The Awareness and Want Matrix with Adoption Gap Ratio Analysis for e-Service Diffusion Effect

Abstract

Since the hierarchical stages of a customer purchasing decision or innovation adoption process are interrelated, an analysis of all their stages, including awareness, want, and adoption, in relation to product or service diffusion, is urgently needed. Therefore, this study proposes the use of an awareness and want matrix, together with an adoption gap ratio analysis, to assess the effectiveness of innovation and technology diffusion for e-services. This study also conducts an empirical test on the promotion performance evaluation of 12 e-services promoted by the Taiwanese government.

Introduction

The prevalence of broadband Internet¹ has influenced traditional aspects of decision making, such as information gathering, entertainment, communication, and media usage,^2,3 and brought about various social benefits.^4–6 Consumer behavior continues to develop over the Internet. In the last 10 years, researchers have begun discussing customer acceptance of e-service using the Theory of Reasoned Action (TRA),⁷ the Technology Acceptance Model (TAM),⁸ the Theory of Planned Behavior (TPB),^9,10 and the Technology Readiness and Acceptance Model (TRAM).^11,12 However, most studies simply discuss adoption from the system design and improvement point of view, and merely explore the adoption of specific e-services. Studies examining the adoption of e-services from a macro view, such as that involving the government, and from a hierarchy-type view, such as that of a customer adopting a new product or service, are rare.

Governments throughout the world have formulated concrete actions and allocated a huge budget to promote Internet usage and ensure that their citizens are provided with digital opportunities for different kinds of services,^13,14 such as the FCC strategy plan (United States),^15,16 e-Japan and U-Japan,^17,18 e-Korea and U-Korea,¹⁹ e-Hungary,²⁰ and e-Taiwan,²¹ M-Taiwan, and i-Taiwan.^22–24 Making rational choices regarding funding allocations from the customer behavior viewpoint has become a serious issue that should be discussed and solved. Therefore, the two main aims of this study are as follows:

To propose an awareness and want matrix with an adoption gap ratio analysis (AWA gap analysis) for the comparison of different e-services, from a customer behavior viewpoint.

To conduct an empirical study assessing the diffusion effectiveness of e-services promoted by the Taiwanese government.

Literature Review

E-service

The Internet is a new advertising medium, a big direct-mail catalogue, a commodity marketplace, a new sales channel, and a big encyclopedia.²⁵ When customer service is supplied over the Internet, it is referred to as “e-service.”²⁶ E-service has been defined as a web-based service²⁷ or an interactive service delivered over the Internet.²⁸ It has also been defined as deeds, efforts, or performances with delivery functions mediated by information technology (the Web, information kiosks, and mobile devices).²⁹ E-service includes the service element of e-retailing, customer support and service, and service delivery.³⁰

Voss³¹ emphasized that e-service may be delivered separately or together with e-commerce. E-service can provide not only basic commerce functions, such as online catalogues, online transactions, and order fulfillment, but also a series of customer-oriented activities, such as online help, configuration, and customization, as well as security mechanisms.⁹ E-services range from the electronic provision of traditional services, such as banking (e.g., E*TRADE), investing (e.g., chase.com), and airline ticketing (e.g., Internet service providers).³² They also include hedonic e-services, such as online gaming and music downloads (e.g., KKBOX, blizzard.com, and iTunes), and socially oriented services, such as virtual communities and social-networking sites (e.g., Second Life, MySpace, and Facebook).³³

As consumer behavior continues to expand in cyberspace, it is important to understand the role of e-service in this new arena. However, the theory and practice of e-service is still in its infancy.³⁴ Over the past 10 years, researchers have begun to discuss customer acceptance of e-service using TAM⁸ and TPB,^9,10 which are both adaptations of TRA.⁷ TAM hypothesizes that system use is directly determined by behavioral intention to use, which is, in turn, influenced by user attitude toward the system and per unit of the system. According to TPB, the actual behavior of a person in performing certain actions is directly influenced by his or her behavioral intention and, in turn, jointly determined by attitude, subjective norm, and perceived behavioral control.³⁵ Due to the necessarily high involvement of customers in co-production of the service, TAM applied in market settings may not sufficiently explain the technology adoption behaviors of consumers. Taking individual differences into account, TRAM,¹¹ an integration of TR¹² and TAM, was established as a means to explain better the intentions of customers using e-services. However, these studies merely explored the adoption of a particular e-service, and simply discussed adoption from the system design and improvement point of view. Studies³⁶ that examine the adoption of e-services from a macro view, such as that of the government, have been rare.

