performance, they may intentionally perform below their capability. Possible means of resistance include: ignoring system information, "forgetting" passwords or procedures, working more efficiently without the system than with it, blame the system for poor performance.
JOB PERFORMANCE: Resistance to training. Drivers may participate in training at a minimum level–attendance but not involvement.
SYSTEM IMPAIRMENT Drivers may find ways to disable the system permanently or temporarily(i.e., on demand).
MAINTENANCE Impair, rather than repair, systems brought in for maintenance.
JOB TURNOVER: Intraorganizational. Drivers may find other jobs within the company in order to avoid use of CVO. They would be expected to communicate their negative feelings about the system to their new coworkers.
JOB TURNOVER: Interorganizational. Drivers may find jobs with other companies (who have not adopted CVO). They would be expected to communicate their negative feelings about the system to their new coworkers.
ORGANIZED RESISTANCE Unions may represent drivers with grievances about changes in work conditions as a result of CVO.

 

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ESTIMATE OF THE PERCENTAGE OF DRIVERS LIKELY TO ADOPT ATIS/CVO

Figure 82 shows the cumulative percentage of U.S. households with certain consumer products, plotted as a function of time (years) since the product was introduced. The diffusion of these products follows the traditional S-shape curve associated with successful innovative products. Cellular telephones are possibly the most relevant analogy that can be made with this data to ATIS/CVO. We suggest that the cellular telephone analogy may be more appropriate as a model for ATIS/CVO than the ATM example given previously in the body of this report. As of 1991, cellular subscribers accounted for only 2.5 percent of the U.S. population. Industry experts project market penetration to increase to 15 percent of the population (approximately 45 percent of the households) by the year 2000.

Caveats For Interpreting Figure 82

1) The data represent the adoption and diffusion of successful products. Unsuccessful products would be characterized by shorter duration (fewer years) on the abscissa and very low slope (negligible increase in sales).

2) There may be a maximum adoption level for ATIS equivalent to the percentage of cars in larger metropolitan areas. For 1992, approximately 54 percent of all passenger vehicles (this does not include trucks and buses) were registered in the 50 largest metropolitan statistical areas. That is, the total addressable market for ATIS might be better characterized by the 54 percent figure than by the total number of registered cars in the U.S.

Sources of Data

1) 1992 Statistical Abstract of the United States

2) 1992 Rand McNally Commercial Atlas and Marketing Guide

3) 1991 Consumer Electronics Review

4) Several papers included in the bibliography and reference sections

Diffusion curves for four consumer products

 

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ESTIMATE OF THE PERCENTAGE OF DRIVERS LIKELY TO FOLLOW ATIS/CVO RECOMMENDATIONS

The percentage of drivers accepting or rejecting advice from ATIS or CVO is difficult to estimate. As described in the fifth section, there are a number of variables that will influence the acceptance of ATIS/CVO system recommendations. These variables may act independently or in combination with other variables. In the body of the report a gross estimate was developed of the total proportion of drivers who might follow the recommendations of an in–vehicle system by the year 2020. That estimate, 20 to 35 percent, was based on the compound probability of technology acceptance (40 percent based on analogy with ATM's) and the range of compliance estimates, 50 to 90 percent, based on survey and simulation studies. An estimate based on cellular telephone diffusion data would lead to similar conclusions since, approximately 45 percent of the households in the U.S. are projected to have a cellular phone by the year 2000. These estimates are more appropriate to ATIS than to CVO systems.

The compound probability of following recommendations can be expected be higher in the CVO environment. Driver compliance will be high, possibly over 90 percent, because use of the equipment will be integral to job performance and included in employee training programs. A higher proportion of commercial vehicles can be expected to be outfitted with ITS in–vehicle systems because it may prove to be cost–effective. We have no basis for projecting the proportion of commercial vehicles that will be equipped with CVO systems, therefore a compound probability cannot be calculated. Beyond predicting that the probability of compliance will be greater in CVO than in ATIS, quantitative estimates must await further data collection.

