Preparing Hospitality Organizations for Self-Service Technology

Abstract

Self-service technology is rapidly changing the hospitality industry, providing new opportunities for the delivery of services and options for customers. Preparing to implement effective self-service technology delivery programs requires a workforce that can rapidly adapt to change. Understanding factors that influence employee readiness to engage in and support self-directed processes are an important consideration when implementing self-service technology. The results of the linear regression model in this study indicate that generalized self-efficacy and the self-directed learning readiness of employees in the hospitality industry are significantly related variables. While self-efficacy was the most highly correlated variable to the self-directed learning readiness of hospitality employees, future studies should consider other characteristics that may influence self-direction. As self-service technology continues to rapidly expand in all areas of the hospitality industry, opportunities and challenges exist for both employees and customers.

KEYWORDS:

• Self-service technology
• hospitality
• self-directed learning

INTRODUCTION

Encompassing areas of food and beverage, lodging, and entertainment, the hospitality industry is one of the largest and fastest growing industries in the world, with an enormous amount of human capital investment in a diverse range of jobs. It is estimated that by the year 2014 the hospitality industry will employ more than 14,693 million workers in the United States (U.S. Department of Labor, 2005). With an industry that has one of the largest capital labor expenditures in the global economy, the need to focus on the foundation of the hospitality workforce includes an examination of employee development (Erdly & Chatterjee, 2003).

The rapid development of self-service technology is significantly influencing hospitality organizations by providing new opportunities and challenges for customers and employees. A Time magazine article reported on the popularity of self-service, with “U.S. customers spending $128 billion at self-service kiosks last year, an 80 percent jump from the year before, and by 2007 it could hit $1.3 trillion” (Kiviat, 2004, p. 101). To maximize learning capacity, self-directed learning readiness is a required strategy to consider with the self-service concept. Self-service allows users greater control over their experience, just as self-directed learning emphasizes the learner's personal control over his or her own learning experience (Long, 2000).

The skill level and adaptability of employees to initiate change in their jobs are factors in the competitiveness of a hospitality organization. With greater emphasis on productivity and accountability for individual performance, the responsibility for employees to rapidly adapt to change is moving from an organizational perspective to that of self-directedness. As the hospitality industry continues to consolidate, these organizations are experiencing mergers and acquisitions as rapidly as many individual employees change jobs and careers.

Technology in hospitality organizations has provided much of the success over past challenges with self-service strategies, although human capital investment is required to match the personal characteristics necessary to maximize the full potential of self-service benefits. A recent article in The Economist reported, “Self-service is now doing for the service sector what mass production once did for manufacturing, automating processes and significantly reducing costs” (You're Hired, 2004, p. 21). With self-service kiosks in hotels, restaurants, and airports, self-service options are becoming a part of everyday life. The explosion of online learning in education and business is another example; the result of rapid increases in technology. Although the capacity of self-service strategies is limited by the competency of the user, self-directed learning is at the center of the self-service concept. The purpose of this study was to examine how self-efficacy and selected demographic variables (position, gender, and ethnicity) relate to the self-directed learning readiness of employees in hospitality organizations.

Examining whether significant relationships exist among the variables of self-efficacy, demographics, and the self-directed learning readiness of employees in the hospitality industry will have an impact on the productivity and competitiveness of the hospitality industry workforce. Connecting these theoretical relationships in the dynamic and diverse environment of the hospitality industry will help link learning theories with emerging practical applications. Examining self-efficacy and demographic variables that influence the self-directed learning readiness of employees provides valuable insights into employee learning strategies and human resource development.

As hospitality organizations are required to remain competitive by implementing technologies that affect the learning environment of employees, the responsibility for self-directed learning has increased, along with technology. Characteristics that embrace self-directed learning and those that act as a barrier to self-directed learning need to be examined in order to determine the readiness of an employee to adopt self-directed learning (Long, 1991b). As reported by senior executives within the hospitality industry, some of the overarching issues for organizations include retention, education, training, and recruitment. In addition, positive solutions are needed to help transition employees and adopt strategies that initiate continuous change efficiently in targeted high-growth areas such as the hospitality industry (U.S. Department of Labor, 2005). Peter Senge (1990), known for his “learning organization” theory, reported in his widely referenced book, The Fifth Discipline, that the new learning organization comprises workers who can adapt to change with transformation through motivated self-directed learning and critical thinking.