Consumer purchasing and the innovation diffusion theory

A consumer purchasing decision is the outcome of a complex, multistage process.³⁷ The earliest and most well-known process, called attention-interest-desire-action (AIDA), was proposed in the late 1800s and early 1900s^38,39 and theorized that sales people have to attract attention (cognition), maintain interest and create desire (affect), and obtain action (conation).^40,41 Most research endeavors posited the hierarchical model to include the six hierarchical stages of awareness, knowledge, liking, preference, conviction, and purchase.⁴² The process of arriving at a purchasing decision essentially entails that each step be conditional on the positive or favorable hierarchical outcome of the previous one.⁴³

From the view of customer retention and financial control, another hierarchical model referred to as the customer learning curve (CLC) with five basic stages (need, awareness, access, motivation, and purchase) was proposed in 1936.⁴⁴ In the CLC model, marketing managers can determine the penetration or number of customers who make it through all the stages by measuring the percentages of retained customers at each stage and multiplying the percentages. It guides managers in assessing progress. It enables the strategic marketer to select the best marketing program, defined as the one that increases revenue with the most per dollar of marketing expenditures.⁴⁵

One of the most often cited models for new technique adoption is the innovation diffusion theory (IDT)⁴⁶ proposed by Rogers in 1962. It consists of five stages, starting from having knowledge to being persuaded, to making the decision and implementing it, to the final stage of confirmation. This theory further involves four main interacting factors: innovation, communication channels, social systems, and time.⁴⁷

According to the willingness of the customer to adopt new products and the adopting time, the technology adoption life cycle of Rogers categorizes potential adopters into five types based on the adoption rate: innovators, early adopters, early majority, late majority, and laggards, with a bell-shaped distribution of adopters.⁴⁸ The adoption success of a new product depends mainly on the innovators and early adopters.⁴⁹ Customer initiators (innovators and early adopters) usually affect how a new product is perceived in the market through their level of satisfaction.⁵⁰ In general, adoption studies consider the impacts of innovation characteristics, such as the relative advantage, ease of use, risk, and complexity, as well as consumer characteristics, such as demographics and innovativeness.^51–53 Recently, a few but limited number of studies have paid attention to the impact of marketing communication efforts on new products or service adoption.⁵⁴

Various models of consumer purchasing or new techniques of adoption decision making consist of a sequence of mental stages or levels that consumers experience throughout a purchasing decision.^55–61 Previous studies^40,43 have summarized the hierarchical stages of the consumer purchasing decision model into three main stages: awareness/cognition, want/interest, and adoption/decision. When evaluating diffusion effectiveness of products or services, the percentages or probabilities of awareness, want, and adoption corresponding to the three main stages of the purchasing decision should be measured.

From a new product or service promotion perspective, awareness of a new service or product does not necessarily translate to choice or usage if it is short of want. Therefore, want for new products or services should also be created typically through promotion and education. In general, the adoption rate rises when awareness and want are both spreading. The adoption rate of a product or service should be highly related to its awareness and want rates. Based on the above discussion, this study has three hypotheses as follows:

H1: Diffusion of adoption is the function of awareness and want.

That is: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document}\begin{align*}U (x) = f [A (x),\ W (x)] \tag{1}\end{align*}\end{document}

where U(x) represents the adoption of product x or service x; A(x) represents awareness for product x or service x; and W(x) represents want for product x or service x.

H2: Adoption rate is positively related to the awareness and want rates.

H3: Adoption rate is bound to the awareness and the want rates.

H3-1: The adoption rate is bound to awareness.

That is: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document}\begin{align*}Pr [U (x)] \leq Pr [A (x)] \tag{2}\end{align*}\end{document}

H3-2: The adoption rate is bound to want.

That is: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document}\begin{align*}Pr [U (x)] \leq Pr [W (x)] \tag{3}\end{align*}\end{document}

Then: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document}\begin{align*}Pr [U (x)] \leq \min \{ Pr [A (x)], Pr [W (x)] \}\tag{4}\end{align*}\end{document}

where Pr[U(x)] represents the adoption rate of product x or service x; Pr[A(x)] represents the awareness rate for product x or service x; and Pr[W(x)] represents the want rate for product x or service x.

Proposed Analytical Model

AW matrix analysis

This research proposes a model for evaluating the impact of marketing communication efforts on e-services promoted by the government or enterprises. Based on the technology adoption life cycle (bell curve), in combination with innovators and early majority stages, and using the three adoption life-cycle cumulative rates of 15%, 50%, and 85%,⁶² awareness and want rates are divided into four levels: popular, high, middle, and low. In the AW matrix, the location of an e-service in this matrix indicates its awareness and want rates. The two-dimensional cross of the AW matrix is divided into 16 categories, shown in Figure 1.

FIG. 1.

The AW matrix with adoption gap ratio analysis—based from technology adoption life cycle.

The four levels of want in this study are defined as star, cow, pet, and rock by a decreasing order of popularity. The star attracts the most number of customers. The allusion to a cow comes from its lack of star luster and its contribution (i.e., milk) to something essential like nutrition. In short, while the degree of want for this e-service may not be high, it is indispensable. The pet does not have the aforementioned popularity or indispensability, nor is it a necessity for daily life, but it does bring happiness. The rock is interesting to a few people. It is alluded to a precious mineral that requires carving and polishing to transform it into something more valuable. Moreover, by following the rising of the want rate, e-services will move stepwise from rock to star.

