978-1-61284-952-2/11/$26.00 ©2011 IEEE 919 IEEE Int'l Technology Management Conference

Analysis of Factors Affecting the Adoption of Smartphones

Young Mo Kang, Chanwoo Cho and Sungjoo Lee Ajou University

Suwon, South Korea princeps@ajou.ac.kr

Abstract

The mobile-phone market has changed from a new purchase market to a replacement market when it is matured. At the same time smartphones come into the spotlight as an alternative of feature phones in the mobile-phone market. Since the characteristics of ‘smartphones’ are greatly different from those of ‘feature phones’ in that more technologies are incorporated and more functionalities are provided with users by smartphones, factors that can affect the adoption of smartphones and feature phones might not be the same.

With all the importance of market change from feature phones to smartphones, most of the previous research on smartphones has been conducted on the assumption that smartphone is used as a specialized tool for a particular purpose, such as smartphones for logistics or smartphones for medical use. However, more and more consumers adopt smartphones as a tool for general use in their everyday life, it is worth emphasizing more on general consumers, which regard smartphone as a next-generation product of feature phone.

And so this study purposes to investigate factors affecting the adoption of smartphones, focusing on general consumers. To achieve the purpose, we adopted TAM (Technology Acceptance Model), which is one of the most popular methods to analyze the acceptability of new technology, and used SEM (Structural Equation Model) for an empirical analysis. Our empirical analysis through the online and offline survey shows that two types of users perceived differently for functional attributes on smartphones. The research results is expected to understand the characteristics of potential market for smart phones and ultimately support diffusion of smartphones, design of new product, and the development of marketing strategy in the mobile phone market.

Keywords: technology acceptance model, smartphone, comparison analysis, mobile phone market, general users

1. Introduction

The mobile-phone market maintained rapid growth until 2002, at which point the growth rate of the mobile phone is considered to have reached maturity in the market [1]. Since then, the focus has been on mobile-phone replacement rather than first-time purchase, and the profile of buyers is changing. These changes and the increasing deployment of the smartphone have affected the mobile-phone market, and the general consumer interest in the smartphone is growing. In the mobile industry, there is no clear industry-standard definition of smartphone, but generally, the PDA (Personal Digital Assistant) and the general mobile phone combine the functions of various applications, using the Internet and the portable PC[2]. These smartphones have been the attractive

devices for various users[3]. Mobile phones are classified according to their features, and a clear definition of each is under review. This study will use the feature phone as the term to describe mobile phones that are not smartphones.

According to the Fortune sit, a US business magazine, the researching group called IDC said that the global market of smartphones increased about fifty-six percent in 2010 (54.7 million from last year’s 34.9 million). In addition, new sales will reach sixty percent in 2014, according to Pyramid Research. This estimates the cumulative share of smartphones in 2009 in excess of 16.3 percent, which means the smartphone market is growing at a rapid pace. These changes in the mobile phone market and the increasing deployment of smartphones are remarkable. It is crucial for sellers to understand the diffusion process of the increasingly popular smartphone to establish customers—including both potential customers and current users—and to understand the required next steps in marketing the smartphone.

Much research has been conducted from the customers’ perspective to analyze the diffusion of the smartphone, but there have been various limitations in such research. First, most of the previous research on smartphones assumed that the smartphone would be used as a specialized tool for a particular purpose, such as for logistics or medical use [4][5][6]. Smartphone that was monopolized by business man and early adopter is diffused throughout the general consumer, and it has increased to meet a growing consumer demand for multimedia and game and so on[7]. People who use smartphones for work will have different purposes than the general buyer, however. Therefore, these previous studies were limited in their reflection of the views of smartphone users. Second, previous research used experts’ opinion in analyzing customers’ buying decision process [8][9]. In the early stages of introducing the smartphone to customers, expert opinion is very important to promote smartphones and to establish marketing strategies. However, it is necessary to direct a study to customers’ comments when smartphones are sold to the general consumer.