While technology is fueling the growth of vast amounts of available information, employees are being challenged to apply relevant information in the context of their own work situation. As technology continues to rapidly advance in the hospitality industry and society, it is becoming extremely important to have a highly capable workforce. Developing a competitive workforce is important in a global economy, and it is essential in highly competitive areas such as the hospitality industry. While technology has fueled much of the growth in delivering new services and options that customers are demanding, a well-prepared and dynamic workforce is required to match the rapidly changing levels of technology.

Motivating employees to initiate change requires preparation and practice. Preparing an employee to be self-directed involves consideration of assumptions that follow self-directed learning methods. An understanding of the theoretical research of the variables that influence self-directed learning readiness will allow a hospitality organization to create effective, efficient programs and practices that maximize the talents of its employees. This unique workforce may have a much greater responsibility to meet the diverse and ever-demanding needs of customers.

Organizations and employees that promote self-directed learning readiness will help prepare their employees to participate in self-directed work teams and support a learning organizational strategy. One of the challenges of this study is to investigate the relationships that selected variables have on self-directed learning readiness. Developing a broader knowledge base of self-directed learning readiness with selected variables may not only benefit a people-based business that is highly concentrated in employing and serving people, but other researchers and practitioners may benefit from the unique social learning environment that the hospitality industry has to offer.

LITERATURE REVIEW

Self-directed learning has been a high-interest topic in the fields of business and education for more than a decade (Mezirow, 1985). Definitions of self-directed learning share a number of unique and similar perspectives. Although there are a number of unifying elements in defining self-directed learning, there is also a concerning ambiguity in precise definitions among the research (Oddi, 1987). Research into self-directed learning, according to Long (1991a), has developed over the past decade in both quantity and quality. Self-directed learning theory may have received greater attention than the practical aspect, which remains underdeveloped and has not received the same attention. Brookfield (1984) describes the process of self-directed learning as lacking a full appreciation for the impact of a skillful instructor and may fail to appreciate the social influence of subgroups in the surrounding community or environment. Part of the confusion with the self-directed learning term may be linked to learning as an internal change process and education as an external change process that facilitates internal change (Brockett & Hiemstra, 1991). Rather than moving away from self-directed learning, Brockett and Hiemstra advocate expanding the concept through continued development as a central theme and conceptual framework for adult learning.

Self-directedness is a characteristic of adult learning that is closely associated with self-directed learning and includes a level of decision making and personal control throughout the learning experience. Tough (1979) regards self-directed learning as a form of adult learning that includes the ability to plan and guide the learning process. Adults, according to Tough, have a desire to learn by drawing upon personal autonomy, self-worth, and acknowledgment of life experiences. Providing adults the opportunity to direct and plan their own learning builds self-directedness that supports a growing number of adult learning theories (Knowles, 1984/1998; Long, 1991b; Tough, 1979).

Self-Efficacy

Self-efficacy is the belief in one's own capability to initiate control over situations in an organized process (Bandura, 1994). Expectations of self-efficacy involve psychological procedures which when analyzed may be distinguished as two expectations of efficacy and outcome (Bandura, 1977). “An efficacy expectation is the conviction that one can successfully execute the behavior required to produce the outcomes. An outcome expectation is defined as a person's estimate that a given behavior will lead to certain outcomes” (Bandura, 1977, p. 79). The differentiation between efficacy and outcome expectations is linked to the learner's belief that a course of action may produce certain outcomes, but the learner questions whether he or she can actually perform those actions.