More accurate projections may be possible after results are available from the research in the remainder of this task. An in–depth marketing analysis is advisable to complement user–interface evaluations. The prediction of technology acceptance or compliance which is based on experimentation with individuals or small groups (the Experimental Social Psychology model) is not recommended (see last section of this appendix). Human factors engineering studies are important for the design of good user–interfaces, but similar methodological approaches for predicting technology compliance are not recommended because, as stated in the last section of this appendix, attitudes are not predictive of behavior.

 

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CONDITIONS THAT MAY AFFECT ACCEPTANCE OR REJECTION OF ATIS/CVO ADVICE

The information in the following table is based on information developed from sources listed in the Bibliography and extrapolated in small group discussion.

Table 62. Information developed from bibliography and small–group discussion.

factor description
URGENCY If the driver perceives him or herself to be under pressure to arrive at an unfamiliar destination as quickly as possible, then the acceptance of an alternative route will be increased. (An example would be taking someone to a hospital emergency room.)
FAMILIARITY A lack of familiarity with the area will increase acceptance of route recommendations. For example, rental cars can be expected to have a relatively high rate of usage and acceptance.
POTENTIAL TIME SAVINGS Increases in the potential time savings will increase the acceptance of route recommendations.
EXPECTED DURATION OF CONGESTION The longer the expected duration of the congestion, the greater the chance that a user will comply with a suggested alternative route. For example, if the delay is caused by a hazardous material spill that will close the road for several hours, drivers will be more likely to search for alternatives than if the delay is due to a crash and there is a reasonable expectation of getting past the bottleneck in a short period of time.
EXPECTATION OF CONGESTION ON ALTERNATIVE ROUTES If the driver expects that conditions on alternative routes are just as bad as on the primary route, the probability of accepting an alternative is low. An alternative route may not be accepted during most normal rush hours if drivers know the surface streets are jammed too.
RAPID ACCESS TO ROUTE PLANNING Unless it is easy to get the information, pre-route planning features for day to day commuting won't be widely used. For longer trips, or trips out of the normal (e.g., when going to a work site that is not your normal one) use of this feature will increase.
CONGESTION HISTORY If the road has a history of occasional severe backups, drivers are more likely to want traffic information on a regular basis. However, this type of driver may have alternative routes pre- selected.
PRIMA FACIA REASONABLENESS The suggested alternative route must look reasonable for a driver to accept it. Apparent backtracking and frequent turns, for example, would make a route appear unreasonable.
USER-INTERFACE FEATURE AND SENSORY MODALITY The recommended route must be in a format that is readily interpreted in a driving (or a pre-driving) context. For example, arrows (e.g., left,right, continue same direction) could be presented visually. In some circumstances drivers might like a voice system ("Left turn, in 2 blocks"). The system should not divert the driver's attention from driving.
CURRENCY OF INFORMATION The maps in the ITS system must be current. Ease of updating the maps and the cost of updates will affect acceptance.
PREDEFINED DESTINATIONS Frequent destinations should be pre-entered (like phone numbers in an autodialer) so that a check on traffic conditions can be made pre-departure.
AUTOMATED AID TO LAW ENFORCEMENT If ATIS/CVO is perceived to have the potential for law enforcement "abuse", fewer people are likely to buy the system. For instance, if it is believed that speeding tickets could be issued based on the data collected by the system (regardless of whether or not this actually occurs), acceptance will be reduced.
PROBABILITY OF THEFT If the system is an attractive target for thieves, acceptance will be reduced.
STABILITY OF RECOMMENDATIONS The system must remain fairly stable. Fluctuations in recommended routes should be minimal. Transient traffic conditions should be ignored. (The size of the delay to ignore should be user defined.)
KNOWLEDGE OF SCHEDULED EVENTS The system needs to have knowledge of scheduled events that will affect traffic. Examples include sporting events, extraordinarily busy times at airports, parades, and political inaugurations.