Therefore, the purpose of this study is to analyze factors in the purchase decision of smartphone for mobile-phone users, considering the market change from feature phones to smartphones. Smartphone is information system included various features. To achieve this purpose, we adopted TAM, which is fit for the acceptability assessment in the information system development. To achieve this purpose, we adopted TAM, which is one of the most popular methods for analyzing the acceptability of new technology. In this study, primary attributes for applying TAM were derived from survey and twelve hypotheses in the model were established from the existing literature on mobile phones and internets that are the basic technologies in smartphones. Finally, these

hypotheses were verified by SEM (Structural Equation Model), which will help understand the characteristics of the potential market for smartphones. The research results are expected to ultimately support the diffusion of smartphones, the design of new products, and the development of marketing strategies in the mobile-phone market [10].

The remainder of this paper consists of five parts. First, Section 2 explains the basics of TAM and various functional attributes of smartphones that are used to develop a TAM in this research. Then, Section 3 describes the overall research process, after which the first survey results to establish the research model and second survey results to verify the model by empirical analysis are summarized in Section 4 and Section 5. Finally, this paper concludes with discussions and closing remarks in Section 6.

2. Literature Review

2.1 Technology Acceptance Model

Technology Acceptance Model (TAM) was proposed for explaining and predicting consumer acceptance of an information system, and it is designed specifically to interpret the acceptance process of information technologies [11]. The original TAM consists of five components, which include perceived usefulness (PU), perceived ease of use (PEOU), attitude toward using, behavioral intention (BI) to use, and actual system use [12]. TAM assumes that whether to adopt a particular technology is determined by two key factors: PU and PEOU for the technology (see Figure 1). Here, PU is defined as the degree to which a person believes that using a particular technology would enhance his or her job performance, while PEOU is defined as the degree to which a person believes that using a particular technology would be free of effort [11]. On the other hand, BI is defined as future behavior of individuals, which entails subjective probability as it relates to actual behavior [13]. Actual behavior is the determinant factor in taking specific action, and BI relates to actual behavior [14][15]. Subsequent research concludes that the attitude variable has weak predictors of BI [16]. Consequently, much of the subsequent research that used TAM omitted the attitude variable.

Figure 1. The concept of basic TAM

2.2 Functional attributes of the smartphone

A product consists of functional attributes that meet personal needs, such as satisfaction and expectation etc. [17][18]. Each consumer accepts different functional attributes [19][20][21][22]. If a consumer does not receive satisfaction from the influential functional attributes, he or she will not use the product. In addition, a consumer estimates the value of functional attributes of the product compared to that of other products to decide what product to use. Therefore, it is important to derive influential functional attributes. To identify functional attributes of the smartphone,

this study focused on two relevant technologies and reviewed their functional attributes.

The first technology is wireless Internet. As mobile communication technology has developed, people have become able to use Internet services at any time, no matter where they are. Because the smartphone provides the wireless Internet function, which is a distinguishing feature compared to the feature phone, a variety of services have been activated, and the market has grown. In a similar way, wireless Internet technology has developed with the development of smart phones. Therefore, functional attributes of wireless Internet should be considered in identifying those of smartphone. The second relevant technology is the mobile service technology, whose representative characteristics are portability and mobility. The development of mobile services will result in more widespread use of the smartphone. In addition, mobile convergence, which enables customer to use a variety of features and services in various types of equipments such as smart phones, will lead to the development of mobile service technology. Therefore, the functional attributes of mobile services are associated with those of the smartphone.

3. Research Framework

3.1 The overall research process

The overall research process is divided into four steps. Firstly, the various functional attributes of smart phones are identified from literature reviews to develop a research model. Among them, the top five functional attributes that are considered important aspects in purchase or replacement of smartphones are selected by survey respondents as a result of the first survey. Based on the top five functional attributes, a research model with twelve hypotheses was established. Finally, the research model was verified with the second survey. Basic statistical analysis was conducted using SPSS 19.0, and the research model was verified by SEM using Amos (Analysis of Moment Structure).

Figure 2. The overall research process

3.2 Identification of functional attributes

To develop TAM, this study organized the first survey as a field poll. We presented 32 functional attributes identified from literature reviews and asked the respondents to mark their importance using the 5-point Likert scale. Target respondents were undergraduate and graduate students, who are main users of mobile phones, and one hundred responses were collected from 24th to 30th of December 2010. The demographic characteristics of survey results are summarized in table 1. Among the respondents, there were

seventy males and thirty females; fifty-one respondents used feature phones and forty-nine used smartphones.