Bandura (1977) argues that the strength of conviction of the learner's own belief in effectiveness may determine whether the learner will pursue changing or challenging situations. Learners may fear and avoid challenging situations when their belief is that they will not be able to handle the problem. Conversely, Bandura explains, learners may behave with confidence when they judge themselves to be capable of successfully handling situations that would have otherwise been threatening to them.

Self-efficacy theory is based on two types of expectations, mentioned earlier as efficacy expectations and outcome expectations, along with the characteristics, behavior, and behavioral outcomes of the person (Bandura, 1986). Efficacy expectation (self-efficacy) is the person's confidence in his or her ability to produce the behavior, while the outcome expectation results from the behavior based on a person's belief about the outcome. Self-efficacy may be a more accurate predictor of performance since outcome expectations are dependent upon self-efficacy (Bandura, 1986). Employees, for example, may be more motivated to perform behaviors that they believe will produce desired outcomes.

Using self-efficacy as a predictor, Bandura (1986) explains, is important in understanding how people function in terms of the choices they make (selection processes), effort (time and persistence), motivation (initiation), thought patterns (cognitive processes), and emotional reactions (affective processes) to various situations. The main sources of information that influence beliefs in self-efficacy include experience of mastery, observation, verbal persuasion, and physiological information (Bandura, 1986, 1997).

One of the most influential sources of information on self-efficacy is experience of mastery (Bandura, 1986). According to Bandura, success and failure attributes are important sources of information for developing self-efficacy. Successful experiences help enhance self-efficacy with a feeling of mastery and control, while repeated failure decreases self-efficacy over time (van der Bijl & Shortridge-Baggett, 2002). When a learner has developed a strong self-efficacy, explains van der Bijl and Shortridge-Baggett, the effect of one failure may not have much influence since the effects of failure follow a total pattern of experiences, although the timing of the moment in the learning process may vary in the power of the effect. If failure takes place in the early stages of the learning process, for instance, the greater will be its negative impact on self-efficacy (van der Bijl and Shortridge-Baggett, 2002).

Bandura (1986) describes a hierarchy in the sources of information for self-efficacy and categorizes them as direct and indirect sources of information. Experience of mastery, for example, is one of the most powerful sources of information, as a person experiences success or failure immediately based on direct information. The other information sources include observation of others, verbal persuasion, and physiological information based on indirect sources of information. Indirect sources of information may not be nearly as powerful in terms of information for self-efficacy as the cognitive process associated with critically reflective patterns of direct earlier experiences. Other sources that influence self-efficacy include personality traits (Strecher, DeVellis, Becker, & Rosenstock, 1986) such as self-esteem, locus of control, self-confidence, and hardiness (Coppel, 1980), and environmental factors such as expectations and support of others (Bandura, 1986).

In a further theoretical analysis of sources that influence self-efficacy, Gist and Mitchell (1992) suggest that experiences of mastery, observation, verbal persuasion, and physiological information contribute through a variety of internal and external information cues. Internal information cues relate to an individual's knowledge or skills and the person's effectiveness in using these skills through various strategies. An individual's self-efficacy can be determined by an internal assessment (adequate, inferior, or superior) of abilities when performing at various task levels. Judgments about expected performance when engaged in a task can be influenced by mood, health, or degree of arousal, whether positive (excited) or negative (fearful). External information cues relate to the characteristics of the task itself, such as complexity, number of components, parts, sequence, uncertainty, and steps. The resources and interdependence required to successfully complete the task can also influence the estimated level of self-efficacy.

Examinations of self-efficacy, Bandura (1997) suggests, often require assessments that an individual makes in terms of the variability in influencing determinants, previously described as experience of mastery, observation, verbal persuasion, physiological information, and others. The level of variability may provide sources of information that range from low to high, immediate or over longer periods, stable or unpredictable. Acquiring knowledge, for instance, is one factor that may have an immediate effect on an individual, whereas other factors such as ability may change after longer periods of time. Bandura also argues that immediate variability in a factor may result in greater perceived control over those factors that are relatively stable and require longer periods of time.