Based on the degree of awareness, star, cow, pet, and rock are each further divided into four levels (Figure 1). By following the rise in the awareness rate, e-services will move stepwise from the right to the left. If the want rate of one e-service is low (e.g., rock or pet) and does not increase gradually when the awareness rate keeps rising, this may cause more trouble for the management. In this case, the management should examine the promotion strategy and reallocate its promotion fund.

In the AW matrix, awareness and want are on the same level for the four regions, which are passed by the upward sloping 45-degree line, including super star, big cow, ordinary pet, and little rock. In these four regions, the promotional efforts are optimal, but the promotion performances are different. For example, an e-service in the super star area is similar to a superstar having a huge market and fan base, suggesting that the positioning strategy of e-service is right and the promotion effort is excellent. In contrast, an e-service in the little rock area may have a great need for increased want and awareness, or there might be no need to keep on promoting it.

The e-services located in the lower right, upward-sloping 45-degree line all have an awareness rate greater than the want rate, whereas the opposing e-services in the other way around have opposite results. The greater distance beyond the 45- degree line means that the promotional performance is less optimal, and more efforts are needed for promoting awareness or want. For example, e-services in the upper-most region pertain to the potential star area, which is similar to a new star with good potential and future. The potential star has a big chance of becoming a superstar after the awareness spreads. E-services in the dangerous rock area could be considered a waste of budget. They are well known but are used and wanted only by a few people. Thus, expenses should be trimmed. An overall evaluation of the e-service is also suggested.

This study defines five possible strategies: create (C), spread and raise (S), hold (H), review (R), and divest (D).^63,64 The create strategy creates new or additional wants in the original market or develops a new segment market. The spread and raise strategy raises or spreads the awareness or wants in the original market. The hold strategy maintains the good work in the original market. The review strategy reevaluates wants, supervises the promotion strategy, or repositions the target market. The divest strategy divests the e-service by phasing it out or selling it, and then uses the resources elsewhere. Each region and its corresponding strategy suggested are shown in Table 1.

Table 1.

Prescriptions of the AW Matrix

Category	Want	Awareness	Strategy^*	Action	Nowadays target market
Super star	Popular	Popular	H	Keep up the good work	Mass market
Big star	Popular	High	S	Raise awareness	Mass market
Second line star	Popular	Middle	R&S	Review the promotion strategy and strongly spread awareness	Mass market
Potential star	Popular	Low	R&S	Review the promotion strategy and strongly spread awareness	Mass market
Super cow	High	Popular	S	Raise want	Mass market
Big cow	High	High	S or H	Raise both awareness and want, or maintain it where it stands	Mass market
Growing up cow	High	Middle	S	Strongly spread awareness then follow it up by raising want	Mass market
Baby cow	High	Low	S	Strongly spread awareness then ollow it up by raising want	Mass market
Pampered pet	Middle	Popular	S&Cor R	Raise want and create new want, or reevaluate the budget	Niche market
Adored pet	Middle	High	S&Cor R	Raise want and create new want, or reevaluate the budget	Niche market
Ordinary pet	Middle	Middle	S&Cor H	Raise awareness and create want together, or maintain it where it stands	Niche market
Abandoned pet	Middle	Low	S&C	Raise awareness then follow it up by creating new wants	Niche market
Dangerous rock	Low	Popular	R & D	Immediately reevaluate then shift resource to others	Bit market
Big rock	Low	High	R&D	Immediately reevaluate then shift resource to others	Bit market
Small rock	Low	Middle	R&S or D	Reevaluate, then raise both awareness and want or just withdraw it	Bit market
Little rock	Low	Low	R&S or D	Reevaluate, then raise both awareness and want or ust withdraw it	Bit market

C, create; S, spread and raise; H, hold; R, review; D, divest.

Using the AW matrix, the manager should first reevaluate wants. If the e-service is found to be necessary to the people, then the e-services located in the lower area should be pulled up to the upper area, the left-area e-services should be pulled to the right, and the e-services in the lower-left area should be pulled up to the top-right area, step by step.

Adoption gap ratio analysis in the AW matrix

Based on the proposed hypotheses H1, H2, and H3, adoption promotion should be limited under the current awareness and want. Therefore, this study proposes an adoption gap ratio analysis to explore the gap between adoption and awareness or want. The adoption gap ratio g(x) is defined as follows: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document}\begin{align*}g (x) = \frac {\min (Pr (A (x)), Pr (W (x))) - Pr (U (x))} {\min (Pr (A (x)), Pr (W (x)))} \times 100 \% \tag{5} \end{align*}\end{document}

The range of the adoption gap ratio is between 0% and 100%. Among those who are already aware of or in want of a strategic business unit (e-service), the adoption gap ratio represents the percentage of people who have never used it. Using the proposed adoption gap ratio, a researcher can thus evaluate the effectiveness of adoption promotion more accurately.

