Impact of Information Technology Reliance and Innovativeness on Rural Healthcare Services: Study of Dindigul District in Tamilnadu,India

Abstract

Purpose: The aim of this report was to analyze the influence of information technology (IT) applications and innovativeness on the acceptance of rural healthcare services. Methods: The impact of IT application, domain-specific innovativeness, and technology acceptance model (TAM) variables on future health IT (HIT) utilization intention has been tested through structural equation modeling techniques. The sample consisted of 465 rural health personnel from the Dindigul District of Tamilnadu, India, who had never had access to IT. Results: Data analysis showed that health workers' innovativeness and IT application have a direct and positive influence on future HIT utilization intention and that the basic TAM hypotheses are fulfilled. IT application can be increased with interfaces that are easier to use, but only if perceived usefulness remains high. Health personnel's innovativeness positively influences technology exposure and the ease of use perception of the IT medium, referred to throughout this article as the “health channel.” Conclusions: This research enables health departments to know which aspects of their communication strategies to highlight in order to get health personnel and other service providers to adopt IT. Perceived ease of use and IT application has a significant influence on health personnel's willingness to adopt HIT. This shows that health information and adoption by health personnel are key tools in the increase of future HIT. It is also recommended that health directors target some of their health campaigns to the more innovative beneficiaries. There are still too few studies that have analyzed the effects of innovativeness and IT adoption on behavior of health personnel. This work aimed to combine the influence of IT adoption, innovativeness, and the traditional TAM in order to construct an improved model for HIT acceptance. It will require an integrated model to do so.

Introduction

Information technology (IT) adoption depends on the profile of potential users as not all health personnel accept an innovation at the same time.¹ Literature review shows that, among the factors, the relationship between the technology and the degree of receptiveness to innovation are factors that determine how quickly health personnel adopt IT.^2,3 In India (particularly in Tamilnadu), as in other countries, growth in the health sector and population has not been accompanied by a similar growth in the number of health personnel. Despite the fact that 40.8% of the health sector based their service decisions in rural health sectors on information they had obtained from health personnel, only 13.6% of the Dindigul District and 27.3% of health personnel used IT in 2006.⁴ Information on the users of the system is therefore important, as in the final instance they are the ones who will determine the success or failure of this new IT medium or health channel. Individual attitudes toward new health channel adoption are based on classical models of behavior of personnel.^5
–7 The innovation dissemination process⁸ and information systems acceptance^9,10 have been used as the explanatory framework for IT adoption analysis in relation to many different services and sectors.

In the last 20 years, different lines of research have focused on identifying certain factors influencing the acceptance of information systems and have provided models and theoretical proposals. In particular, the technology acceptance model (TAM) introduced by Davis⁹ and Davis et al.¹⁰ has received considerable attention from the scientific community^11

–16 and has been used to study many types of technological innovation. This model explains attitudes toward information systems, predicts use intentions and adoption, and is the most widely used theoretical system in this field.

However, although the TAM has provided understanding of acceptance of information systems, more in-depth understanding is needed of the factors that contribute to the acceptance of the Internet as a health channel. More in-depth studies are still needed on the influence of attitudes toward innovation on the behavior of non-user health personnel.³ It is also crucial to understand information use patterns in order to develop effective strategies for attracting non-user health personnel.^17,18 In addition, the group of Internet users interested in future health IT (HIT) can act as opinion leaders for other consumers.^3,19

In view of the above, this work aims to combine in an integrated model (Consumers Personal Characteristics Extended TAM [CPCETAM]) the influence of innovativeness, IT adoption, and the traditional TAM in order to construct an improved model for HIT acceptance. The study was divided into three parts. In the first section, the conceptual model is presented, focusing on the rationale for the constructs used to expand the TAM and deriving testable hypotheses. In the second or methodology section, design, sampling, and measures are described and validated. In the third section, the results, based on a sample of 465 Dindigul District IT users, are presented, and managerial implications are discussed.

Theoretical Framework

HIT intention refers to a technical state that reflects the user's decision to adopt an information system or service in the immediate future.²⁰ In the context of virtual service providers, this would be the decision to use the Internet as a new health channel. The proposed model for integrating the influence of innovativeness, health information use patterns, and attitudinal TAM variables on HIT intention is presented below.