Bandura (1986) emphasizes that one important element to consider with self-efficacy is the perception of control. Some factors involve personal control (e.g., effort), while other factors are controlled by someone else (e.g., facilitator). The perception that the causes of performance are uncontrollable may result in lower levels of variability, resistance to change, and a lower level of self-efficacy. Bandura claims that analysis and understanding of the individual and task is necessary to enhance self-efficacy.

According to Bandura (1994), self-efficacy involves the belief that people have in their personal capabilities the ability establish personal standards. Since personal standards may be modified by the environment or demographic characteristics (position, gender, ethnicity), beliefs in personal capabilities may influence possible discrepancies between capabilities and self-generated standards. Based on his social cognitive theory, Bandura's reciprocal process of self-efficacy has a primary objective of enhancing learning skills and self-directedness in individuals (Bandura, 1994; Kitson, Lekan, & Guglielmino, 1995).

Self-efficacy theories have created a framework for understanding elements related to self-directed learning. Brockett and Hiemstra (1991), for example, developed a two-component model referred to as the Personal Responsibility Orientation (PRO) that supports personal responsibility and individual ownership of the learner's thoughts and actions or learner self-direction. The other component consists of self-directed learning that emphasizes the relationship between the learner and facilitator. The PRO model suggests that self-efficacy is central to understanding self-direction in regards to employee learning. The model also suggests that employees are capable of taking a proactive approach to learning and, when given the opportunity to be self-directed, there is the potential to maximize benefits for both the employee and organization.

Another model based on the situational nature of the learner and facilitator was developed by Grow (1991) called the Staged Self-Directed Learning Model (SSDL). This model assumes that the self-directedness of the learner is based on situational processes. Grow explains that learners progress through stages of self-direction that may either increase or decrease depending upon the situational circumstances. Furthermore, Grow argues, depending on the facilitator's approach, learning may be supported or hindered in the process. The SSDL model may help to indicate whether a facilitator's style aligns with the learner's self-directed learning readiness.

METHODOLOGY

The convenience sample consisted of employees who work in hospitality organizations, with approximately 216 employees participating. Data collection occurred at three participating hospitality organizations during April 2006. The participating organizations offer a diverse workforce, with food and beverage, lodging, and entertainment operations representing significant areas of the hospitality organization. A facilitator administered the survey to the employees who volunteered for the study in the participating organizations’ business facilities. Completion time for the survey was 30 minutes. Participants were presented with an informed consent form before they participated in the study which clearly stated the voluntary nature of participation, the ability to withdraw from the survey at any time, and confidentiality of the participants’ identities.

A survey integrating the Oddi Continuing Learning Inventory (OCLI) and Generalized Self-Efficacy Scale (GSE) was presented to the participants as a five-page instrument consisting of 49 questions. The OCLI and GSE instruments uniquely complement each other in their generalizability to provide a stable comprehensive indicator of relationships rather than measuring a narrowly defined activity that could be the result of a brief occurrence. Rather than measuring a specific task, the OCLI and GSE measure overall job-related activities. The GSE instrument was used to assess self-beliefs of personal capabilities of the employee (Jerusalem & Schwarzer, 1993). The OCLI instrument, developed as a doctoral dissertation by Oddi (1984), was administered to assess employees’ levels of self-directed learning readiness. The relationships among GSE scores and OCLI scores were examined.

The OCLI survey is one of the most widely reliable and validated instruments used for the measurement of readiness for self-directed learning (Brockett & Hiemstra, 1991). The OCLI survey measures the level of self-directed learning readiness of adults. With a reported Cronbach's alpha of .88 and retest reliability of r = .89, the OCLI is an adequately reliable instrument for this study (Oddi, 1984).