Table 62. Information developed from bibliography and small–group discussion (continued).

factor description
ERROR RECOVERY AIDING The system needs to be helpful in error recovery. It should take current position into account and offer a return to the original route or an entirely new route. Also, the system must allow changes in destination to be entered at any time. Rigid and unforgiving systems will reduce acceptance.
RURAL OR OFF-ROAD USE For vehicles that may become lost in areas where there are not major roads (e.g., rural delivery vehicles) some means (such as GPS) of finding the vehicle's current location in absolute terms will be a desirable feature.
PAST EXPERIENCE

WITH THE SYSTEM

Getting directed to go the wrong way down one way streets, failure to account for road closures, or wrong information given a very few times will deter system use. These bad experiences will be more damaging to acceptance than a large number of positive experiences would be toward improving acceptance.
TIMELY DATA The traffic information used to support system route recommendations must be timely (on the order of 5 minutes).
MAGNITUDE OF RECOMMENDED ROUTE CHANGE If the route change is a simple detour, as opposed to a new route, the driver will be more likely to accept the recommendation.
PERSONAL SAFETY/CAR-JACKING There are some neighborhoods that some drivers will not enter no matter how much time might be saved. This is a problem for rental car drivers who are not aware of these neighborhoods. System users will not be pleased with such reroutings.
RENTAL CAR DIRECTIONS The system should direct the driver to and from the hotel, and to and from the rental car return. The driver may not know the address of either location.
RENTAL CAR FUELING Since most rental cars need to be refueled during use and before being returned, the driver should be able to get directions to or locations of gas stations. He/she might want to select a particular brand of station, or stations that sell diesel fuel, or stations within 10 min or 16.10 km, or some combination of logical conditions.
HIGHWAY TOLLS In unfamiliar areas, the driver should be warned about toll booths. The cost also needs to be mentioned. If there are automatic toll collection or exact change only lanes, the driver needs to know which ones they are.
CVO RESTRICTIONS The system must take roadway restrictions (e.g., maximum height, maximum weight, local delivery only, hazardous material restrictions) into account.
CVO ROUTING RECOMMENDATION If accepting the recommended route is company policy, and there are sanctions for failure to comply, then acceptance will increase.
CVO WEATHER INFORMATION Winter storm warnings, tornado warnings and the like affect route selection. However, if the detour is very long relative to the length of time likely to be saved, then the drive may simply be postponed instead.
CVO CONSIDERATION OF ALTERNATIVE ROUTES Consider the situation of a delivery route driver. Intelligent alternative routings will be more readily accepted than if the system blindly reroutes the drivers to the original stop.
CVO URGENCY Anything that increases the driver's sense of urgency will increase the acceptance of alternative routes. Perishable cargo and delivery incentives (bonus for timeliness) are two factors that could affect perceived urgency for CVO that are not applicable to private vehicles.

 

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POTENTIAL TECHNIQUES FOR PROMOTING THE ACCEPTANCE AND USE OF ATIS/CVO

The information in this table was generated from small-group discussion after reviewing sources listed in the bibliography.

Table 63. Information generated from small-group discussion.

FACTOR DESCRIPTION
PROMOTION: Government. Federal, State, and local governments could promote ATIS/CVO as progress toward economic, efficient, safe automobile travel.
PROMOTION:

Consumer groups.

Drivers must be convinced that ATIS/CVO is safe and is not a threat to privacy or mobility. Consumer advocacy organizations should endorse ATIS/CVO because of the safety and economic benefits.
PROMOTION:

Auto industry.

Automobile manufacturers have traditionally resisted adding standard features or options to their cars (e.g., airbags and seatbelts). The automobile industry needs to be convinced of the utility of ATIS/CVO and actively promote the systems.
PROMOTION: Unions. Union leaders should be convinced of CVO's utility or necessity prior to introduction of the system. Union acceptance of CVO technology is important for its viability.
PROMOTION:

Opinion leaders.

For certain subgroups, such as car or computer enthusiasts, acceptance of ATIS/CVO may be influenced by editorials or product reviews in trade periodicals.

Marketing to the computer enthusiast will probably emphasize the "high tech" aspect of the system. Building on what the computer enthusiast already knows or owns will encourage purchase and use of such a system. Perhaps an add–on to a notebook computer (much like the current TV reception add–ons) would be one way to distribute the system.

