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Journal of Human Resources in Hospitality & Tourism
ISSN: 1533-2845 (Print) 1533-2853 (Online) Journal homepage: https://www.tandfonline.com/loi/whrh20
Preparing Hospitality Organizations for Self-Service Technology
Joseph D. Lema
To cite this article: Joseph D. Lema (2009) Preparing Hospitality Organizations for Self-Service Technology, Journal of Human Resources in Hospitality & Tourism, 8:2, 153-169, DOI:10.1080/15332840802269791
To link to this article: https://doi.org/10.1080/15332840802269791
Published online: 17 Jun 2009.
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Journal of Human Resources in Hospitality & Tourism, 8:153–169, 2009Copyright © Taylor & Francis Group, LLCISSN: 1533-2845 print / 1533-2853 onlineDOI: 10.1080/15332840802269791
Preparing Hospitality Organizationsfor Self-Service Technology
JOSEPH D. LEMAHospitality Management, Drexel University, Philadelphia, PA
Self-service technology is rapidly changing the hospitality indus-try, providing new opportunities for the delivery of services andoptions for customers. Preparing to implement effective self-servicetechnology delivery programs requires a workforce that can rapidlyadapt to change. Understanding factors that influence employeereadiness to engage in and support self-directed processes are animportant consideration when implementing self-service technol-ogy. The results of the linear regression model in this study indicatethat generalized self-efficacy and the self-directed learning readi-ness of employees in the hospitality industry are significantly relatedvariables. While self-efficacy was the most highly correlated vari-able to the self-directed learning readiness of hospitality employees,future studies should consider other characteristics that may influ-ence self-direction. As self-service technology continues to rapidlyexpand in all areas of the hospitality industry, opportunities andchallenges exist for both employees and customers.
KEYWORDS Self-service technology, hospitality, self-directedlearning.
Encompassing areas of food and beverage, lodging, and entertainment, thehospitality industry is one of the largest and fastest growing industries in theworld, with an enormous amount of human capital investment in a diverserange of jobs. It is estimated that by the year 2014 the hospitality industrywill 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
Address correspondence to Joseph D. Lema, PhD, Assistant Professor, Hospitality Man-agement, Drexel University, 3001 Market St., Philadelphia, PA 19104. E-mail: email@example.com
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capital labor expenditures in the global economy, the need to focus on thefoundation of the hospitality workforce includes an examination of employeedevelopment (Erdly & Chatterjee, 2003).
The rapid development of self-service technology is significantly influ-encing hospitality organizations by providing new opportunities and chal-lenges for customers and employees. A Time magazine article reported onthe popularity of self-service, with “U.S. customers spending $128 billion atself-service kiosks last year, an 80 percent jump from the year before, and by2007 it could hit $1.3 trillion” (Kiviat, 2004, p. 101). To maximize learning ca-pacity, self-directed learning readiness is a required strategy to consider withthe self-service concept. Self-service allows users greater control over theirexperience, just as self-directed learning emphasizes the learner’s personalcontrol over his or her own learning experience (Long, 2000).
The skill level and adaptability of employees to initiate change in theirjobs are factors in the competitiveness of a hospitality organization. Withgreater emphasis on productivity and accountability for individual perfor-mance, the responsibility for employees to rapidly adapt to change is movingfrom an organizational perspective to that of self-directedness. As the hospi-tality industry continues to consolidate, these organizations are experiencingmergers and acquisitions as rapidly as many individual employees changejobs and careers.
Technology in hospitality organizations has provided much of the suc-cess over past challenges with self-service strategies, although human cap-ital investment is required to match the personal characteristics necessaryto maximize the full potential of self-service benefits. A recent article inThe Economist reported, “Self-service is now doing for the service sectorwhat mass production once did for manufacturing, automating processesand significantly reducing costs” (You’re Hired, 2004, p. 21). With self-service kiosks in hotels, restaurants, and airports, self-service options arebecoming a part of everyday life. The explosion of online learning in ed-ucation and business is another example; the result of rapid increases intechnology. Although the capacity of self-service strategies is limited bythe competency of the user, self-directed learning is at the center of theself-service concept. The purpose of this study was to examine how self-efficacy and selected demographic variables (position, gender, and ethnic-ity) relate to the self-directed learning readiness of employees in hospitalityorganizations.