Validation of the OCLI instrument was conducted by Oddi, Ellis, and Altman-Roberson (1990) to examine the relationship of the survey constructs and behavioral characteristics that indicate self-directed learning readiness. Three theories were developed to describe the affective, motivational, and cognitive attributes of the self-directed learner. The proactive drive versus reactive drive, commitment to learning versus apathy to learning, and cognitive openness versus defensiveness were reported by Oddi et al. (1990) to be the three constructs that emerged. Factor analysis reported by Oddi (1984) indicated that OCLI items contained self-confidence, autonomous learning, and learning with the participation of others. When items were loaded on a general factor analysis, reading avidity and self-regulation emerged as subsidiary factors. No factor was related to cognitive openness in the analysis, since scores failed to correlate with the adult intelligence factor. When scores failed to correlate with adult intelligence, discriminate validity was provided. The two other dimensions that Oddi describes as reading avidity and ability to be self-regulating positively correlated with self-confidence, participation, and endurance. These results indicate that the total OCLI score can be used to provide a reliable and valid measure for the construct of self-directed learning readiness.

The generalizability of the OCLI, detailed in a follow-up study by Six (1987), reported that factor analysis across different populations suggested that the factors identified by Oddi (1984) in the development of the OCLI instrument were not unique to the sample. The factor analysis indicated that the factors derived by Oddi did not break up under different study conditions to form new factors and, as a result, remained stable across different studies (Six, 1987). Validation of the factor match, Six argued, demonstrates the generality of the instrument across different populations. Respondents circled an answer from a 7-point Likert scale ranging from 1 (strongly agree) to 7 (strongly disagree) to best describe their behavior. The total self-directed learning readiness score from the survey was used in the statistical procedures as recommended in the literature (Brockett & Hiemstra, 1991; Oddi, 1984).

The GSE consists of a scale designed to measure the generalized self-efficacy or the employees’ belief in their ability to perform their job. Jerusalem and Schwarzer developed the GSE in 1980, and it has been used with thousands of participants in 27 language versions throughout the world (Jerusalem & Schwarzer, 1993). The stability of the GSE has been reported in a number of longitudinal studies along with validation of the instrument in similar occupational and educational environments (Jerusalem & Schwarzer, 1993; Pasveer, 1998; Schwarzer, BaBler, Kwiatek, Schroder, & Zhang, 1997; Schwarzer & Born, 1997; Schwarzer, Mueller, & Greenglass, 1999; Schwarzer & Schroder, 1997). The GSE reports a Cronbach's alpha of .75, with a retest reliability (after 1 year) of r = .67 and a stability coefficient (after 2 years) of r = .75 (Jerusalem & Schwarzer, 1993). The participant was required to circle the correct response from a 4-point scale ranging from 1 (not at all true) to 4 (exactly true). The overall score of the instrument was calculated by totaling the response score to the appropriate questions. Scoring of the instrument was in accordance with the guidelines provided by the authors (Jerusalem & Schwarzer, 1993).

FINDINGS

Descriptive data for the total sample include position, gender, and ethnicity. The sample consisted of individuals working as supervisors (34%, n = 71) and nonsupervisors (66%, n = 141). In addition, the sample contained 52% females (n = 111) and 48% males (n = 101). Ethnicity included 14% African American (n = 30), 2% American Indian (n = 5), 17% Asian (n = 35), 45% Caucasian (n = 96), 13% Hispanic (n = 27), and 9% Pacific Islander (n = 19). Due to missing responses on the OCLI and GSE, four participants were eliminated from the study. Three participants inaccurately completed responses on the OCLI scale and one participant failed to complete responses on the GSE scale. Consequently the OCLI and GSE scales could not be sufficiently scored for these participants. A total of 212 participants provided an adequate sample size for the number of predictors used in the stepwise multiple linear regression model.

The OCLI variable consisted of a 7-point scale, with a lower number (24 being the lowest possible score) indicating less self-directed learning readiness and a higher number (168 being the highest possible score) representing greater self-directed learning readiness. Scores for the OCLI ranged from a low score of 51 to a high score of 154 with a mean score of 107. The range of OCLI scores in this research were consistent with other research in the area of self-directed learning readiness. The mean OCLI score in this study (107), however, was lower than the OCLI mean score (128) in a study on practicing nurses (Oddi, 1987).