Marketing the system to car enthusiasts could include racing and rally competition.

Many car enthusiasts will want to add the system as an after–market item (like radios and CD players). The after–market systems should be available from the same sources as are radios and other automotive accessories.

PROMOTION:

Other industry

Electronics and software/hardware systems manufacturers can promote ITS technologies through the usual advertising and promotion methods.
TRAINING: Learning to use the system. Government and industry should support training facilities and classes to teach drivers how to use ATIS/CVO. Older drivers could be offered the option of training while training might be required for newly licensed drivers.
TRAINING: Media. In–vehicle training packages could include embedded multi–media – sound and visual, with interactive examples on the system itself.
TRAINING: Job performance. For some companies (e.g., taxis, delivery trucks), CVO maps and route information may be used as a training device to teach new drivers.
STAGED INTRODUCTION: Function availability. The availability of system function might be staged with low function–low cost systems at first and higher function–higher cost systems to follow. Early introduction of feature-rich systems may overload drivers and accentuate problems.
STAGED INTRODUCTION: Geographic availability. Much as the TravTek system is being introduced in Orlando, ATIS/CVO might be made available in only a few States or cities initially. Consumer products are commonly tested in "bellwether" States before being made available nationally.
STAGED INTRODUCTION: CVO versus ATIS. Acceptance may be increased by introducing CVO prior to ATIS. Training on the CVO systems and evidence of CVO utility might cause commercial drivers to promote CVO/ATIS among other drivers.
SYSTEM MODIFICATION One advantage of a staged introduction is the opportunity to modify the system. Early feedback from drivers should be useful in guiding system development.

Table 63. Information generated from small–group discussion (continued).

FACTOR DESCRIPTION
SOCIETAL/ENVIRON–MENTAL BENEFITS A possible benefit of ATIS is the substitution of in–vehicle advertising for billboards. Another benefit of buying and using the system is reduced traffic congestion and consequent reductions in air pollution, reduced need to build roadways, etc. This may influence these people to buy a system even though its benefit to them is less than the cost.
SAFETY Features that have a safety implication may influence some drivers to purchase a system. One example is emergency assistance facilitated by providing the location of the vehicle to the proper authorities. Summoning a tow truck, or the police, might be useful and attractive features.
FORCED ADOPTION Drivers could be forced to purchase a system by legislative action, such as current seat–belt requirements, making the installation of an ATIS/CVO system mandatory in order to license a vehicle another possibility. Probably the least painful way to mandate the use of ATIS is to require the systems in new vehicles. With normal attrition, the majority of cars would be equipped with systems in the lifetime of production automobiles.

Some drivers will resist forced adoption as another intrusion by the government into their private life. This group will feel that the information on, or in, the system can be used against them. For example, the system will have information sufficient to determine driving speeds. It may also allow the government to be able to track the position of their autos. Note that the issue of location tracking will be advertised as positive feature to commercial fleet managers, but perceived as a negative by the drivers.

 

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RELATIONSHIP OF ATTITUDES TO BEHAVIOR: THEORY AND RESEARCH

History of Attitude/Behavior Research

The "attitude" construct received its first serious attention from Darwin in 1872. Darwin defined attitude as a motor concept, or the physical expression of an emotion. For early psychologists, "attitude" was an emotion or thought with a motoric (behavioral) component. In some cases, the motoric component was subvocal speech; in other cases, gross behavior, such as postural change, was of interest. Beginning in the 1930's, psychologists began to argue actively about what components should comprise the attitude concept. Although there was agreement that all attitudes contain an evaluative component, theorists disagreed about whether beliefs (cognitions) and behaviors should be included as part of the attitude concept. The prevailing view among cognitive social psychologists was that "attitude" has both affective and belief components and that attitudes and behavior should be consistent; i.e., people with positive attitudes should behave positively toward the attitude object.