Examining whether significant relationships exist among the variables ofself-efficacy, demographics, and the self-directed learning readiness of em-ployees in the hospitality industry will have an impact on the productivityand competitiveness of the hospitality industry workforce. Connecting thesetheoretical relationships in the dynamic and diverse environment of the hos-pitality industry will help link learning theories with emerging practical ap-plications. Examining self-efficacy and demographic variables that influence
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the self-directed learning readiness of employees provides valuable insightsinto employee learning strategies and human resource development.
As hospitality organizations are required to remain competitive by im-plementing technologies that affect the learning environment of employees,the responsibility for self-directed learning has increased, along with technol-ogy. Characteristics that embrace self-directed learning and those that act asa barrier to self-directed learning need to be examined in order to determinethe readiness of an employee to adopt self-directed learning (Long, 1991b).As reported by senior executives within the hospitality industry, some of theoverarching issues for organizations include retention, education, training,and recruitment. In addition, positive solutions are needed to help transitionemployees and adopt strategies that initiate continuous change efficiently intargeted high-growth areas such as the hospitality industry (U.S. Departmentof Labor, 2005). Peter Senge (1990), known for his “learning organization”theory, reported in his widely referenced book, The Fifth Discipline, that thenew learning organization comprises workers who can adapt to change withtransformation through motivated self-directed learning and critical thinking.
While technology is fueling the growth of vast amounts of available in-formation, employees are being challenged to apply relevant information inthe context of their own work situation. As technology continues to rapidlyadvance in the hospitality industry and society, it is becoming extremelyimportant to have a highly capable workforce. Developing a competitiveworkforce is important in a global economy, and it is essential in highlycompetitive areas such as the hospitality industry. While technology hasfueled much of the growth in delivering new services and options that cus-tomers are demanding, a well-prepared and dynamic workforce is requiredto match the rapidly changing levels of technology.
Motivating employees to initiate change requires preparation and prac-tice. Preparing an employee to be self-directed involves consideration ofassumptions that follow self-directed learning methods. An understanding ofthe theoretical research of the variables that influence self-directed learningreadiness will allow a hospitality organization to create effective, efficientprograms and practices that maximize the talents of its employees. Thisunique workforce may have a much greater responsibility to meet the di-verse and ever-demanding needs of customers.
Organizations and employees that promote self-directed learning readi-ness will help prepare their employees to participate in self-directed workteams and support a learning organizational strategy. One of the challengesof this study is to investigate the relationships that selected variables haveon self-directed learning readiness. Developing a broader knowledge baseof self-directed learning readiness with selected variables may not only ben-efit a people-based business that is highly concentrated in employing andserving people, but other researchers and practitioners may benefit from theunique social learning environment that the hospitality industry has to offer.
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Self-directed learning has been a high-interest topic in the fields of busi-ness and education for more than a decade (Mezirow, 1985). Definitions ofself-directed learning share a number of unique and similar perspectives.Although there are a number of unifying elements in defining self-directedlearning, there is also a concerning ambiguity in precise definitions amongthe research (Oddi, 1987). Research into self-directed learning, accordingto Long (1991a), has developed over the past decade in both quantity andquality. Self-directed learning theory may have received greater attentionthan the practical aspect, which remains underdeveloped and has not re-ceived the same attention. Brookfield (1984) describes the process of self-directed learning as lacking a full appreciation for the impact of a skillfulinstructor and may fail to appreciate the social influence of subgroups inthe surrounding community or environment. Part of the confusion with theself-directed learning term may be linked to learning as an internal changeprocess and education as an external change process that facilitates internalchange (Brockett & Hiemstra, 1991). Rather than moving away from self-directed learning, Brockett and Hiemstra advocate expanding the conceptthrough continued development as a central theme and conceptual frame-work for adult learning.