TAM

The TAM was developed by Davis⁹ and Davis et al.¹⁰ to explain the acceptance of IT for different tasks and may be used to predict Internet health utilization intention.^14,21 This model establishes that the intention to use a technology is determined by the individual's attitude toward using that technology. That attitude is, in turn, determined by the technology's perceived usefulness and perceived ease of use.

Davis et al.¹⁰ identified perceived usefulness and perceived ease of use as the basic determining factors in information system acceptance. These authors defined perceived usefulness as the degree to which a consumer believes that the use of a system will increase his or her performance. Specifically, it refers to effectiveness at work, productivity (understood as time savings), and the relative importance of the system for that individual's work. Perceived ease of use refers to the degree to which a consumer believes that no effort will be required to use the system, with effort being understood to include both physical and mental effort, and how easy it is to learn to use the system.¹⁰

Both the perceived usefulness and the perceived ease of use influence an individual's attitude toward a technology. Attitude and perceived usefulness in turn predict the individual's behavior intention. In addition, perceived ease of use influences perceived usefulness.

The following hypotheses were proposed:

H1. Perceived ease of use of the HIT as a health channel has a positive influence on the perceived usefulness of the HIT as a health channel.

H2. Perceived ease of use of the HIT as a health channel has a positive influence on attitudes toward the HIT as a health channel.

H3. Perceived usefulness of the HIT as a health channel has a positive influence on attitudes toward the HIT as a health channel.

H4. Perceived usefulness of the HIT as a health channel has a positive influence on future health management IT utilization intention.

H5. The attitude toward the HIT as a health channel has a positive influence on future health management IT utilization intention.

Innovativeness and Hit

A literature review of new technology adoption revealed several works that propose methods for distinguishing between categories of adopters^1,22 and that try to characterize the behavior of the individuals in the different categories.^3,23,24

Innovativeness is a concept linked to the new technology adoption process that has received considerable attention from researchers.^25
–27 This construct of the personality of individuals reflects their degree of adoption of technology and ideas that are new in their individual experience. Rogers and Shoemaker²⁸ considered that innovativeness is the degree to which an individual adopts an innovation before other members of his or her social system. Midgley and Dowling²⁶ defined innate innovativeness as the degree to which an individual is receptive to new ideas and makes innovative decisions independently of the experiences related by other individuals and maintained that first adopters are those with the greatest innate innovativeness. In contrast, Hirschman²⁵ considered that innovativeness is influenced more by the social system than by the individual's personality and that an individual's desire to seek new stimuli is a segmentation variable that provides three groups of consumers with different degrees of innovativeness: adopters (individuals who adopt a technology), vicarious consumers (individuals who seek information on new technologies and services), and users (individuals who apply new uses of existing technologies).

Although many researchers have used different techniques to measure innovativeness, two main approaches to the concept can be distinguished—general innovativeness and innovativeness applied to a specific domain. Joseph and Vyas²⁹ focused on a cognitive perspective, considering that innovativeness incorporates the individual's intellectual, perceptual, and attitudinal characteristics. General innovativeness reflects openness and an individual's search for new experiences, and it is a significant predictor of HIT intention.^29,30

A limitation of the previous definition is its degree of abstraction and its generalist character as innovativeness can be associated with a specific technology or service rather than with a generic characteristic of an individual's personality. Owing to this limitation, Goldsmith and Hofacker³¹ developed a measurement scale for innovativeness in a specific domain. Domain-specific innovativeness is the individual's tendency to try innovations in technology, services, or processes in his or her area of interest.³¹ Domain-specific measures are more predictive of the adoption of new systems than global innovativeness.^31,32 Later research^2,33
–35 has applied the domain-specific innovativeness scale to HIT and has shown a direct and positive influence of this variable in both the search for pre-technology adoption and the decision to adopt technology in health sectors.

A set of studies relates personnel innovativeness and intention to use IT. Eastlick and Lotz²⁴ showed that innovators are heavy users of interactive electronic records data and that the strongest predictors of potential innovator group membership were the perceived advantage of interactive electronic data transmission and innovation over traditional manual data entries and its compatibility with lifestyles. The study by Limayem et al.³⁶ found that innovativeness influences IT for behavior of health personnel both directly and indirectly through user's attitudes and intentions. Goldsmith³⁴ also found evidence that the frequency of applying IT and intent to adopt IT in the future were predicted by general innovativeness, an innovative predisposition toward IT application in health sectors and involvement with the information. Citrin et al.² supported this conclusion with their findings that domain-specific innovativeness along with IT usage directly influences a user's adoption behavior of IT adoption in health activities.