LaPiere (1934) reported that hotel managers' attitudes toward Chinese guests did not predict their responses to a Chinese couple who asked for a room. LaPiere's work was criticized on numerous grounds (e. g., the person who filled out the questionnaire may not have been the same person who later admitted the Chinese to the hotel), but many other researchers reported similar findings: attitudes did not predict behavior even when measured under optimal conditions, (Wicker, 1969).

In 1975, Fishbein and Ajzen published Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, laying out the theory of reasoned action which they claimed would improve our ability to predict behavior. In published reports, the variables specified by the theory generally did account for more of the variance in behavior than had previous attitude/behavior measures. However, it soon became clear that some important limitations on the theory's domain were required, that additional variables would need to be included, and that the theory was perhaps better understood as a taxonomy, as opposed to an explanatory system. Ajzen (1987) has published an updated version of the theory of reasoned action called the theory of planned behavior. Although the theory of planned behavior has undergone relatively few empirical tests, it seems unlikely that it will fare significantly better than Fishbein and Ajzen's earlier work. Although Fishbein's model remains popular with some market researchers, the prevailing theory among psychologists is Fazio's (1986) attitude accessibility model.

In the remainder of this paper, each of these theories will be discussed along with the research undertaken to test them and the major problems each seems to have.

Fishbein and Ajzen's Theory of Reasoned Action

The theory of reasoned action actually applies to the prediction of intentions, as opposed to behavior itself. According to the theory, if behavior is under volitional control, then the intention to perform an action will correlate very highly with the action itself. By and large, this supposition has been found to be correct, with correlations between intention and behavior averaging 0.55. The full model is:

B I = wp Attitudebehavior + wp Subjective Norm

and Attitudebehavior = biei

and Subjective Norm = bi mi

and the w's are subjective weightings for a particular person.

Attitudes Toward the Behavior

Attitudes toward the behavior are made up of beliefs about engaging in the behavior and the associated evaluation of that belief. For example, consider the purchase of a car, X. In tests of the model, subjects are asked to list their beliefs associated with buying the car. These beliefs are consequences of the action. One belief might be: "Buying car X will cost me $300 a month." Another belief might be "Buying car X will make me more attractive to the opposite sex." Each belief is then rated for the likelihood that engaging in the behavior will produce that consequence. The likelihood ratings are an index of belief strength. After subjects rate the probability of each belief's being true, they evaluate how good or bad this aspect is. A car payment of $300 might be rated as quite bad, while being attractive to the opposite sex might be quite good. These ratings (both belief strength and evaluations) are quantified on –3 to +3 or 1 to 7 scales. The belief strength and evaluation ratings are multiplied together for each belief and summed across beliefs to give a measure of attitude toward the behavior.

Subjective Norm

The subjective norm term in the model is also multiplicative. The "b's" in this term are beliefs about what relevant others will think if the respondent engages in the behavior. For example, "People who are important to me would not want me to buy car X." Again, the certainty that this is true is rated by the respondent. Each belief receives a second rating: how strongly does the respondent wish to comply with the referent other's views. So, I might feel very certain that important others would not approve of my buying car X but I might have a very low desire to comply with their views.

Intention

Intention is usually measured by one to four questions asking the likelihood the respondent will engage in the behavior. Bagozzi, Baumgartner, and Yi (1989) have called attention to neglect of the reliability of the intention measure. A recent metanalysis (Sheppard, Harwick & Warshaw, 1988) found the mean correlation between intention and the attitudes + norm component to be 0.66.

Selection of the Beliefs

In some studies, subjects list their own beliefs about the consequences of engaging in a behavior. In other studies, pilot testing of a large sample is used to discover the most common relevant beliefs for a particular group; these common beliefs (usually 7 to 15 different beliefs) are then given to the experimental sample for rating.

This model has been used extensively with health and social issues, particularly intentions to use birth control, stop smoking, smoke marijuana, recycle, use alcohol, etc.

Problems in this Area of Research

Limitations on the Domain of Application

Conscious control. As implied by its name, the theory of reasoned action does not apply to habitual actions that are presumably not under continual conscious processing. In other words, the theory applies to behavior the individual consciously elects to do. Many tests of the model have been conducted with habitual behaviors, however. Kahle & Beatty (1987) applied the model to coffee drinking and found good statistical support for the theory. However, a model that included only habit and situation did an even better job of prediction.