Table 1. Demographics of respondents in the first survey Variables Frequency

Gender Male 70

Female 30

Types of mobile phone

Feature-Phone

51

Smart-Phone

49

The survey results were analyzed using SPSS 19.0, and

the five primary functional attributes include wireless Internet, design, multimedia, application, and after service (see Table 2).

Table 2. Derived primary functional attributes

3.3 Development of a research model and hypotheses

In TAM, the five primary functional attributes were defined as the independent variables and assumed that they affect to BI of smartphones through PU and PEOU as shown in Figure 3. Based on the model, twelve hypotheses were developed to be verified.

Figure 3. TAM for smartphones

First, a hypothesis regarding Wireless Internet was established. The wireless Internet can be defined as the services that provide digitized information or contents to users, which can eliminate time constraints or spatial constraints [23][24]. According to the research of Sarker and Wells [25], wireless Internet greatly affected mobile service acceptance. Therefore, this study establishes hypotheses for wireless Internet as follows:

[Hypothesis 1-1] Wireless Internet influences on PU of the smartphone positive. [Hypothesis 1-2] Wireless Internet influences on PEOU of the smartphone positive.

Second, a hypothesis for Design was established. The design has two aspects: (1) inherent design and colors of mobile phones, and (2) detailed elements of design regarding the entire appearance, color, harmony of appearance, shape of keypads, etc. These design elements are significant in the entire balance, proportion, and convenience of mobile phones. Leung and Wei [26] identified the fact that the mobile phone is utilized as a means of expression for the user. Therefore, the hypotheses for design are proposed as follows:

[Hypothesis 2-1] Design influences on PU of the smartphone positive. [Hypothesis 2-2] Design influences on PEOU of the smartphone positive.

Third, hypotheses for Multimedia were constructed. The multimedia refers to the various media functions, such as camera, mp3, and games. As many functions have been integrated into one mobile device, the multimedia function has become a core function that diminishes time or spatial constraints and supports ease of use [27]. Therefore, this research suggests the following hypotheses for multimedia:

[Hypothesis 3-1] Multimedia influences on effect PU of smartphone positive. [Hypothesis 3-2] Multimedia influences on effect PEOU of smartphone positive.

Following this, hypotheses for Application were established. The application refers to contents that are executed in the mobile equipment, and we can determine the supply of diverse applications that meet users’ BI. These are the applications that the users feel provide convenience and usefulness, and they raise the level of acceptability among users. Therefore, the following hypotheses are suggested below.

[Hypothesis 4-1] Application influences on PU of the smartphone positive. [Hypothesis 4-2] Application influences on PEOU of the smartphone positive.

Finally, hypotheses for After Service were suggested. The after service can be defined as services that improve product sales and it includes various services such as users’ claims, claim treatment and onsite service to users. These provide ease and convenience to the user and improve acceptance of the product among users. Therefore, the following hypotheses for after service are suggested below.

[Hypothesis 5-1 After service influences on PU of the smartphone positive.

After hypotheses for the five functional attributes were determined, hypotheses for PU and PEOU were also established. PU can be defined as the degree of belief of users that the use of technology will help to improve their performance, and it relates to the effect of mission, productivity, and importance of information technologies

utilized in tasks [28]. PEOU can be defined as the degree of belief of users that specific technologies are not difficult to use, and it refers to ease of learning. According to Adams et al. [29], PEOU significantly affects BI, and according to the research of Igbaria et al. [30], this effect is more significant than that of PUs. Several existing studies verified that PEOU is the preceding variable of PU, which means that users prefer to use technologies that are easy to use and that those technologies help to improve user’s performance [16][31][32][33]. Therefore, the following hypotheses are established.

[Hypothesis 6-1] The PEOU of smartphone service influences on the BI of the smartphone. [Hypothesis 6-2]The PEOU of smartphone service influences on the PU of the smartphone. [Hypothesis 6-3]The PEOU of smartphone service influences on the acceptance of the smartphone among users.

4. Empirical Analysis

4.1 Data

The second survey for verification of the hypotheses was conducted in the same manner as the first survey. It was executed for 206 undergraduate and graduate students in Korea using the 7-point Likert scale, and 200 available responses were used in this study. As Korean market is rapidly changing from feature phones to smartphones, the selected samples seem suitable for this research purpose. Table 3 shows the demographic characteristics of the respondents who were involved in the second survey. Among the respondents, there were 153 males and 47 females; 95 respondents used feature phones, and 105 used smartphones.