To complement the contributions of the above studies, we propose the following hypothesis:

H6. Innovativeness toward HIT has a favorable influence on the future IT utilization intention.

Indirect effects of innovativeness toward future HIT utilization intention, mediated by TAM variables, can also be expected. According to innovation diffusion theory, earlier adopters, given their knowledge, experience, technical competence, and high aspiration,^1,37 should consider the same technology to be easier to use and less challenging than later adopters. Therefore, highly innovative individuals may perceive IT adoption as easier to use than less innovative consumers.

Previous research has found that individual innovativeness enhances perceived ease of use. Research by Yi et al.³⁸ across innovation showed that individual innovativeness is one significant antecedent, among others, of ease of use. Lewis et al.³⁹ also found that innovativeness had a significant positive effect on ease of use in the context of the adoption and use of ITs by health personnel and administrators in their service activities. Agarwal and Karahanna⁴⁰ demonstrated that personal innovativeness indirectly influences behavioral intention via its effect on cognitive absorption, which is in turn a significant determinant of ease of use. Jashapara and Tai⁴¹ showed that personal innovativeness with IT influences positively the perceived ease of use of a virtual learning environment. Therefore, we hypothesize that:

H7. Innovativeness toward HIT has a favorable influence on perceived ease of use of the IT as a health channel.

Hirschman²⁵ clearly stated novelty seeking is an inherent characteristic of innovators and that novelty seeking would seem to represent an innate search for information. She argued that a possible explanation for this linkage is that the consumer who has sought and stored more information is likely to be better equipped for novel problem circumstances and can improve his or her performance to adopt IT.

The acquisition of novel information may be achieved through many different sources. When Hirschman²⁵ wrote her article in 1980 (p. 285), IT was not a feasible medium from which to acquire information, but we believe that the reasons she provided to explain why innovators would be more likely to read electronic records can be applied directly to IT. Every datum (electronic record) contains patient information, and a health schema subscription (exposure time to IT) represents a commitment by the user to acquire patient data. An information-rich medium, such as health policies or schemes, allows the individual to absorb the accumulated experiences of others in an accessible and low-risk form.

Recent research has provided evidence that Hirschman's ideas are applicable to IT. Rangaswamy et al. reported that innovative users spent more time per week on IT than did other groups of users. Goldsmith³⁵ showed that IT innovativeness is positively correlated with use of IT. Thus, we are able to hypothesize that:

H8. Innovativeness toward HIT has a favorable influence on individual IT exposure levels.

Internet users modify their surfing and health personnel behavior as they gain experience in new environments.⁴³ Thus, expert users surf more quickly, have shorter sessions, and visit a smaller number of health data. In addition, as their exposure and knowledge of the medium increase, they enjoy their surfing experience more^44
–46 and develop more positive attitudes toward using the system for IT adoption.^47,48

Although previous studies have related IT exposure to health decision, the TAM postulates that the impact of external variables is mediated by perceived usefulness and perceived ease of use.

In view of the above, we posit the following research hypothesis:

H9. Individual IT exposure levels have a positive influence on perceived ease of use of the IT as a health channel.

Online Health Information Dependency and Hit Intention

According to the individual medium dependency theory,^49,50 individuals achieve some of their personal and collective objectives by having to access information resources that are controlled by the mass media such as the IT and Internet. In this sense, individual media dependency is defined as “a relation where the individual's capacity to reach his or her objectives, depends to a certain extent on the information resources in the medium”⁵¹ (p. 3).