The GSE is measured on a 4-point scale, with a lower number indicating less generalized self-efficacy and a higher number representing greater generalized self-efficacy. Scores for the GSE ranged from a low score of 12 to a high score of 40 with a mean score of 30. Industry experience was measured in months of hospitality industry work experience. Position was effect coded into the classifications of Supervisor and Nonsupervisor. Gender was effect coded into the categories of Male and Female. Finally, ethnicity was effect coded into the classifications of African American, American Indian, Asian, Caucasian, Hispanic, and Pacific Islander.

Overall Stepwise Multiple Linear Regression Model

A correlation matrix is presented in Table 1 to report the relationships between the OCLI and GSE, along with selected demographic variables. The GSE variable reported the strongest correlation with OCLI scores (r = .840) when compared to position, gender, and ethnicity. Position was negatively correlated with OCLI scores, indicating that supervisors had higher OCLI scores and nonsupervisors had lower OCLI scores. All variables reported a statistically significant correlation with OCLI scores at or below the .05 alpha level.

Table 1 Pearson Correlation Matrix (N = 212)

	OCLI	GSE	Position	Gender	Ethnicity
OCLI	—
GSE	.840∗	—
Position	−.405∗	−.414	—
Gender	.310∗	.089	.024	—
Ethnicity	.129∗	.006	−.105	.082	—
Note. ∗p < .05, one-tailed.

Considering the number of predictors and exploratory nature of the study, stepwise multiple linear regression was selected for the statistical analysis. Only significant predictors remain in the stepwise multiple linear regression model by the retesting of the predictor variables at each step (Mertler & Vannatta, 2005). The final stepwise multiple linear regression model consisted of four predictor variables, including generalized self-efficacy, position, gender, and ethnicity.

Data inspection did not locate any outliers, therefore no cases were deleted from the analysis. Evaluations of linearity, Kolmogorov-Smirnov tests of normality, homoscedasticity, and multicollinearity showed that the assumptions were within the range of tolerance. Four of the variables—generalized self-efficacy, position, gender, and ethnicity—entered into the overall model, R ² = .81, R ² _adj = .81, F(4, 206) = 176.48, p = .001. The final stepwise multiple linear regression model accounted for 81.1% of the variance in OCLI scores.

Hypotheses Testing

Hypothesis 1: There is a significant relationship between self-directed learning readiness and self-efficacy of employees in the hospitality industry.

The results of the stepwise multiple linear regression model indicated that the self-efficacy variable makes a statistically significant contribution to the self-directed learning readiness of employees in the hospitality industry. The strength of the correlation (r = .840) between self-efficacy and self-directed learning readiness indicated that higher self-efficacy scores are associated with higher self-directed learning readiness scores. Furthermore, self-efficacy reported the strongest correlation among all of the tested variables to the self-directed learning readiness of employees.

Hypothesis 2: There is a significant relationship between self-directed learning readiness and selected demographic variables (position, gender, and ethnicity) of employees in the hospitality industry.

The stepwise multiple linear regression model reported that position had a statistically significant relationship to self-directed learning readiness of employees in the hospitality industry. The strength of the relationship was (r = −.405), indicating that supervisors have higher self-directed learning readiness scores than nonsupervisors. Position was the most strongly correlated variable to self-directed learning readiness of the demographic variables. Gender also reported a statistically significant relationship to the self-directed learning readiness of employees in the hospitality industry and entered into the stepwise multiple linear regression model. The strength of the relationship (r = .310) indicated that females had higher self-directed learning scores that males. Finally, ethnicity entered into the stepwise multiple linear regression model and had a statistically significant relationship to the self-directed learning readiness of employees in the hospitality industry. The strength of the relationship (r = .129), although weak, indicated that Asians had higher self-directed learning readiness scores.