Table 3. Demographics of respondents in the second survey

Variables Statistics

Frequency Percentage(%)

Gender Male 206 78.6

Female 56 21,4

Types of mobile phone

Feature-Phone

130 49.6

Smart-Phone

132 50.4

Table 4. Operational definition of variables

4.2 Operational definition and measure of variables

With only a conceptual definition of variables for the functional attributes, the measurement of practices is not easy to execute. Therefore, based on questionnaires regarding each functional attribute, operational definition for research variables were developed based on the existing studies. Table 4 shows the operational definition for the variables. Wireless Internet was defined as the speed of Internet and the limit of scope for use, which consists of four subordinates. Design

was defined as the inherent design and exterior color of smartphone, having three subordinate. Multimedia was defined as ease of use for playing music or video, and it consists of five subordinates. Application was redefined as convenience of use of applications, and it has three subordinates. Finally, after service was redefined as the quality and terms of guarantee of posterior services, and it consists of three items.

4.3 Validity and reliability test for measures

Prior to verifying the hypotheses, the test for validity and reliabilty of the measurements is needed. To test validity, this research used the advanced exploratory factor analysis for functional attributes and beliefs. The exploratory factor analysis refers to an analysis method that has exploratory purposes, and it can be utilized in studies that are not systematized theoretically. To extract the factors explored, the principal component factor analysis and varimax factor rotation method were used. As a result of the analysis, twenty-five questionnaires relating to five functional attributes and three beliefs were loaded to each factor, as demonstrated in Table 5, and the validity of measurements was confirmed. The variables measured with other items (rather than isolated) need to be tested for reliability to verify that the subordinates suitably reflect each variable [34]. All subordinates fulfilled the standard of Cronbach’s alpha coefficient, meaning that the value of the coefficient was above 0.7 [35], and thus reliability was verified.

Table 5 The results of factor analysis

5. Analysis Results

5.1 Fitness test for model and data

To verify the hypotheses of TAM, SEM, which considers influences between functional attributes, was used. SEM has

advantages over regressions when observed variables may have some errors or when relationships between unobserved variables should be analyzed [36], and thus adopted in this research. The AMoS 19.0 was used to test the fitness between the research model and the collected data of this study. There is no absolute standard of analysis for fitness of SEM, and because of the sensitiveness of the chi-square coefficient to the distribution of observed variables and to the size of samples, several fitness indexes should be considered concurrently in the test [37][38][39][40]. Therefore, this study used all the following indexes, CMIN, DF, CMIN/DF, p-value, GFI, AGFI, TLI, CFI, and RMSEA in the fitness test. With most of these indexes, a higher value indicates better fitness, but with CMIN/DF, p-value, and RMSEA, a lower value indicates better fitness. Table 6 presents the fitness of the proposed research model, and all measures fulfill the ideal level and moderate level of value, so the research model of this study can be regarded as acceptable for this experiment.

Table 6 The results of the fitness test

5.2 Path coefficient analysis

Following the fitness test of the research model, the significance of the defined paths of TAM was analyzed. The analysis results are shown in figure 6. According to these results, most of the functional attributes affect PU and PEOU except [Hypothesis 2-2]. Additionally, only [Hypothesis 6-2] was rejected. These results mean that design affects negative effects to PEOU, and PEOU affects negative effects to PU, although other functional attributes and beliefs affect positive effects to beliefs and BI.

5.3 Discussions

This study organized two rounds of survey to analyze the functional attributes considered when users purchase or replace smartphones. A research model was developed based on TAM and SEM was used to test the model. According to the empirical analysis, the four functional attributes - wireless Internet, multimedia, application, and after services - were proven to affect PU and PEOU, but design affected only PU. This shows that superior design can improve convenience, while not being connected to an increase in usefulness. In the case of ease of use, the technical performance of the smartphone is totally different from that of the feature phone, which means that design cannot determine ease of use of the smartphone. However, after smartphones are more widely used by the general population, it is expected that design will significantly affect ease of use.