Self-directedness is a characteristic of adult learning that is closely asso-ciated with self-directed learning and includes a level of decision making andpersonal control throughout the learning experience. Tough (1979) regardsself-directed learning as a form of adult learning that includes the abilityto plan and guide the learning process. Adults, according to Tough, havea desire to learn by drawing upon personal autonomy, self-worth, and ac-knowledgment of life experiences. Providing adults the opportunity to directand plan their own learning builds self-directedness that supports a growingnumber of adult learning theories (Knowles, 1984/1998; Long, 1991b; Tough,1979).
Self-efficacy is the belief in one’s own capability to initiate control oversituations in an organized process (Bandura, 1994). Expectations of self-efficacy involve psychological procedures which when analyzed may bedistinguished as two expectations of efficacy and outcome (Bandura, 1977).“An efficacy expectation is the conviction that one can successfully executethe behavior required to produce the outcomes. An outcome expectationis defined as a person’s estimate that a given behavior will lead to certainoutcomes” (Bandura, 1977, p. 79). The differentiation between efficacy andoutcome expectations is linked to the learner’s belief that a course of action
Preparing Hospitality Organizations for Self-Service Technology 157
may produce certain outcomes, but the learner questions whether he or shecan actually perform those actions.
Bandura (1977) argues that the strength of conviction of the learner’sown belief in effectiveness may determine whether the learner will pursuechanging or challenging situations. Learners may fear and avoid challeng-ing situations when their belief is that they will not be able to handle theproblem. Conversely, Bandura explains, learners may behave with confi-dence when they judge themselves to be capable of successfully handlingsituations that would have otherwise been threatening to them.
Self-efficacy theory is based on two types of expectations, mentionedearlier as efficacy expectations and outcome expectations, along with thecharacteristics, behavior, and behavioral outcomes of the person (Bandura,1986). Efficacy expectation (self-efficacy) is the person’s confidence in hisor her ability to produce the behavior, while the outcome expectation re-sults from the behavior based on a person’s belief about the outcome. Self-efficacy may be a more accurate predictor of performance since outcomeexpectations are dependent upon self-efficacy (Bandura, 1986). Employees,for example, may be more motivated to perform behaviors that they believewill produce desired outcomes.
Using self-efficacy as a predictor, Bandura (1986) explains, is importantin 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 (affectiveprocesses) to various situations. The main sources of information that in-fluence 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 isexperience of mastery (Bandura, 1986). According to Bandura, success andfailure attributes are important sources of information for developing self-efficacy. Successful experiences help enhance self-efficacy with a feeling ofmastery and control, while repeated failure decreases self-efficacy over time(van der Bijl & Shortridge-Baggett, 2002). When a learner has developed astrong self-efficacy, explains van der Bijl and Shortridge-Baggett, the effect ofone failure may not have much influence since the effects of failure followa total pattern of experiences, although the timing of the moment in thelearning process may vary in the power of the effect. If failure takes place inthe early stages of the learning process, for instance, the greater will be itsnegative impact on self-efficacy (van der Bijl and Shortridge-Baggett, 2002).
Bandura (1986) describes a hierarchy in the sources of information forself-efficacy and categorizes them as direct and indirect sources of informa-tion. Experience of mastery, for example, is one of the most powerful sourcesof information, as a person experiences success or failure immediately basedon direct information. The other information sources include observation ofothers, verbal persuasion, and physiological information based on indirect
158 J. D. Lema
sources of information. Indirect sources of information may not be nearlyas powerful in terms of information for self-efficacy as the cognitive pro-cess 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 factorssuch 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 vari-ety of internal and external information cues. Internal information cues relateto an individual’s knowledge or skills and the person’s effectiveness in us-ing these skills through various strategies. An individual’s self-efficacy canbe determined by an internal assessment (adequate, inferior, or superior) ofabilities when performing at various task levels. Judgments about expectedperformance when engaged in a task can be influenced by mood, health, ordegree of arousal, whether positive (excited) or negative (fearful). Externalinformation cues relate to the characteristics of the task itself, such as com-plexity, number of components, parts, sequence, uncertainty, and steps. Theresources and interdependence required to successfully complete the taskcan also influence the estimated level of self-efficacy.