DISCUSSION

Self-Efficacy

The findings of this study provided both expected and unexpected results in view of previous studies. Self-efficacy, as expected, significantly correlated with the self-directed learning readiness of employees in the study, yet the strength of the relationship compared to the other variables was surprisingly higher. Self-efficacy, as described by Bandura (1994), refers to the belief in one's own capability to initiate control over situations in an organized process. The strong, significant relationship between self-efficacy and self-directed learning readiness supports Bandura's (1997) notion that learners will pursue challenging situations when they have a belief that their capabilities to handle situations will produce positive outcomes. An employee's motivation to participate in self-directed learning activities comes from the belief that they are capable of succeeding in handling those particular situations.

The strength of the relationship between self-efficacy and self-directed learning readiness was discovered to be stronger than personal characteristics and demographic variables. In examining the four dynamics Bandura (1977) describes as performance accomplishments, experience, verbal persuasion, and emotional arousal, the importance of building positive experiences is also evident in the significance of the strength in the relationship between self-efficacy and the self-directed learning readiness of employees in this study. Since experience of mastery is one of the most important sources of information for determining levels of self-efficacy, positive experiences with self-directed learning opportunities may reciprocally indicate a greater readiness to engage in self-directed activities and higher levels of self-efficacy. Lower levels of self-efficacy imply lower levels of self-directed learning readiness based on the results of this study. The quality of the experiences, however, may not be determined from the findings of this research.

Demographic Variables

Position was found to be significantly correlated to the self-directed learning readiness of employees in this study. In addition, position entered into the final stepwise multiple linear regression model. Individuals in supervisory positions showed higher levels of self-directed learning readiness than those in nonsupervisory positions. Although the significant results of this variable are consistent with the findings of another study (Roberts, 1986) involving a Hong Kong telephone company, other researchers have indicated mixed results in examining position relative to self-directed learning readiness (Brockett & Hiemstra, 1991). Oddi (1987) also reports that further examination of the variable “position” is recommended. Supervisor positions in the hospitality industry typically require greater leadership and critical decision-making responsibilities than nonsupervisors. The results of this study imply that supervisors have a greater self-directed learning readiness than nonsupervisors and, although statistically significant, the correlation is not nearly as strong as the self-efficacy variable.

Gender was significantly correlated with self-directed learning readiness. Furthermore, gender entered into the final stepwise multiple linear regression model. Coinciding with a previous study by Guglielmino and Guglielmino (as cited in Brockett & Hiemstra, 1991), females reported higher overall levels of self-directed learning readiness scores than males. Although their study reported a significant relationship between gender and self-directed learning readiness scores, the difference was narrow. The results of this study similarly reported a narrow correlational significance between self-directed learning readiness scores relative to the three other variables of interest, including self-efficacy, position, and ethnicity.

The results of ethnicity indicated a significant relationship with self-directed learning readiness scores. In addition, ethnicity entered into the final stepwise multiple linear regression model, indicating possible predictive capabilities in determining self-directed learning readiness for employees. Ethnicity has shown inconclusive results in a number of self-directed learning readiness studies in another field, nursing, as reported by Oddi (1987). The correlation between ethnicity and self-directed learning readiness is relatively weak in comparison to the other variables that were tested in this study.

CONCLUSION

The role of self-efficacy in relation to self-directed learning readiness will require careful consideration. An increase in the level of self-directed learning readiness of employees will need to coincide with strategies that enhance self-efficacy. As Bandura (1977) argues, experience of mastery is one of the most significant information sources relative to self-efficacy. In view of Bandura's theory, providing focused facilitation to complement self-directed processes may help to provide positive and successful experiences with self-service technologies and increased self-efficacy.

Bandura's (1986) self-efficacy theory also emphasizes that environmental factors can impact levels of self-efficacy. The social element of the hospitality industry provides unique situations where concentrations of employees and customers interact in dynamic environments. Levels of support may change in an instant for employees and customers, and social support may be instrumental in enhancing levels of self-efficacy. Organizational cultures that create an environment in which hospitality among employees and customers is an essential priority will help to positively influence self-efficacy and self-directed learning readiness strategies.