Empirical Design

This study applies the AWA gap analysis to evaluate the promotion performance of e-services promoted by the Taiwanese government.

Framework for ubiquitous e-services

The framework of e-services has eight pillars and 12 sub-pillars,⁶⁵ based on the “U-Taiwan Program” structure and integrated reports and research from Japan,⁶⁶ South Korea,⁶⁷ the European Union,^68,69 the United Kingdom,⁷⁰ the United States,^15,16 and other countries.^71–75

Survey method and questionnaire

A stratified simple random sampling with a sample size allocation, proportioned by the population distribution of 23 cities/counties (strata), was used. According to the population distributions of gender and age, post-stratifications were also conducted during the interview process to control the sample structures of these two variables. All p values of the chi-squared homogeneity tests for cities/counties, gender, and age were all greater than 0.05, indicating that the sample distributions of the three characteristics are as homogeneous as the population distribution.

The respondents were asked to express their opinions using the following two statements on a binary scale (1 = yes and 2 = no): (1) Have you ever heard of this kind of e-service? (detailed items are described in Table 2); and (2) Do you want to use this kind of e-service in your daily life? The usage of the e-services (1 = user and 0 = nonuser) was also investigated to estimate the adoption rate of each service.

Table 2.

The Framework for Ubiquitous e-Services

Main pillar	Sub-pillars	Detailed item
Health and care	(1) Digital medical information interactive service	• E-case history
		• Online medical information search
	(2) Digital healthcare service	• E-ambulance
		• On-line register
		• Long-distance medical care
Living environment	(3) Digital living environment and public security information interactive service	• Living environment information
		• Video monitoring system on the road
		• GPS police dispatch system
		• License-plate recognition
		• Police action
	(4) Digital family and home safety service	• Intelligent electric equipment control system
		• Automatic lighting at night
		• SMS of home safety
		• Safety warning
		• On time observation system
Transportation	(5) Digital information interactive service of traffic condition	• Time of bus stop prompted
		• Online transportation information searching
		• GPS service of public transportation
		• Transportation dispatch service
		• Digital safe cars
	(6) Digital transportation service	• Online train or flight tickets ordering
		• Intelligent OBU
		• E-payment service
Learning	(7) Digital learning service	• Online learning
		• E-school bag
		• E-reading machine
Entertainment	(8) Digital tourism and museum guiding service	• Culture center, museum, and sightseeing guide
Government	(9) Digital government service	• E-housekeeper
		• Household, land, and tax information searching
		• ATM payment
		• M reservation by government
		• Online tax
		• Online service of household, land, and tax
		• General people license
Shopping	(10) Digital living and shopping information searching interactive service	• Resumption of products manufacturing and promotion
		• Online life information searching
	(11) Digital shopping and entertainment service	• Mobile payment
		• Online shopping
		• Online booking
		• TiVo
		• Online games
Digital communication	(12) Internet and communication service	• Internet
		• E-mail
		• On time communication
		• Wireless Internet
		• Mobile web
		• Internet telephone
		• Blog

Sample structure

A total of 6,036 Taiwanese respondents aged 15 years and above were interviewed by telephone. The frequency table of the demographic variables is shown in Table 3. The penetration rate of computers at home and Internet access were all high in Taiwan; 84.2% have computers at home and 67.7% have access to the Internet.

Table 3.

Demographic Profile of Respondents

Demographics	Attribute	n	%
Total		6,036	100.0
Gender	Male	2,986	49.5
	Female	3,050	50.5
Age group	15–24	876	14.5
	25–34	1,063	17.6
	35–44	1,053	17.4
	45–54	1,155	19.1
	55–64	848	14.1
	Over 65	1,040	17.2
Education level	Below primary school	924	15.3
	Junior high school	745	12.3
	Senior high school and vocational school	1,824	30.2
	Junior college	858	14.2
	University	1,404	23.3
	Graduate school and above	280	4.6
Marital status	Unmarried	1,770	29.3
	Married or cohabited	3,978	65.9
	Divorce or separation	106	1.8
	bereft of spouse	182	3.0
Do you have children, and how many?	None	1,995	33.1
	1	606	10.0
	2	1,666	27.6
	3	1,139	18.9
	4	383	6.4
	More than 5	246	4.1
Family's average monthly income	Less than 20,000 NT dollars	317	5.3
	20,000–40,000 NT dollars	651	10.8
	40,000–60,000 NT dollars	855	14.2
	60,000–80,000 NT dollars	824	13.7
	80,000–100,000 NT dollars	696	11.5
	100,000–150,000 NT dollars	654	10.8
	150,000–200,000 NT dollars	228	3.8
	More than 200,000 NT dollars	175	2.9
	The income is unstable or uncertain	232	3.8
	Don't know or refuse to answer	1,403	23.2
Personal average monthly income	Less than 15,000 NT dollars	2,160	35.8
	15,000–20,000 NT dollars	400	6.6
	20,000–30,000 NT dollars	859	14.2
	30,000–40,000 NT dollars	816	13.5
	40,000–50,000 NT dollars	605	10.0
	50,000–70,000 NT dollars	378	6.3
	70,000–100,000 NT dollars	137	2.3
	More than 100,000 NT dollars	107	1.8
	Don't know or refuse to answer	573	9.5
Do you have the computer at home?	Yes	5,084	84.2
	No	952	15.8
Do you have access to Internet?	Yes	4,085	67.7
	No	1,951	32.3