Individual medium dependency has three dimensions or categories: understanding, orientation, and play.^{49

–57} Understanding focuses on the individual's need to have basic understanding of him- or herself and to find sense in the world that surrounds that individual. Orientation refers to the need to obtain a guide in order to behave correctly with other people. Play is also an important way to learn social roles, norms, and values, and it also provides escape mechanisms and release from tension.⁴⁹

Dependence on the medium information resources may cause cognitive, affective, and behavioral changes in people who are regularly exposed to them.^49,51,53,55 For example, in terms of behavioral effects, the adoption of IT and services may be intensified when individual medium dependency is high.^52,54 Previous studies that focused on the television medium^49,57 have shown that individual dependency on that medium is a significant predictor of HIT behavior. Other studies have found a direct, positive relationship between IT user dependency levels and present and future IT adoption in health sector decisions.^58,59

Stigler⁶⁰ found that users analyze the costs and benefits of the information search and abandon it when they perceived that the marginal costs (waste of time, money, transport, etc.) are greater than the benefits (duration of the search, variety of information sources, etc.) that they obtain. Other studies have also shown that the nature of the information sources may influence behavior of health personnel, for example, when users have to search pre-information through a single channel, as this reduces the effort involved.⁶¹ Klein¹⁷ developed an interactive behavior model where the information search processes are predictors of behavior of health personnel, with the advantages of the IT being useful in the case of search data owing to the low perceived costs of obtaining information.

IT users perceive that the utility of the IT to support the pre-data information process is one of its most outstanding characteristics^62,63 as it is the most appropriate channel for comparing different health options.⁶⁴ The IT allows users to identify their most useful options easily owing to Web tools such as portals and search engines that make it possible to find relevant information for the purchase decision, reducing information search costs.^65
–67 Several authors have suggested that interactivity increases a consumer's skills in exploring and analyzing the available information.^66,68 It is to be expected therefore that users will increasingly use virtual environments to consider different health options.

In addition, the increasing amount of online information that is personalized in accordance with the user's previous searches or purchases helps him or her to make better health decisions and consequently develop a more favorable attitude to health Web sites. It is also worth noting that, according to Petty and Cacioppo,⁶⁹ consumers' attitudes are favorable when they process relevant information for making their health decisions. As more information is available on the IT, consumers tend to make a greater effort to process it, and therefore a positive change in their attitude is to be expected.

Bearing in mind the results in the literature, we tested a similar effect with the following hypothesis:

H10. As online health information dependency increases, so does the future online health utilization intention.

However, online information dependency is at the same time determined by several TAM variables. As we have stated, media system dependency theory sees individuals as having personal goals. People will develop dependency relations with the media as a means of attaining those objectives,⁴⁹ we believe, only if the media allow the individual to attain the goal, that is, if it is a useful instrument in that task. We agree with Ball-Rokeach⁵³ (p. 495) when she stated that media dependency will change “as perceptions of the utility of media resources change.” If users believe that the IT as a health channel enables them to accomplish health tasks more quickly, to make better health decisions, or to save money or lives, the perception of the utility of the medium will increase, as will IT dependency according to the statement of Ball-Rokeach⁵³ cited above.

There, we hypothesize that:

H11. As the perceived usefulness of the IT as a health channel increases, so does the HIT adoption of the users.

Greater perceived ease of use may activate IT adoption because users can become aware of the IT as a tool that not only allows them to fulfill their objectives, as but also with less effort than other channels. In other words, if users can attain the same objective through two different channels (of the same usefulness), it seems rational to rely on the channel that is easier to use and to become more dependent on it with time.

Several studies have reported that the growing dependency on the IT to search for information is due to several benefits, one of which is that it is an easy to use way of accessing patient records and public health information.^70,71 Riffe et al.⁷² posited that easy use of the IT for detailed, in-depth information about specialized topics increases an individual's IT adoption more than general and nonspecific exposure does. This relation is sometimes so strong that it may become counterproductive. For instance, MacDonald and Dunkelberger⁷³ reported that the growing adoption of health personnel of full-text databases due to their ease of use is biasing their research and assignments as they exclude all other information sources. Independent of the positive or negative consequences of dependency, this study also expects to observe a positive relationship between perceived ease of use of the IT and dependency on this medium:

H12. As the perceived ease of use of the IT as a health channel increases, so does the HIT dependency of the users.

Figure 1 shows the extended TAM examined here (CPCETAM). The model below shows the influence of innovativeness, HIT dependency, and TAM variables on future health technology utilization intention.

Fig. 1.

The extended technology acceptance model examined here (Consumers Personal Characteristics Extended Technology Acceptance Model).

Methods

Sample and Data Collection

The sample consisted of 465 IT users over the age of 18 years. The fieldwork was developed in the Dindigul District of Tamilnadu from April to May 2010, and the sample consisted of users who had never been aware of IT application in health.