Correspondence Ajzen and Fishbein (1977) subsequently published an article further limiting the theory to those situations in which the attitude and behavior demonstrate correspondence. According to Ajzen and Fishbein, attitudes and behavior each have four elements: action, target, context, and time. Correspondence occurs to the extent that attitudes and behaviors are identical on all four elements. To predict intention, attitudes and intention must measure exactly the same four elements. To return to the car purchase example: my intention to buy car X has an action (buy), a target (car X), and to expand the example, a context (at a particular dealer's with a particular loan), and a time (next month). If I want to predict a specific intention, I must measure a specific attitude. The resulting experimental procedure seems extremely trivial; instead of measuring these extremely specific components, one can simply ask "Do you intend to purchase car X from this dealership next month?" Fishbein and Ajzen's primary goal in developing the notion of correspondence was to show why one can't predict specific intentions from general measures of attitude, such as, "What do you think of car X?" To predict specific intentions (behaviors), equally specific attitudes must be measured.

Behavior scaling. Fishbein and Ajzen (1976) also addressed a measurement problem that makes the prediction of intentions problematic. They noted that researchers spend great effort to develop attitude scales that are reliable, valid, and satisfy certain measurement criteria, such as Guttman or Thurstone scales. Behaviors are often chosen haphazardly. Other than the researcher's intuition, there is no way to scale how positive or negative a particular behavior might be. Consider someone who has a "very positive attitude" toward abortion rights. I may find that the person does not sport a bumper sticker advocating abortion rights. Fishbein would not find this puzzling because we don't know anything about how the behavior, displaying the bumper sticker, scales. Is it an extremely positive behavior? Is it slightly positive? Fishbein and Ajzen found that scaling behaviors and attitudes on the same scale (e. g., Likert, Guttman, etc.) resulted in dramatic improvements in the attitude/intention (behavior) correlation. In addition, the predictive power of general attitude measures, in particular Likert scales, improved when a number of behaviors were presented and subjects were asked how many behaviors they had performed or intended to perform. Prediction of individual behaviors from a general attitude measure was extremely poor. Prediction of the score on this so–called behavioral composite scale was much better. People with more positive attitudes performed a greater number of positive behaviors but which specific behavior was performed was not predictable from the general attitude measure.

Additional Variables

Moderators. Although Fishbein and Ajzen believed that any other variable affecting the attitude–intention (behavior) link exerted its effect on one of the terms in the model, Ajzen's own research proved this was erroneous. Self–monitoring refers to a stable individual difference (Snyder, 1974) in the tendency to vary one's behavior in different situations. High self–monitors are sensitive to situation cues and tailor their behavior, dress, and speech to the situation. Low self–monitors are indifferent to situational cues and act on the basis of their principles. Ajzen, Timko and White (1982) found that the attitude/intention model was more predictive of the behavior of low self–monitors than high self–monitors. High self–monitors' intentions did not correlate with their behavior. Low self–monitors apparently tend to act on their attitudes no matter what the situation. High self–monitors may not express an attitude in behavior if they feel the behavior is inappropriate for the situation. To summarize, the Fishbein–Ajzen model works better for low self–monitors because these people are more likely to translate their attitudes into behavior across a variety of situations.

Private self–consciousness. This is a second individual difference moderator currently receiving attention. Private self–consciousness is the dispositional tendency to be aware of one's own internal thoughts and feelings. Miller & Grush (1986) found higher attitude/behavior consistency for people high in private self–consciousness, presumably because they were more aware of their own attitudes.

Other variables. In many tests of Fishbein's model, additional variables have been included and found to increase the attitude/behavior correlation. Some of these include: economic variables (Lynne & Rola, 1988) in predicting farmers' behavior regarding soil conservation; moral values (Boyd & Wandersman, 1991) in predicting condom use; and academic achievement and friends' intentions (Carpenter & Fleishman, 1987) to predict college entry.