Examinations of self-efficacy, Bandura (1997) suggests, often require as-sessments that an individual makes in terms of the variability in influencingdeterminants, previously described as experience of mastery, observation,verbal persuasion, physiological information, and others. The level of vari-ability may provide sources of information that range from low to high,immediate or over longer periods, stable or unpredictable. Acquiring knowl-edge, for instance, is one factor that may have an immediate effect on anindividual, whereas other factors such as ability may change after longerperiods of time. Bandura also argues that immediate variability in a factormay result in greater perceived control over those factors that are relativelystable and require longer periods of time.
Bandura (1986) emphasizes that one important element to considerwith self-efficacy is the perception of control. Some factors involve personalcontrol (e.g., effort), while other factors are controlled by someone else (e.g.,facilitator). The perception that the causes of performance are uncontrollablemay result in lower levels of variability, resistance to change, and a lowerlevel of self-efficacy. Bandura claims that analysis and understanding of theindividual and task is necessary to enhance self-efficacy.
According to Bandura (1994), self-efficacy involves the belief that peo-ple have in their personal capabilities the ability establish personal stan-dards. Since personal standards may be modified by the environment ordemographic characteristics (position, gender, ethnicity), beliefs in personalcapabilities may influence possible discrepancies between capabilities and
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self-generated standards. Based on his social cognitive theory, Bandura’s re-ciprocal process of self-efficacy has a primary objective of enhancing learningskills and self-directedness in individuals (Bandura, 1994; Kitson, Lekan, &Guglielmino, 1995).
Self-efficacy theories have created a framework for understanding el-ements related to self-directed learning. Brockett and Hiemstra (1991), forexample, developed a two-component model referred to as the PersonalResponsibility Orientation (PRO) that supports personal responsibility andindividual ownership of the learner’s thoughts and actions or learner self-direction. The other component consists of self-directed learning that em-phasizes the relationship between the learner and facilitator. The PRO modelsuggests that self-efficacy is central to understanding self-direction in regardsto employee learning. The model also suggests that employees are capableof taking a proactive approach to learning and, when given the opportunityto be self-directed, there is the potential to maximize benefits for both theemployee and organization.
Another model based on the situational nature of the learner and facili-tator was developed by Grow (1991) called the Staged Self-Directed LearningModel (SSDL). This model assumes that the self-directedness of the learner isbased on situational processes. Grow explains that learners progress throughstages of self-direction that may either increase or decrease depending uponthe situational circumstances. Furthermore, Grow argues, depending on thefacilitator’s approach, learning may be supported or hindered in the process.The SSDL model may help to indicate whether a facilitator’s style aligns withthe learner’s self-directed learning readiness.
The convenience sample consisted of employees who work in hospitalityorganizations, with approximately 216 employees participating. Data col-lection occurred at three participating hospitality organizations during April2006. The participating organizations offer a diverse workforce, with foodand beverage, lodging, and entertainment operations representing significantareas of the hospitality organization. A facilitator administered the survey tothe employees who volunteered for the study in the participating organi-zations’ business facilities. Completion time for the survey was 30 minutes.Participants were presented with an informed consent form before they par-ticipated in the study which clearly stated the voluntary nature of participa-tion, the ability to withdraw from the survey at any time, and confidentialityof the participants’ identities.