Table 1 displays demographic variables associated with the sample. Of the total sample, 51.4% were men, and 48.6% were women. A large percentage of the interviewees belonged to the age segment between 25 and 49 years (58%), were medium-educated (51.2%), and had an above average level of income (42.1%).

Table 1.

Sample Demographics

CHARACTERISTIC	PERCENTAGE (N=465)
Gender
Male	51.4
Female	48.6
Education
No formal education	0.4
Primary	11.2
Secondary	51.2
University	37.2
Age (years)
24 or under	27.4
25–34	28.0
35–49	30.0
50–64	12
65+	2.6
Income (monthly)^a
No income (family dependent)	20.2
Below average	17.4
Around average	20.4
Above average	34.5
Well above average	7.6

Monthly income average for this District was 900 rupees.

Measures

As illustrated in Table 2 the constructs used in our study were adapted from previous studies and were measured by multiple-item 5-point Likert-type scales with the exception of IT exposure (one item) and future HIT utilization intention.

Table 2.

Measurement Scales

CONSTRUCT	SCALE TYPE	ITEM CODE	ITEM DESCRIPTION
Perceived usefulness	Likert 5-point	USEFUL1	Using IT for health enables me to perform quick healthcare
		USEFUL2	Using IT for health helps me to make better health decisions
		USEFUL3	Using IT for health improves the health tasks
		USEFUL4	Using IT for health saves me money
		USEFUL5	Using IT for health increases the productivity of my health
Perceived ease of use	Likert 5-point	EASE1	I think I would find it easy to think how to apply IT in healthcare
		EASE2	I think that it would be possible for me to apply IT in healthcare without the help of any expert
		EASE 3	I think that I have no problems in using the Internet or IS for healthcare
		EASE4	I think that I could become skillful in using health IT
		EASE5	I think that to apply IT in healthcare does not need much mental effort
		EASE6	I think that it is easy to use IT-enabled healthcare to get a efficient health support
Innovativeness		INN1	I think I would be the first in my circle to find health IT
		INN2	I think I would be the first to apply health IT
		INN3	I think I would know all about the health IT
		INN4	I think I would go for new innovative process like mHealth
Attitude toward health IT			Using IT in healthcare is:
		ATT1	Enjoyable
		ATT2	Convenient
		ATT3	Absorbing
		ATT4	Attractive
		ATT5	Interesting
		ATT6	Worth it
		ATT7	Pleasant
		ATT8	Secure
		ATT9	Necessary
		ATT10	Good idea
Health IT dependency		DEP1	IT helps in finding sources for health problems
		DEP2	IT helps in getting immediate healthcare
		DEP3	IT supports the improved knowledge on healthcare

IS, information systems; IT, information technology; mHealth, mobile health.

The scale items for perceived ease of use and perceived usefulness were adapted from the measurement defined by Davis⁹ and Ahn et al.¹¹ Attitude toward e-health was measured using the Personal Involvement Inventory scale⁷⁴ with modifications to suit the environment of IT adoption. Innovativeness was measured using a four-item scale based on the domain-specific scale developed by Goldsmith and Hofacker.³¹ HIT dependency was derived from the individual action orientation dimension of the scale provided by Grant.⁵⁵ The two items used to measure IT exposure and future health utilization intention were taken from Ruiz and Sanz⁵⁹ and Goldsmith.⁷⁵ Table 2 describes how the variables used in this research were measured.

Validation of the Measurement Model

A confirmatory factor analysis was developed to validate the measurement model (Appendix, Table A1). To guarantee convergent validity, items with factor loadings that were not significant or below 0.6^76,77) and those for which the Lagrange multiplier test suggested significant relations over a different factor to the one for which they were indicators⁷⁸ were eliminated. The final measurement model is also reliable as all Cronbach α values⁷⁹ are above the recommended value of 0.7,⁸⁰ and composite reliability indexes are also higher than 0.7.⁸¹ No evidence of a lack of discriminant validity is found, either applying the confidence interval criterion⁸² or the average variance extracted criterion,⁸⁰ as can be seen in the Appendix, Table A2. Nomological validity is assured as the difference between the measurement model and the theoretical model (structural model) χ² values is not significant.^78,82

Results

After the psychometric properties of the measurement instrument were evaluated, the structural model shown in Figure 1, which synthesizes the hypotheses posited, was estimated. Hypotheses were tested using structural equation models. Steenkamp and Baumgartner⁸³ highlighted two main advantages of this technique. First, structural equation models allow measurement error to be explicitly incorporated into models and its influence on the degree of fit to be analyzed. Second, unlike multiple regressions, relations between model variables can be studied simultaneously as several dependent variables can be considered in the same model and the same variable can be at the same time an endogenous and exogenous variable regarding the other variables in the model.