Measurement Problems

Methodology. Most tests of Fishbein's model ask subjects to indicate how strongly they believe that a series of belief statements are true. These ratings are followed by a request to indicate how good or bad each belief consequence is. Research by Budd and Spencer (1986) suggests that this format creates a serious confound. First, when the Fishbein items are scattered within a larger questionnaire, the correlations between intention and the [bi ei + bi mi] term are much lower than when all the ratings are presented in a cluster. Second, Budd and Spencer asked students to rate how honestly they felt a questionnaire measuring attitudes, beliefs, norms, and intentions had been answered. When the theory of reasoned action was violated (attitudes were inconsistent with intentions), the hypothetical respondent was seen as more dishonest. The authors argue that Fishbein's theory is part of an intuitive psychology of intention and that this intuitive psychology acts as a source of response bias, promoting consistency between responses.

Self–reports. A second methodology issue is that tests of the Fishbein–Ajzen model rely almost exclusively on self–reports. Behavior itself is rarely directly observed. Self–reports of behavior are notoriously unreliable and have been found to vary with attitude (Ross, McFarland, Conway & Zanna, 1986); people with more positive attitudes report more positive actions than they actually performed; people with negative attitudes report more negative actions than actually performed. Manfredo and Shelby (1988) studied wildlife tax–fund donations. Fishbein and Ajzen's model was applied to both actual behavior gathered from the fund's records and self–reports from donors. The correlations for predicting actual and self–reported behaviors were both significant but were significantly different from each other.

Statistical issues. Evans (1991) has criticized the Fishbein and Ajzen model on statistical grounds. Evans notes that when one is using a multiplicative component to predict a simple variable (e.g., attitudes toward the behavior to predict intention), one must include the main effects (the belief strengths and the evaluations of each belief) in the model prior to the entry of the multiplicative component. Evans notes that multiplicative components have a peculiar property: a change in the zero point or a change in the interval size of either component scale can have marked effects on the size of the correlation coefficient. Evans singles out the Fishbein–Ajzen model for scrutiny. He notes that of 40 studies covered in a recent meta analysis (Sheppard et al., 1988), none have tested a full additive model that included main effects. Evans could find only a single study (Hewstone & Young, 1988) in which attitude researchers studied main effects. These authors examined the relationship between beliefs and evaluations of outcomes to overall attitudes toward the European Economic Community. Hewstone and Young actually compared an additive model that included main effects and a multiplicative model without the main effects. They also compared two different ways of scaling beliefs and evaluations, a –3 to +3 scale and a +1 to +7 scale. For the full additive model, scaling had no effect on the multiple R which was 0.46. For the multiplicative model, the –3 to +3 scale version correlated 0.30 with attitudes. Given the variety of scales used to study the attitude/behavior link and the failure of investigators to enter the main effects first, Evans concludes that a whole body of literature is rendered suspect.

Ajzen's Theory of Planned Behavior

The theory of planned behavior (Schifter & Ajzen, 1985) is an extension of the theory of reasoned action. The theory of planned behavior includes one additional variable: perceived behavioral control. Perceived behavioral control is assessed by asking people how much control they have over performing a particular behavior. In Ajzen's tests of the theory (Ajzen & Madden, 1986; Madden, Ellen & Ajzen, 1992) the measurement of the attitudinal component has also been simplified. Attitudes toward the behavior are measured on a five item semantic differential scale. Including the perceived behavior control variable does lead to significant improvements in R2 for behaviors perceived to be low in control. "Getting a good night's sleep" is an example of a low control behavior; "taking vitamins" is a high control behavior. The behavioral control variable did not improve prediction for the latter behavior, presumably because the behavior itself is already perceived as high control.

Problems. All the problems associated with the theory of reasoned action are also problems for the theory of planned behavior.

Fazio's Attitude Accessibility Theory

Fazio's (1986) model of the process by which attitudes guide behavior is currently receiving a fair amount of attention in the social psychological literature. Fazio defines attitude as a learned association between a concept and an evaluation. Like any construct based on associative learning, attitude strength varies. Fazio indexes strength using a reaction time paradigm. The more rapidly an attitude can be expressed, the greater its strength. The stronger the attitude the more accessible it is.