Results

Adoption function of awareness and want

The awareness, want, and adoption rates of 12 e-services are shown in Table 4. The hierarchical stages of a customer decision process are correlated. Therefore, this study integrated awareness and want into one variable, AW(x), to deal with the collinear problem. All 12 logistic regressions were significant; that is, the adoption can be expressed as the function of awareness and want. Hypothesis H1 is proven true (Table 5).

Table 4.

The Awareness Rates, Want Rates, Adoption Rates, and Adoption Gap Ratio for the Ubiquitous e-Services and Their AW Matrix Position

e-Service Items (x)	Awareness Pr[A(x)]	Want Pr[W(x)]	Use Pr[U(x)]	Gap ratio g(x)	AW area
(11) Digital shopping and entertainment service	86.3	57.5	47.8	16.9	Super cow
(12) Internet and communication service	85.9	64.5	63.5	1.6	Super cow
(9) Digital government service	85.2	56.6	34.0	39.9	Super cow
(6) Digital transportation service	83.7	58.2	35.7	38.7	Big cow
(4) Digital family and home safety service	80.3	67.7	28.1	58.5	Big cow
(3) Digital living environment and public security information interactive service	78.6	68.3	15.3	77.6	Big cow
(5) Digital information interactive service of traffic condition	75.0	56.1	28.6	49.0	Big cow
(10) Digital living and shopping information searching interactive service	73.8	56.0	38.9	30.5	Big cow
(2) Digital health care service	71.2	56.0	23.8	57.5	Big cow
(1) Digital medical information interactive service	63.5	58.9	25.8	56.2	Big cow
(8) Digital tourism and museum guiding service	62.7	56.5	32.6	42.3	Big cow
(7) Digital learning service	72.2	43.5	23.1	46.9	Adored pet

Table 5.

The Logistic Regressions of e-Services Adoption by the Awareness and Want

	AW (x)			Constant
e-Service Items (x)	B	exp (B)	sig.	B	exp (B)	sig.	Nagelkerke R²
(1) Digital medical information interactive service	2.26	9.63	0.000^*	− 6.68	0.00	0.000^*	0.44
(2) Digital health care service	1.39	4.03	0.000^*	− 4.40	0.01	0.000^*	0.32
(3) Digital living environment and public security information interactive service	1.20	3.31	0.000^*	− 4.69	0.01	0.000^*	0.15
(4) Digital family and home safety service	1.13	3.11	0.000^*	− 3.74	0.02	0.000^*	0.20
(5) Digital information interactive service of traffic condition	1.54	4.67	0.000^*	− 4.54	0.01	0.000^*	0.37
(6) Digital transportation service	1.45	4.28	0.000^*	− 3.95	0.02	0.000^*	0.38
(7) Digital learning service	1.00	2.72	0.000^*	− 3.10	0.05	0.000^*	0.27
(8) Digital tourism and museum guiding service	1.60	4.97	0.000^*	− 4.25	0.01	0.000^*	0.46
(9) Digital government service	1.41	4.10	0.000^*	− 3.85	0.02	0.000^*	0.37
(10) Digital living and shopping information searching interactive service	1.49	4.42	0.000^*	− 3.62	0.03	0.000^*	0.46
(11) Digital shopping and entertainment service	1.52	4.59	0.000^*	− 3.27	0.04	0.000^*	0.48
(12) Internet and communication service	1.84	6.30	0.000^*	− 2.77	0.06	0.000^*	0.61

p < 0.05.

In AW(x), 0 = neither aware of nor in want of; 1 = aware of but not in want of; 2 = not aware of but in want of; 3 = both aware of and in want of.

Adoption rate is positively related to the awareness rate and want rate

The values of exp(B) for 12 logistic regressions were between 2.72 and 9.63 and were all greater than 1. Thus, a one unit increase of AW(x) will result in a multiple-unit increase of use, and the adoption is positively related to awareness and want. Therefore, hypothesis H2 is also proven.

Adoption rate is bound to the awareness rate and the want rate

Except for “Internet and communication service,” all of the p values of the paired proportion test between adoption and awareness or want rates for the 12 e-services were all less than 0.05 (Table 6). This indicates that the adoption rates are significantly less than either the awareness or want rate. Although the difference between the adoption and want rates for “Internet and communication service” is not significant, the want rate is still larger than the adoption rate. Therefore, hypothesis H3 is also proven.

Table 6.