A survey integrating the Oddi Continuing Learning Inventory (OCLI)and Generalized Self-Efficacy Scale (GSE) was presented to the participantsas a five-page instrument consisting of 49 questions. The OCLI and GSE
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instruments uniquely complement each other in their generalizability to pro-vide a stable comprehensive indicator of relationships rather than measuringa narrowly defined activity that could be the result of a brief occurrence.Rather than measuring a specific task, the OCLI and GSE measure overalljob-related activities. The GSE instrument was used to assess self-beliefs ofpersonal capabilities of the employee (Jerusalem & Schwarzer, 1993). TheOCLI instrument, developed as a doctoral dissertation by Oddi (1984), wasadministered 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 in-struments 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.88and retest reliability of r = .89, the OCLI is an adequately reliable instrumentfor this study (Oddi, 1984).
Validation of the OCLI instrument was conducted by Oddi, Ellis, andAltman-Roberson (1990) to examine the relationship of the survey constructsand behavioral characteristics that indicate self-directed learning readiness.Three theories were developed to describe the affective, motivational, andcognitive attributes of the self-directed learner. The proactive drive versusreactive drive, commitment to learning versus apathy to learning, and cogni-tive openness versus defensiveness were reported by Oddi et al. (1990) to bethe 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 ageneral factor analysis, reading avidity and self-regulation emerged as sub-sidiary factors. No factor was related to cognitive openness in the analysis,since scores failed to correlate with the adult intelligence factor. When scoresfailed to correlate with adult intelligence, discriminate validity was provided.The two other dimensions that Oddi describes as reading avidity and abilityto be self-regulating positively correlated with self-confidence, participation,and endurance. These results indicate that the total OCLI score can be usedto provide a reliable and valid measure for the construct of self-directedlearning readiness.
The generalizability of the OCLI, detailed in a follow-up study by Six(1987), reported that factor analysis across different populations suggestedthat the factors identified by Oddi (1984) in the development of the OCLIinstrument were not unique to the sample. The factor analysis indicated thatthe factors derived by Oddi did not break up under different study condi-tions to form new factors and, as a result, remained stable across differentstudies (Six, 1987). Validation of the factor match, Six argued, demonstratesthe generality of the instrument across different populations. Respondentscircled 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
Preparing Hospitality Organizations for Self-Service Technology 161
learning readiness score from the survey was used in the statistical proce-dures 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. Jerusalemand Schwarzer developed the GSE in 1980, and it has been used withthousands of participants in 27 language versions throughout the world(Jerusalem & Schwarzer, 1993). The stability of the GSE has been reported ina number of longitudinal studies along with validation of the instrument insimilar 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 retestreliability (after 1 year) of r = .67 and a stability coefficient (after 2 years)of r = .75 (Jerusalem & Schwarzer, 1993). The participant was required tocircle the correct response from a 4-point scale ranging from 1 (not at alltrue) to 4 (exactly true). The overall score of the instrument was calculatedby totaling the response score to the appropriate questions. Scoring of theinstrument was in accordance with the guidelines provided by the authors(Jerusalem & Schwarzer, 1993).
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% AfricanAmerican (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 participantswere eliminated from the study. Three participants inaccurately completedresponses on the OCLI scale and one participant failed to complete responseson the GSE scale. Consequently the OCLI and GSE scales could not besufficiently scored for these participants. A total of 212 participants providedan adequate sample size for the number of predictors used in the stepwisemultiple 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 learningreadiness and a higher number (168 being the highest possible score) repre-senting greater self-directed learning readiness. Scores for the OCLI rangedfrom a low score of 51 to a high score of 154 with a mean score of 107. Therange of OCLI scores in this research were consistent with other researchin the area of self-directed learning readiness. The mean OCLI score in this
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study (107), however, was lower than the OCLI mean score (128) in a studyon practicing nurses (Oddi, 1987).