Raw data screening showed evidence of non-normal distribution (Mardia's coefficient normalized estimated=16.5). Although other estimation methods have been developed for use when the normality assumption does not hold, the recommendation of Chou et al.⁸⁴ and Hu et al.⁸⁵ of correcting the statistics rather than using a different estimation model has been followed. So, robust statistics⁸⁶ will be provided.

The empirical estimates for the main effects model are shown in Table 3. The results indicate that the data fit our conceptual model acceptably (S-B χ²=295.03, df=124, p=0.00; RMSEA=0.055; NFI=0.90; NNFI=0.92; CFI=0.94). Modification indices do not provide any indication of misfit of the structural model, suggesting that there is no need to include any new path between constructs in the model.

Table 3.

Hypothesis Testing

MODEL BLOCK	HYPOTHESIS	STANDARDIZED LOADING	ROBUST T-VALUE
Original TAM	H1. Perceived ease of use—perceived usefulness	0.613^**	10.15
	H2. Perceived ease of use—attitude to HIT	0.133^**	1.99
	H3. Perceived usefulness—attitude to HIT	0.569^**	7.52
	H4. Perceived usefulness—health IT intention	0.188^**	2.32
	H5. Attitude to health IT—IT intention	0.213^**	3.52
Innovativeness role	H6. Innovativeness—Health IT intention	0.304^**	6.40
	H7. Innovativeness—perceived ease of use	0.284^**	4.08
	H8. Innovativeness—IT exposure	0.248^**	4.53
	H9. IT exposure—perceived ease of use	0.216^**	3.62
IT information dependency role	H10. IT health information dependency—health IT intention	0.192^**	2.75
	H11. Perceived usefulness—IT health information dependency	0.642^**	6.67
	H12. Perceived ease of use—IT health information dependency	0.237^**	2.37

S-B χ² (124 df)=295.03 (p<0.01); NFI=0.90; NNFI=0.92; CFI=0.94; IFI=0.94; RMSEA=0.055.

p<0.05; ^** p<0.01.

IT, information technology; TAM, technology acceptance model.

The results obtained show that the perceived ease of use of the IT as a health channel has significant positive influences on the set of variables that act as mediators in future HIT utilization intention—the perceived usefulness of the IT as a health channel (H1: A=0.613; p<0.01), attitude toward HIT (H2: A=0.133; p<0.05), and dependency on the IT to obtain information for the health-related activities and process (H12: A=0.237; p<0.01). The results also show how the perceived ease of use of the technology is highly conditioned by IT user experience measured by exposure to the medium (H9: A=0.216; p<0.01), that is, users who access the information system more frequently perceive less difficulty associated with its use. Consequently, although perceived ease of use does not directly influence HIT utilization intention, it does activate the other variables that directly influence the intention to become electronic health personnel.

As Table 2 shows, perceived usefulness of HIT influences future HIT utilization intention both directly (H4: A=0.188; p<0.05; H4) and through its influence on HIT attitude (H3: A=0.569; p<0.01), which also has a positive effect on HIT utilization intention (H5: A=0.213; p<0.01). These results confirm that TAM is a valid model to explain HIT utilization intention. The question is whether a consumer's personal characteristics can significantly improve it or not.

Focusing on one of these personal characteristics of the consumers, dependency on the IT to obtain information in the health process has a positive and significant impact on future HIT utilization intention (H10: A=0.192; p<0.01). As expected, dependency is heavily conditioned by how useful users perceived the IT to be as a health channel (H11: A=0.642; p<0.01).

The role of the second personal characteristic of the users—their degree of innovativeness—also is revealed as crucial to improve the explanatory power of TAM. Innovativeness not only directly and positively influences a user's future intention to acquire products or services using the IT (H6: A=0.304; p<0.01), but it also influences the perceived ease of use of the channel both directly (H7: A=0.284; p<0.01) and by making them spend more time connected to the IT communication (H8: A=0.248; p<0.01).