The Difference Tests Between Awareness and Adoption as Well as Between Want and Adoption for 12 e-Services—The Paired Proportion t Test

	Pr [A(x)] − Pr[U(x)]		Pr[W(x)] − Pr[U(x)]
e-Service items (x)	T	Sig.	T	Sig.
(1) Digital medical information interactive service	33.80	0.000^*	29.95	0.000^*
(2) Digital health care service	43.24	0.000^*	30.02	0.000^*
(3) Digital living environment and public security information interactive service	67.03	0.000^*	55.27	0.000^*
(4) Digital family and home safety service	45.02	0.000^*	34.37	0.000^*
(5) Digital information interactive service of traffic condition	39.79	0.000^*	24.33	0.000^*
(6) Digital transportation service	37.99	0.000^*	18.55	0.000^*
(7) Digital learning service	45.21	0.000^*	20.06	0.000^*
(8) Digital tourism and museum guiding service	25.18	0.000^*	20.33	0.000^*
(9) Digital government service	41.25	0.000^*	18.99	0.000^*
(10) Digital living and shopping information searching interactive service	27.04	0.000^*	13.85	0.000^*
(11) Digital shopping and entertainment service	27.39	0.000^*	7.38	0.000^*
(12) Internet and communication service	14.48	0.000^*	0.69	0.246

p < 0.05.

AWA gap analysis

In the AW matrix analysis, the 12 e-services were positioned in the super cow, big cow, and adored pet areas (Fig. 2). The awareness rates of all 12 e-services were all considered high or popular, indicating that the present government efforts in promoting awareness are effective. The values of the adoption gap ratios for 12 e-services range from 1.6% to 77.6% (Table 4). The adoption gap ratio analysis shows that while there is almost no room to promote the adoption of “Internet and communication service,” unless awareness and want are first raised, there is a huge room to promote the adoption of “digital living environment and public security information interactive service.”

FIG. 2.

The AW matrix with adoption gap ratio analysis for 12 e-services.

Discussion and Conclusion

According to the AWA gap analysis, three e-services, including “digital shopping and entertainment service,” “Internet and communication service and digital government service,” were located in the super cow area. The gap between adoption and awareness or want for “Internet and communication service” is small and not significant, indicating that the adoption rate nestled up against the wants of the people.

Eight of the e-services related to personal and family security, health care, e-transportation, and leisure activity were located in the big cow area with awareness and want rates at the same high level. This indicates that the promotional efforts of the government were optimal. It must be noted that “digital living environment and public security information interactive service” should be ranked first when establishing the order of priority for promotion activities because it had the highest gap ratio at 77.6%.

The “digital learning service” was located in the adored pet area. This service may cause budgetary waste. Hence, shifting the budget to other services should be considered if want rate does not rise. The gap ratio of “digital learning service” was also large at 46.9%, which indicated that the adoption rate also had room to rise.

This study proposed the AWA gap analysis and provided empirical evidence that it is workable. For managers in business, government, or any kind of institution, the AWA gap analysis provides a simple and clear method for exploring and comparing the strategic business units or projects. Moreover, it helps managers reallocate their budget given the limited resources from an objective and customer-oriented view.

Suggestions for Future Research

In future research, the AWA gap analysis could be extended to analyze gaps among different characteristics, such as gender, age groups, and education levels, or among different usage behavior groups and different segmentations. It could also be used to compare and explore digital divides thoroughly among generations.

Footnotes

Disclosure Statement

No competing financial interests exist.

References

Stats

. Internet usage statistics—The Internet big picture world internet users and population stats. www.internetworldstats.com/stats.htm. 2009 Jun. 30.

Kim

, Jeon

, Bae

. Innovation patterns and policy implications of ADSL penetration in Korea: A case study. Telecommunications Policy, 2008; 32:307–25.

Katz

, Blumler

, Gurevitch

. 1974. Utilization of mass communication by the individual. Blumler

, Katz

. The uses of mass communications: Current perspectives on gratifications research. Beverly Hills, CA: Sage, 19–32.

Pascoe

, Sunderam

, Varshney

et al. Middleware enhancements for metropolitan area wireless Internet access. Future Generation Computer Systems, 2002; 18:721–35.

Rupp

, Smith

. Mobile commerce: New revenue machine or black hole? Business Horizons, 2002; 45:26–9.

, Wang

. What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management, 2005; 42:719–29.

Fishbein

, Ajzen

. 1975. Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.

Gefen

, Karahanna

, Straub

. Inexperience and experience with online stores: The importance of TAM and trust. IEEE Transactions on Engineering Management, 2003; 50:307–21.

Pavlou

. 2002. What drives electronic commerce? A theory of planned behavior perspective. Paper presented at Best Paper Proceedings of the Academy of Management Conference, Denver, CO.

10.

Liao

, Chen

, Yen

. Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model. Computers in Human Behavior, 2007; 23:2804–22.

11.