Organizations that are able to incorporate self-directed learning concepts into their self-service processes may benefit by delivering successful programs to their employees and customers. With further understanding in the differences of self-directed learning readiness, measurement, and benchmarking procedures among participants, hospitality organizations have an opportunity to gain competitive advantages. Recognizing self-directed learning as a dynamic process that varies among different groups of individuals based on their unique characteristics may help provide successful self-service programs for hospitality organizations.

Employee self-efficacy should be considered when implementing self-directed learning processes. Examining the self-directed learning readiness of employees will help to determine at what level employees are able to successfully engage in self-directed processes. The importance of building strong levels of self-efficacy relative to the self-directed learning readiness of employees is evident in the results of this study.

Self-efficacy may be easily enhanced in situations that offer immediate personal benefits to the learner. An employee who, for example, is a novice user of technology may be highly motivated to pursue a learning opportunity that will provide an immediate impact on his or her life, such as having unlimited access to self-service benefit options. An organization that can facilitate personal rewarding experiences for their employees also have an opportunity to create positive experiences that may increase self-efficacy and advance technological skill levels. One of the concerns that Hu, Nelson, Braunlich, & Hsieh (2003) explain in their research on technology-related training is that participants need to be more self-motivated in training activities in order to use the technological capabilities to the fullest extent. Providing learning opportunities through activities that have an immediate personal interest and impact on employees may be one possible motivational strategy. Furthermore, in view of rapid technological developments, offering employees incentives to purchase personal computers for their homes may provide other opportunities for employees to gain experience with self-service applications. Providing employees with the opportunity to gain self-service technology experience can begin with initiatives that are of personal interest to employees, such as self-service benefits enrollment, payroll transactions, and other personnel-related activities.

In moving beyond the misconceptions and misunderstandings of self-directed learning, Brocket et al. (2000) propose that by identifying new lines of inquiry into self-directed learning readiness, opportunities exist to fully expand the potential of self-directed learning. Oddi (1987) argues that opportunities exist to move beyond self-directed learning as a self-instructional process to examine self-directed learning in terms of cognitive, motivational, and affective characteristics and personalities of self-directed learners. Houle (1961) suggests, for example, the essence of self-directed learning is the inquiring mind that approaches life with openness to discovery. Houle argues that outstanding continuing learners have this attribute of personality (inquiry) to initiate learning. Although Oddi advocates that various modes of learning should not fail to be recognized, linking personality characteristics to self-directed learners offers substantial benefits to understanding the self-directed learning readiness of learners. Self-efficacy, being the predominantly significant variable relative to the self-directed learning readiness of employees as examined in this study, similarly supports Oddi's attempt to identify significant relationships related to self-directed learning processes.

Since the total OCLI and GSE scores are the most highly recommended and valid reporting statistics, factor analysis within the scales will not be used in further interpretation of the results due to a less stable level of reliability. Furthermore, another limitation of this study is in regards to the GSE instrument as a measure of generalized self-efficacy and not a measure of self-efficacy of a particular task, therefore limiting the analysis to generalized results. The results from this study did not attempt to provide cause-and-effect relationships among the variables. While offering suggestions for future research investigations to refine self-directed learning, careful consideration should be given to the operational definition of variables. The complexity of examining aspects related to self-directed learning may appear to be narrow in some circumstances, yet broad in others. Inquiry into self-directed learning, however, should continue to experiment with variables that will provide significant relationships and predictive capabilities with self-directed learning instruments to measure participants’ levels of readiness in both educational and occupational environments.

Opportunities exist to further develop instruments that measure self-directed learning readiness. Both Oddi and Guglielmino have significantly contributed to advancing self-directed learning theories with the development of their self-directed learning readiness instruments. As technology continues to rapidly shape society and the hospitality industry, development of new instruments that build on the framework of existing self-directed learning readiness instruments will help to provide greater understanding of emerging self-directed learning processes.

Self-directed learning research should continue to benefit the hospitality industry as self-service technologies become part of daily operations. Hospitality organizations that are able to gain competitive advantages from self-service processes will need support in developing strategies that can provide the best experiences for their employees and customers.