To guide behavior, attitudes must be accessible. Attitudes that are highly accessible from memory are much more likely to guide behavior than less accessible attitudes. Fazio, Sanbonmatsu, Powell and Kardes (1986) have demonstrated that accessible attitudes are activated spontaneously upon presentation of the attitude issue. Their emphasis on the automatic activation of attitudes differs markedly from Fishbein's view that attitudes result from a controlled effortful process of attribute consideration and evaluation.

Fazio and his colleagues have shown that correlations between attitudes and behavior are much higher among people with highly accessible attitudes. In one study (Fazio and Williams, 1986), accessibility was assessed by how quickly respondents rated the 1984 candidates for U.S. President. Four months later on the day after the elections, the respondents were asked if they had voted and for whom. Among voters with highly accessible attitudes, 80 percent of the variance in voting behavior was explained by attitudes; among voters with less accessible attitudes, only 44 percent of the voting behavior was accounted for by attitudes. Fazio and Williams believe the greater consistency of the highly accessible group is a function of greater attitudinal stability. Highly accessible attitudes are linked to selective processing of information and even selective attention (Fazio, 1989; Roskos–Ewoldson & Fazio, 1992). To the extent that accessible attitudes are accessed each time an individual encounters the relevant concept, the attitude protects its holder against counter–attitudinal information and potential attitude/behavior inconsistency.

Accessibility is weakly related (0.30) to attitudinal polarity. Extreme attitudes do have a tendency to be more accessible. Accessibility, measured by reaction time to an attitudinal query, is a function of: number of previous expressions of the attitude; opportunities for review or rehearsal of the beliefs and behaviors associated with the attitude; direct experience with the attitude object; and anticipation of future interaction with the attitude object. Highly accessible attitudes are more difficult to change (Wu and Shaffer, 1987).

Problems

There have been few published criticisms of the attitude accessibility model. Bargh and Chaiken (1992) have recently claimed that variations in associative strength are more a function of word frequency and cultural norms than individual differences in experience with the attitude object. Bargh and Chaiken were able to replicate Fazio and colleagues' reaction time findings.

The lack of criticism may result from a number of factors: the relative recency of the theory is one obvious consideration. Because the computers needed to measure reaction times are more expensive than a paper questionnaire, attempts to replicate and extend the theory may be more difficult. Finally, social psychologists essentially abandoned interest in learning theories of attitudes in the 1950's; Fazio's work on the power of learned associations to guide behavior may raise issues with which cognitive theorists are less comfortable. One interesting development is that Ajzen discusses Fazio's model without criticism (but possibly without enthusiasm) in a recent chapter.

Summary

As Evans (1991) noted, interest in the Fishbein/Ajzen models has waned in recent years. Attitude accessibility would appear to be the promising newcomer. There are, however, two strands of thought that suggest accurate prediction of specific volitional actions may be a difficult, if not impossible, task.

First, consistency between nonhabitual behaviors themselves tends to be quite low. Epstein (1979) tracked the behaviors of college students over a 2–week period. Behaviors were actions such as number of telephone calls made, number of letters written, number of social contacts initiated, etc. Consistency between actions on any two days was extremely low. With 7–day means, correlations improved dramatically. Thus, even the old adage that the best predictor of behavior is past behavior held true only for aggregated behaviors. To the extent that behaviors themselves exhibit temporal instability, a stable construct, such as an attitude, cannot predict a particular behavior successfully.

Second, Mischel (1983) has noted the power of situations, relative to attitudes or traits, in the control of behavior. He has argued that consistency between an internal state and a behavior is an epiphenomenon constructed by the observer to simplify the task of making sense of the world. Mischel believes behavior is situationally specific and cannot be understood by aggregation to remove temporal instability.

One should also note that the attitude/behavior relationship is currently receiving relatively little research attention. Whether this declining interest is a consequence of the difficulty of demonstrating the relationship or simply a manifestation of the "fads and fashions" of social psychology is a question yet unanswered.

 

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