Lin

, Shih

, Sher

. Integrating technology readiness into technology acceptance: The TRAM model. Psychology & Marketing, 2007; 24:641–57.

12.

Parasuraman

. Technology readiness index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2000; 2:307–20.

13.

Picot

, Wernick

. The role of government in broadband access. Telecommunications Policy, 2007; 31:660–74.

14.

Kim

, Lee

, Menon

. The role of the uncertainty of the performance of knowledge in knowledge management. KAIST Business School Working Paper Series No. 2008-016 2008 Dec. 15.

15.

FCC. The Federal Communication Commission's plan for 2008 to 2012. www.fcc.gov/omd/strategicplan/InfoTechnologyPlansandReports/itsp2008-2012.pdf. 2009 Apr. 30.

16.

FCC. The Federal Communication Commission's plan for 2009 to 2014. http://hraunfoss.fcc.gov/edocs_public/attachmatch/DOC-285705A1.pdf. 2009 Apr. 30.

17.

MIC. 2005. Information and communications in Japan 2005 feature: Stirrings of u-Japan: The Ministry of Internal Affairs and Communications.

18.

MIC. u-Japan policy. www.soumu.go.jp/menu_02/ict/u-japan_en/index.html. 2008 Aug. 30.

19.

Korea.net. Personal u-learning terminals to be piloted next year. www.korea.net/News/News/NewsView.asp?serial_no=20061214034. 2008 Jul. 11.

20.

Posfai

, Fejer

. The eHungary programme 2.0. Innovation: The European Journal Of Social Science Research, 2008; 21:407–15.

21.

CPA. Advanced broadband integrated e-service network (ABIEN) http://duct.cpami.gov.tw/intro/index_01.htm. 2009 Aug. 31.

22.

STAG. e-Taiwan program. www.etaiwan.nat.gov.tw/content/application/etaiwan/english/index.php. 2009 Feb. 28.

23.

NICI. M-Taiwan program. www.nici.nat.gov.tw/content/application/nici/egeneralb/guest-cnt-browse.php?sel=m_intro&cnt_id=639. 2008 Sep. 15.

24.

NICI. U-Taiwan Program for Ubiquitous Society. www.utaiwan.nat.gov.tw/content/application/utaiwan/generalb/guest-cnt-browse.php?ordinal=10020026&cnt_id=1762. 2008 Sep. 15.

25.

Rust

, Lemon

. E-service and the consumer. International Journal of Electronic Commerce, 2001; 5:85–101.

26.

Turban

, King

, Lee

et al. 2002. Electronic commerce: A managerial perspective. Upper Saddle River, NJ: Prentice Hall.

27.

Reynolds

. 2000. The complete e-commerce book: Design, build and maintain a successful web-based business, 20. New York: CMP Books.

28.

Boyer

, Hallowell

, Roth

. E-services: Operations strategy—A case study and a method for analyzing operational benefits. Journal of Operations Management, 2002; 20:175–8.

29.

Hoffman

, Bateson

JEG

. 1997. Essentials of service marketing. Fort Worth, TX: Dryden Press, 7.

30.

Rowley

. An analysis of the e-service literature: Towards a research agenda. Internet Research, 2006; 16:339–59.

31.

Voss

. Developing an eService strategy. Business Strategy Review, 2000; 11:21–33.

32.

de Ruyter

, Wetzels

, Kleijnen

. Customer adoption of e-service: An experimental study. International Journal of Service Industry Management, 2001; 12:184–207.

33.

Wareham

, Zheng

, Straub

. Critical themes in electronic commerce research: A meta-analysis. Journal of Information Technology, 2005; 20:1–19.

34.

Santos

. E-service quality—A model of virtual service dimensions. Managing Service Quality, 2003; 13:233–47.

35.

, Chen

. An extension of Trust and TAM model with TPB in the initial adoption of on-line tax: An empirical study. International Journal of Human–Computer Studies, 2005; 62:784–808.

36.

Wonglimpiyarat

, Yuberk

. In support of innovation management and Roger's Innovation Diffusion theory. Government Information Quarterly, 2005; 22:411–22.

37.

Bettman

. 1979. An information processing theory of consumer choice. Reading, MA: Addison-Wesley.

38.

Strong

Jr.

1925. The psychology of selling and advertising. New York: McGraw-Hill.

39.

Strong

Jr.

Theories of selling. Journal of Applied Psychology, 1925; 9:75–86.

40.

Barry

, Howard

. A review and critique of the hierarchy of effects in advertising. International Journal of Advertising, 1990; 9:121–35.

41.

Barry

. In defense of the hierarchy of effects: A rejoinder to weilbacher. Journal of Advertising Research, 2002; 42:44–7.

42.

Hauser

, Urban

. A normative methodology for modeling consumer response to innovation. Operations Research, 1977; 25:579–619.

43.

Bruyn

, Lilien

. A multi-stage model of word-of-mouth influence through viral marketing. International Journal of Research in Marketing, 2008; 25:151–63.

44.