Discussion and Conclusions

The main contribution of this research lies in proposing and empirically verifying a model that integrates the influence of innovativeness, IT health information dependency, and the traditional TAM future HIT utilization intention.

The indirect influence of perceived ease of use on future health utilization intention through attitudes highlights the central role of the perceived ease of use of the IT for health in the adoption of this health channel. This result has important managerial implications. If an e-commerce company wishes to increase the number of users, in addition to service level and quality considerations, it must also take great care to design a user-friendly Web site and include elements that facilitate health service.

Furthermore, this study emphasis the importance of general IT use in the population as the preceding step to growth in electronic commerce,^47,48 given the central role of user experience (exposure) as an antecedent to perceived ease of use. The direct and positive influence of innovativeness toward HIT on future IT health utilization intention confirms similar results obtained in prior studies showing that a positive attitude to electronic channels is a significant predictor of adoption.^20,24,36,87 This result also highlights that health service providers need to be able to do more than just identify innovators. They should target some of their advertising campaigns toward the more innovative users. As Moore³⁷ suggested, innovators provide companies with great feedback early in the design cycle and may become supporters who will influence users. If the bulk of non-HIT users need word-of-mouth promotion before they will adopt HIT, innovators can initiate this dialogue.

Targeting innovators has an additional attraction for directors. We know that these individuals are usually early adopters of many products, and therefore they are well able to bear price-skimming strategies, that is, charging a relatively high price for a short time when a new, innovative, or much improved health product is launched onto a market to “skim off” patients who are willing to pay more to have the HIT product sooner. Prices are lowered later when demand from the early adopters falls. This strategy is probably the most profitable in terms of margin for the service provider.

Finally, we have shown the direct, positive influence of IT health information dependency on future HIT utilization intention. This result is consistent with earlier studies in cultural contexts different from ours showing that IT users who use health information to support their health decisions develop a greater IT health utilization intention and that IT dependency has a direct positive influence on IT health utilization intention. But, it should not be forgotten that this dependency is highly conditioned by how useful and easy to use users perceived the IT health channel to be. That means that dependency should be carefully built by adding value to the medium. Overloading of unstructured information, privacy concerns, or poorly designed storefronts, among others, can reduce IT usefulness and ease of use as a health channel and subsequently IT health information dependency.

Therefore, we can conclude that users develop complex health strategies in which achievement of the final objective (HIT utilization intention) is preceded by securing a set of prior objectives. This result may also have important implications in terms of e-commerce Web site design. Information is always important for decision-making, but in e-commerce it appears to be even more so. Differentiation in the amount and quality of information on the product or service being offered thus becomes a significant competitive instrument.

These conclusions have some limitations and open new lines for future research. A possible limitation is that the study has focused on measuring attitudes (future HIT utilization intention), which do not always become behaviors. Thus, possible future research could contrast the proposed model with a sample of IT users to see if the results obtained remain valid.

Another limitation is that there has been no consideration of the influence of the characteristics of goods and services on health behavior. Prior studies have shown that the greater the perceived risk for users, the greater the pre-user information search effort. We therefore propose as a future line of research to apply the model to the purchase of search and credence goods. Given that the perceived risk in the IT is greater than in traditional environments, another future line of research would be to analyze the influence of perceived user risk on the different variables analyzed.

Footnotes

Disclosure Statement

No competing financial interests exist.

Appendix

Table A2.

Validation of the Final Measurement Model: Discriminant Validity

	1	2	3	4	5
Perceived usefulness	0.75	0.60^**	0.20^**	0.65^**	0.62^**
Perceived ease of use	(0.51; 0.69)	0.66	0.33^**	0.47^**	0.558^*
Innovativeness	(0.09; 0.32)	(0.19; 0.46)	0.82	0.17^**	0.08
Attitude to health IT	(0.59; 0.71)	(0.36; 0.58)	(0.05; 0.29)	0.77	0.44^**
Health IT dependency	(0.53; 0.72)	(0.43; 0.67)	(−0.04; 0.21)	(0.33; 0.55)	0.77

The diagonal represents the square root of the average variance extracted. Above the diagonal the shared variance (squared correlations) are represented. Below the diagonal the 95% confidence interval for the estimated factors correlations is provided.

p<0.05; ^** p<0.01.

IT, information technology.

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