The analysis showed that PU affects BI directly, whereas PEOU does not. Actually, most of previous research has conflicted in terms of conclusions regarding PU and PEOU. In most of the previous research, PEOU directly affects BI; however, only PU affects BI in other research [28][31][41]. By Adams et al. [29], an emphasis on PEOU is not proper when preceding variables for using an information system are defined. Further, according to Szajna [33], if users do not have enough experience in the use of information systems, PEOU increasingly affects BI. In the results of this research, PEOU does not influence BI directly, because the majority of respondents use smartphones.

Figure 6 The results of the path coefficient analysis

6. Conclusion

This research aims to investigate factors affecting the adoption of smartphones using TAM, focusing on general consumers. In this study, there are two notable facts regarding functional attributes and beliefs. First, this research identified functional attributes that are considered by users who purchase or replace smartphones and analyzed the ways these attributes affect BI of smartphones. Second, we also found that PU significantly affects BI directly, while PEOU does not in adopting smartphones. This research is one of the first attempts to investigate technology acceptance of smartphones for general use in the mobile phone market. In particular, it reflects customer opinion, not expert opinion directly to analyze the adoption of smartphones. The research results are useful in interpreting the characteristics of the market with regard to smartphones and customers, and further can support the design of new products and the development of marketing strategies.

In spite of these contributions, however, this paper has two limitations and future research is needed. First, data collection was limited to Seoul and Kyungki areas in Korea and the ratio of smartphone users to feature phone users in our data was different with actual ratio in the Korean mobile market. These limitations can cause generality problems. Therefore, in future research, the data collection process should be elaborated using hierarchical random sampling method. Also, the behavior of smartphone users and feature phone users may be different and two different TAMs, one for potential customers and the other for real users can be developed to reflect the distingushed characteristics of the two groups.

Acknowledgment

This research was supported by Basic Science Research Program through the National Research Foundation of Korea

(NRF) funded by the Ministry of Education, Science and Technology (No.2009-0089674).

References [1] Gartner Group (2002), Year Tear-End 2001, Fourth

Quarter 2001 Mobile Terminal Market Shares, 3(7) [2] P. Yangil, J. V. Chen (2007), Acceptance and adoption of

the innovative use of smartphone, Industrial Management and Data System, 107(9), 1349-1365

[3] J. Cheng, S. H. Y. Wong, H. Yang, S. Lu (2007), SmartSiren: virus detection and alert for smartphones. Proceedings of the 5th international conference on Mobile systems, applications and services, 258-271

[4] M. J. Succi and Z. D. Wa;ter (1999), Theory of user acceptance of information technologies: An examination of health care professionals, IEEE

[5] L. F. Pitt, M. Parenet, I. Junglas, A. Chan, S. Spyropoulou (2010), Integrating the Smartphone into a Sound Environmental Information Systems Strategy: Principles, Practices and a Research Agenda, Journal of Strategic Information Systems

[6] B. M. Dala-Ali, M. A. Lloyd, Y. Al-Abed (2010), The uses of the iPhone for surgeons, The Surgeon, Journal of the Royal Colleages of Surgeons of Edinburgh and Ireland, 9(1), 44-48

[7] J. V. N. Steven (2003), OSs battle in the smart-phone market, IEEE Computer, 36(6), 10-12

[8] J. V. Chen, D. C. Yen, K. Chen (2009), The acceptance and diffusion of the innovative smart phone use: A case study of a delivery service company in logistics, Information & Management, 46(4), 241-248

[9] A. Holzer, J. Ondrus (2010), Mobile application market: A developer’s perspective, Telematics and Informatics, 28(1), 22-31

[10] V. Venkatesh and F. D. Davis (1996), A model of the antecedents of perceived ease of use: Development and test, Decision Sciences, 27(3), 451-481

[11] F. D. Davis (1989), Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS Quarterly, 13(3), 319-339

[12] J. H. Wu, S. C. Wang (2005), What drives mobile commerce? An empirical evaluation of the revised technology acceptance model, Information & Mangement, 42, 719-729

[13] J. E. Engel and R. D. Blackwell (1982), Consumer Behavior, 4th ed., New York: The Dryden Press

[14] J. A. Howard and J. N. Seth (1973), A Theory of Buyer Behavior, in H.H. Kassarjian and T.S. Roberton (eds.) Persepectives in Consumer Behavior, Glenview, Illinois: Scott, Foresman and Company, 467-487