Wright

. Factors affecting the cost of airplanes. Journal of Aeronautical Sciences, 1936; 3:122–8.

45.

Hellman

, Burst

. 2004. The customer learning curve—Creating profits from marketing chaos. Mason, OH: Thomson South-Western.

46.

Rogers

. 1962. Diffusion of innovation. New York: Free Press.

47.

Haider

, Kreps

. Forty years of diffusion of innovations: Utility and value in public health. Journal of Health Communication, 2004; 9:3–11.

48.

Oser

, Roman

. A categorical typology of naltrexone-adopting private substance abuse treatment centers. Journal of Substance Abuse Treatment, 2008; 34:433–42.

49.

Eng

, Quaia

. Strategies for improving new product adoption in uncertain environments: A selective review of the literature. Industrial Marketing Management, 2009; 38:275–82.

50.

Rosen

, Dorsett

, Jack

. A proline-rich region in the zeste protein essential for transvection and white repression by zeste. Genetics, 1998; 148:1865–74.

51.

Colarelli O'Connor

. Market learning and radical innovation: A cross case comparison of eight radical innovation projects. Journal of Product Innovation Management, 1998; 15:151–66.

52.

Ottum

, Moore

. The role of market information in new product success/failure. Journal of Product Innovation Management, 1997; 14:258–73.

53.

Prins

, Verhoef

. Marketing communication drivers of adoption timing of a new e-service among existing customers. Journal of Marketing, 2007; 71:169–83.

54.

Steenkamp

J-BEM

, Gielens

. Consumer and market drivers of the trial probability of new consumer packaged goods. Journal of Consumer Research, 2003; 30:368–84.

55.

McGuire

. 1969. An information-processing model of advertising effectiveness. Symposium on Behavioural and Management Science in Marketing, University of Chicago, IL.

56.

McGuire

. 1978. An information-processing model of advertising effectiveness. Davis

, Silk

. Behavioral and management science in marketing. New York: Ronald Press, 156–80.

57.

Lavidge

, Steiner

. A model for predictive measurements of advertising effectiveness. Journal of Marketing, 1961; 25:59–62.

58.

Preston

. The association models of the advertising communication process. Journal of Advertising, 1982; 11:3–15.

59.

Preston

, Thorson

. 1983. Challenges to the use of hierarchy models in predicting advertising effectiveness. Paper presented at the Annual Convention of American Academy of Advertising.

60.

Robertson

. 1971. Innovative behavior and communication. New York: Holt.

61.

Wolfe

, Brown

, Thompson

. 1962. Measuring advertising results. New York: National Industrial Conference Board.

62.

Rogers

. 1983. Diffusion of innovation, 3rd. New York: Free Press.

63.

Kolter

. 1997. Marketing management—Analysis, planning, implementation and control, 9th. New York: Prentice-Hall.

64.

Mercer

. A two-decade test of product life cycle theory. British Journal of Management, 1993; 4:269–74.

65.

Liang

, Peng

, Chi

. 2009. The awareness and want analysis of ubiquitous e-services in Taiwan. Paper presented at NISS2009 New Trends in Information and Service Sciences, Beijing, China.

66.

Fujii

, Ueda

, Minoh

. Looking for objects service in ubiquitous home. Journal of the National Institute of Information and Communications Technology, 2007; 54:175–81.

67.

Kim

. Ubiquitous dream hall sometimes shows future coming too close. www.korea.net/News/News/NewsView.asp?serial_no=20070406023. 2008 Aug. 30.

68.

CORDIS. Smart clothes: Textiles that track your health. http://cordis.europa.eu/ictresults/index.cfm/section/news/tpl/article/BrowsingType/Features/ID/89605. 2008 Aug. 30.

69.

CORDIS. Transforming buses into mobile sensing platforms. http://cordis.europa.eu/ictresults/index.cfm/section/news/tpl/article/BrowsingType/Features/ID/89747. 2008 Aug. 31.

70.

BUSAN. Mobile RFID making busan city tour fun and easy. http://english.busan.go.kr/community/news/news_01.jsp?nSelected=2&command=view&sn=736&page=10&nowBlock=1&board_code=8&d_code=news_01&select=title&key=. 2008 Aug. 30.

71.

BNET. Eprovenance unveils the “intelligent bottle” for fine wine. http://findarticles.com/p/articles/mi_m0EIN/is_2008_March_26/ai_n24948440. 2008 Aug. 30.

72.

Bieliková

, Krajcovic

. Ambient intelligence within a home environment. ERCIM News, 2001; 47:12–13.

73.

Epractice.eu. DE: New eService helps find the right medicine. www.epractice.eu/document/4761. 2008 Sep. 15.

74.

TNS. New future in store—How will shopping change between now and 2015. www.tnsglobal.com/search-results.aspx. 2008 Sep. 15.

75.

Center u. Agricultural product direction tracing experiment—PJN 001. www.uidcenter.tw/projects-1.html. 2008 Jan. 25.