The GSE is measured on a 4-point scale, with a lower number indicatingless generalized self-efficacy and a higher number representing greater gen-eralized self-efficacy. Scores for the GSE ranged from a low score of 12 to ahigh score of 40 with a mean score of 30. Industry experience was measuredin months of hospitality industry work experience. Position was effect codedinto the classifications of Supervisor and Nonsupervisor. Gender was effectcoded into the categories of Male and Female. Finally, ethnicity was effectcoded 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 be-tween the OCLI and GSE, along with selected demographic variables. TheGSE variable reported the strongest correlation with OCLI scores (r = .840)when compared to position, gender, and ethnicity. Position was negativelycorrelated with OCLI scores, indicating that supervisors had higher OCLIscores and nonsupervisors had lower OCLI scores. All variables reported astatistically significant correlation with OCLI scores at or below the.05 alphalevel.
Considering the number of predictors and exploratory nature of thestudy, stepwise multiple linear regression was selected for the statistical anal-ysis. Only significant predictors remain in the stepwise multiple linear regres-sion model by the retesting of the predictor variables at each step (Mertler& Vannatta, 2005). The final stepwise multiple linear regression model con-sisted of four predictor variables, including generalized self-efficacy, position,gender, and ethnicity.
Data inspection did not locate any outliers, therefore no caseswere deleted from the analysis. Evaluations of linearity, Kolmogorov-Smirnov tests of normality, homoscedasticity, and multicollinearity showedthat the assumptions were within the range of tolerance. Four of the
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.
Preparing Hospitality Organizations for Self-Service Technology 163
variables—generalized self-efficacy, position, gender, and ethnicity—enteredinto the overall model, R2 = .81, R2
adj = .81, F(4, 206) = 176.48, p = .001.The final stepwise multiple linear regression model accounted for 81.1% ofthe variance in OCLI scores.
Hypothesis 1: There is a significant relationship between self-directed learn-ing readiness and self-efficacy of employees in the hospitality industry.
The results of the stepwise multiple linear regression model indicatedthat the self-efficacy variable makes a statistically significant contribution tothe self-directed learning readiness of employees in the hospitality indus-try. The strength of the correlation (r = .840) between self-efficacy andself-directed learning readiness indicated that higher self-efficacy scores areassociated with higher self-directed learning readiness scores. Furthermore,self-efficacy reported the strongest correlation among all of the tested vari-ables to the self-directed learning readiness of employees.
Hypothesis 2: There is a significant relationship between self-directed learn-ing readiness and selected demographic variables (position, gender, andethnicity) of employees in the hospitality industry.
The stepwise multiple linear regression model reported that positionhad a statistically significant relationship to self-directed learning readinessof employees in the hospitality industry. The strength of the relationshipwas (r = −.405), indicating that supervisors have higher self-directed learn-ing readiness scores than nonsupervisors. Position was the most stronglycorrelated variable to self-directed learning readiness of the demographicvariables. Gender also reported a statistically significant relationship to theself-directed learning readiness of employees in the hospitality industry andentered into the stepwise multiple linear regression model. The strength ofthe relationship (r = .310) indicated that females had higher self-directedlearning scores that males. Finally, ethnicity entered into the stepwise multi-ple linear regression model and had a statistically significant relationship tothe self-directed learning readiness of employees in the hospitality industry.The strength of the relationship (r = .129), although weak, indicated thatAsians had higher self-directed learning readiness scores.
The findings of this study provided both expected and unexpected results inview of previous studies. Self-efficacy, as expected, significantly correlated
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with the self-directed learning readiness of employees in the study, yet thestrength of the relationship compared to the other variables was surprisinglyhigher. Self-efficacy, as described by Bandura (1994), refers to the beliefin one’s own capability to initiate control over situations in an organizedprocess. The strong, significant relationship between self-efficacy and self-directed learning readiness supports Bandura’s (1997) notion that learnerswill pursue challenging situations when they have a belief that their capa-bilities to handle situations will produce positive outcomes. An employee’smotivation to participate in self-directed learning activities comes from thebelief that they are capable of succeeding in handling those particularsituations.