[15] I. Ajzen, and M. Fishbein (1980), Understanding Attitudes and Predicting Behavior, Englewood Cliffs, NJ.: Prentice-Hall Inc

[16] S. Taylor, and P. A. Todd (1995), Understanding Information Technology Usage: A Test of Competing Models, Information System Research, 6(2), 144-176

[17] W. L. Wilkie and E.A. Pessemier (1973), Issues in Marketing Use of Multi-Attitude Models, Journal of Marketing Research, 10(11), 428-441

[18] R. Ferber (1974)., Handbook of Marketing Research, New York: McGraw-Hill Book Company

[19] J. H. Myers and M. I. Aplert (1968), Determinant Buying Attitude: Meaning and Measurement, Journal of Marketing, 32(4), 13-20

[20] C. M. Crawford (1983), New Products Analysis, Homewood, III: Richard D. Irwin, Inc

[21] D. J. Reibstein, E. G. Basteson, and W. Bouling (1987), Conjoint Analysis Reliability: Empirical Findings, 87-102

[22] P. E. Green and V. Srinivasan (1990), Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice, Journal of Marketing, 54(4), 1-19

[23] G. Ng-Kruelle, P. Swatman, D. Rebne, and F. Hampe (2002), The Price of Convenience: Privacy and Mobile Commerce, Quarterly Journal of Electronic Commerce, 3(3), 273-285

[24] E. Turban, D. King, J. Lee, M. Warkentin, and M. Chung (2002), Electronic commerce, A Managerial Perspective, 2nd Ed., New Jersey, Pearson Education

[25] S. Sarker and J. Wells (2003), Understanding Mobile Handheld Device Use and Adoption, Communications of the ACM, 46(12), 35-40

[26] L. Leung, and R. Wei (2000), More Than Just Talk on the Move: Uses and Gratifications of the Cellular Phone, Journalism & Mass Communication Quarterly, 77(2), 308-320

[27] Y. F. Chang, C.S. Chen, H. Zhou (2009), Smart phone for mobile commerce, Computer Standards & Interfaces, 31(4), 740-747

[28] F. D. Davis, R. Bagozzi, and P. R. Warshaw (1989), User Aceeptance of Computer Technology: A Comparison of Two Theoretical Models, Management science, 35(8), 982-1003

[29] D. A. Adams, R. R. Nelson, and P. A. Todd (1992), Perceived Usefulness, Ease of Use, And Usage of Information Technology: A Replication, MIS Quarterly, 6(2), 227-247

[30] M. Igbaria, N. Zinatelli, P. Cragg, and A. L. M. Cavaye (1997), Personal Computing Acceptance Factors in Small Firms: A Structural Equation Model, MIS Quarterly, 21(3), 279-305

[31] K. Mathieson (1991), Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior, Information Systems Research, 2(3), 173-191

[32] P. Y. K. Chau (1996), An Empirical Assessment of a Modified Technology Acceptance Model, Journal of Management Information Systems, 13(2), 185-204

[33] B. Szajna (1996), Empirical Evaluation of the Revised Technology Acceptance Model, Management Science, 42(1), 85-92

[34] G. A. Churchill (1979), A Paradigm for Developing Better Measures of Marketing Constructs, Journal of Marketing Research, 16(2), 64-73

[35] J. C. Nunnally (1967), Psychometric Theory, New York: McGraw-Hill

[36] A. S. Goldberger (1973), Structural Equation Models in

the social sciences, Econometrica, 979-1001 [37] P. Bentler, and D. Bonnett (1980), Significance Tests

and Goodness of Fit in the Analysis of Covariance Structure, Psychological Bulletin, 88(3), 588-606

[38] C. Fornell (1983), Issues in the Applicaion of Covariance Structure Analysis: A Comment, Journal of Consumer Research, 9(3), 443-488

[39] L. A. Hayduk (1987), Structural Equation Modeling with LISREL, Johns Hopkins University Press, Baltimore, MD

[40] J. Etezadi-Amoli, and A. F. Farhoomand (1996), A Structural Model of End User Computing Satisfaction and User Performance, Information & Management, 30(2), 65-73

[41] A. Bandura (1997), Self-Efficacy: The Exercise of Control, New York: W.H. Freeman