The strength of the relationship between self-efficacy and self-directedlearning readiness was discovered to be stronger than personal character-istics and demographic variables. In examining the four dynamics Bandura(1977) describes as performance accomplishments, experience, verbal per-suasion, and emotional arousal, the importance of building positive experi-ences is also evident in the significance of the strength in the relationshipbetween self-efficacy and the self-directed learning readiness of employeesin this study. Since experience of mastery is one of the most importantsources of information for determining levels of self-efficacy, positive expe-riences with self-directed learning opportunities may reciprocally indicate agreater readiness to engage in self-directed activities and higher levels ofself-efficacy. Lower levels of self-efficacy imply lower levels of self-directedlearning readiness based on the results of this study. The quality of the expe-riences, however, may not be determined from the findings of this research.
Position was found to be significantly correlated to the self-directed learningreadiness of employees in this study. In addition, position entered into thefinal stepwise multiple linear regression model. Individuals in supervisorypositions showed higher levels of self-directed learning readiness than thosein nonsupervisory positions. Although the significant results of this variableare consistent with the findings of another study (Roberts, 1986) involving aHong Kong telephone company, other researchers have indicated mixed re-sults in examining position relative to self-directed learning readiness (Brock-ett & Hiemstra, 1991). Oddi (1987) also reports that further examination ofthe variable “position” is recommended. Supervisor positions in the hospital-ity industry typically require greater leadership and critical decision-makingresponsibilities than nonsupervisors. The results of this study imply that su-pervisors have a greater self-directed learning readiness than nonsupervisorsand, although statistically significant, the correlation is not nearly as strongas the self-efficacy variable.
Preparing Hospitality Organizations for Self-Service Technology 165
Gender was significantly correlated with self-directed learning readiness.Furthermore, gender entered into the final stepwise multiple linear regressionmodel. Coinciding with a previous study by Guglielmino and Guglielmino(as cited in Brockett & Hiemstra, 1991), females reported higher overall levelsof self-directed learning readiness scores than males. Although their studyreported a significant relationship between gender and self-directed learningreadiness scores, the difference was narrow. The results of this study similarlyreported a narrow correlational significance between self-directed learningreadiness scores relative to the three other variables of interest, includingself-efficacy, position, and ethnicity.
The results of ethnicity indicated a significant relationship with self-directed learning readiness scores. In addition, ethnicity entered into thefinal stepwise multiple linear regression model, indicating possible predictivecapabilities in determining self-directed learning readiness for employees.Ethnicity has shown inconclusive results in a number of self-directed learningreadiness studies in another field, nursing, as reported by Oddi (1987). Thecorrelation between ethnicity and self-directed learning readiness is relativelyweak in comparison to the other variables that were tested in this study.
The role of self-efficacy in relation to self-directed learning readiness willrequire careful consideration. An increase in the level of self-directed learningreadiness of employees will need to coincide with strategies that enhanceself-efficacy. As Bandura (1977) argues, experience of mastery is one ofthe most significant information sources relative to self-efficacy. In view ofBandura’s theory, providing focused facilitation to complement self-directedprocesses may help to provide positive and successful experiences withself-service technologies and increased self-efficacy.
Bandura’s (1986) self-efficacy theory also emphasizes that environmentalfactors can impact levels of self-efficacy. The social element of the hospital-ity industry provides unique situations where concentrations of employeesand customers interact in dynamic environments. Levels of support maychange in an instant for employees and customers, and social support maybe instrumental in enhancing levels of self-efficacy. Organizational culturesthat create an environment in which hospitality among employees and cus-tomers is an essential priority will help to positively influence self-efficacyand self-directed learning readiness strategies.
Organizations that are able to incorporate self-directed learning con-cepts into their self-service processes may benefit by delivering successfulprograms to their employees and customers. With further understanding inthe differences of self-directed learning readiness, measurement, and bench-marking procedures among participants, hospitality organizations have an
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opportunity to gain competitive advantages. Recognizing self-directed learn-ing as a dynamic process that varies among different groups of individualsbased on their unique characteristics may help provide successful self-serviceprograms for hospitality organizations.
Employee self-efficacy should be considered when implementing self-directed learning processes. Examining the self-directed learning readinessof employees will help to determine at what level employees are able tosuccessfully engage in self-directed processes. The importance of buildingstrong levels of self-efficacy relative to the self-directed learning readiness ofemployees is evident in the results of this study.
Self-efficacy may be easily enhanced in situations that offer immedi-ate personal benefits to the learner. An employee who, for example, is anovice user of technology may be highly motivated to pursue a learningopportunity that will provide an immediate impact on his or her life, such ashaving unlimited access to self-service benefit options. An organization thatcan facilitate personal rewarding experiences for their employees also havean opportunity to create positive experiences that may increase self-efficacyand advance technological skill levels. One of the concerns that Hu, Nelson,Braunlich, & Hsieh (2003) explain in their research on technology-relatedtraining is that participants need to be more self-motivated in training ac-tivities in order to use the technological capabilities to the fullest extent.Providing learning opportunities through activities that have an immediatepersonal interest and impact on employees may be one possible motivationalstrategy. Furthermore, in view of rapid technological developments, offer-ing employees incentives to purchase personal computers for their homesmay provide other opportunities for employees to gain experience withself-service applications. Providing employees with the opportunity to gainself-service technology experience can begin with initiatives that are of per-sonal interest to employees, such as self-service benefits enrollment, payrolltransactions, 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 linesof inquiry into self-directed learning readiness, opportunities exist to fullyexpand the potential of self-directed learning. Oddi (1987) argues that op-portunities exist to move beyond self-directed learning as a self-instructionalprocess 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 theinquiring mind that approaches life with openness to discovery. Houle ar-gues that outstanding continuing learners have this attribute of personality(inquiry) to initiate learning. Although Oddi advocates that various modesof learning should not fail to be recognized, linking personality charac-teristics to self-directed learners offers substantial benefits to understand-ing the self-directed learning readiness of learners. Self-efficacy, being the
Preparing Hospitality Organizations for Self-Service Technology 167
predominantly significant variable relative to the self-directed learning readi-ness of employees as examined in this study, similarly supports Oddi’s at-tempt to identify significant relationships related to self-directed learningprocesses.
Since the total OCLI and GSE scores are the most highly recommendedand valid reporting statistics, factor analysis within the scales will not beused in further interpretation of the results due to a less stable level ofreliability. Furthermore, another limitation of this study is in regards to theGSE instrument as a measure of generalized self-efficacy and not a measure ofself-efficacy of a particular task, therefore limiting the analysis to generalizedresults. The results from this study did not attempt to provide cause-and-effect relationships among the variables. While offering suggestions for futureresearch investigations to refine self-directed learning, careful considerationshould be given to the operational definition of variables. The complexityof examining aspects related to self-directed learning may appear to benarrow in some circumstances, yet broad in others. Inquiry into self-directedlearning, however, should continue to experiment with variables that willprovide significant relationships and predictive capabilities with self-directedlearning instruments to measure participants’ levels of readiness in botheducational and occupational environments.
Opportunities exist to further develop instruments that measure self-directed learning readiness. Both Oddi and Guglielmino have significantlycontributed to advancing self-directed learning theories with the develop-ment of their self-directed learning readiness instruments. As technologycontinues to rapidly shape society and the hospitality industry, developmentof new instruments that build on the framework of existing self-directedlearning readiness instruments will help to provide greater understanding ofemerging self-directed learning processes.
Self-directed learning research should continue to benefit the hospi-tality industry as self-service technologies become part of daily operations.Hospitality organizations that are able to gain competitive advantages fromself-service processes will need support in developing strategies that canprovide the best experiences for their employees and customers.
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