The relationship between national culture and the usability of an e-learning system.
The purpose of this study was to investigate possible relationships between national culture and the usability of an e-learning system. The theoretical frameworks that were used to guide this study were Hofstede's (1980) cultural dimensions, and Nielson's (1993)usability attributes. The sample for this study was composed of 24 international students from 11 different countries attending a large Midwest university in the United States. This sample was selected for this study because they represented the various cultural dimensions being studied, had spent their formative years in their home country, and were not advanced computer users. Three instruments were used in this study and each instrument assisted in collecting information regarding unique aspects of the research study. The study revealed that females had higher scores for Learnability Time and Learnability Path, while males had higher scores for Satisfaction with Navigation and Satisfaction with General Usage. The study also found that respondents in the Low Uncertainty Avoidance group had higher Memorability Path scores than those in the High Uncertainty Avoidance group.**********
Demographic changes, technological advances, and globalization have forced corporations throughout the world to reexamine their policies, programs, and practices. This multifaceted statement is reflective of the concept of a changing global marketplace and the effects this will have on corporations. It appears obvious that these effects will not only change the face of business but also the methods of conducting training and doing business. A global marketplace has created many more opportunities for corporations to work with businesses and individuals throughout the world. In particular, technology has provided individuals around the world the convenience of constructing a virtual global learning community. Businesses, educational, and training organizations no longer must rely solely on face-to-face, teacher-led, or lecture style instruction. A wide variety of technology is available to companies, governments, organizations, and educational institutions for training and instructional purposes (Berge, 1998). A specific growing technology that complements the expanding global need is e-learning.
In this study, e-learning was defined according to the National Center for Supercomputing Application (NCSA) e-learning group definition. This definition, states, "e-learning is the acquisition and use of knowledge distributed and facilitated primarily by electronic means" (Wentling, Waight, Gallaher, Fleur, Wang, & Kanfer, 2000, p. 5). According to Wentling, Waight, Gallaher, et al. this form of learning currently depends on networks and computers, but will likely evolve into systems consisting of a variety of channels (e.g., wireless, satellite), and technologies (e.g., cellular phones, PDA's) as they are developed and adopted. E-learning can take the form of courses as well as modules and smaller learning objects. E-learning may incorporate synchronous or asynchronous access and may be distributed geographically with varied limits of time (Wentling, Waight, Gallaher, et al.).
It is the flexibility of e-learning that makes it so usable and suitable for business and for the individual. With e-learning, individuals can be trained at a variety of locations, and often at their own convenience and pace, thus avoiding the time and expense associated with attending regular classes (Rosenberg, 2001; Urdan, & Weggen, 2000). Without an effective interface, however, e-learning cannot exist. A skillfully designed interface is able to draw the learner's attention, motivate him or her toward interaction with the system, and help him or her achieve learning goals without confusion and fatigue (Faiola, 1989; Galitz, 1989; Jacques, Preece, & Carey, 1995). This also contributes to the quality and usability of the system (Tufte, 1992).
In dealing with e-learning as a training tool, Wentling, Waight, Strazzo, File, Fleur, and Kanfer (2000) revealed that e-learning could become the major form of training and development within organizations. Specifically, Wentling, Waight, Strazzo, et al. indicated that e-learning infrastructure should be responsive to learner diversity. This diversity will extend but not be exclusive to age, nationality, ethnicity, educational background, intelligence levels, learning styles, language, and learners' needs. Although the growth of e-learning programs has been significant in recent years (Abernathy, 2000; Masie, 2000; Hoffer, 2000), the capabilities and efficacy of such programs have yet to be fully investigated. When coupling the growth of c-learning with an expanding global marketplace, it becomes imperative to gain a better understanding of the relationship between the user's cultural differences and the usability of e-learning systems.
According to del Galdo and Nielsen (1996), developing an interface for multiple cultures or for an unfamiliar culture requires the consideration of cultural dimensions such as language, social factors, rules, norms, and values. Modern interfaces include a large variety of dialogue components. These components include interactive objects such as menus, buttons, and multimedia presentation devices such as pictures, images, drawings, or video sequences (Pribeanu, 2000). The design of an interface thus becomes very important. It requires both the structuring of dialogue components into dialogue units that are compatible with the user and their task, and it requires their placement and dimensioning based on ergonomic and aesthetic criteria (Pribeanu, 2000).
In the context of Human Computer Interaction (HCI), the term usability generally refers to the ease of use and operational suitability of the interactive displays and controls that serve as the user interface to a computing system (Murphy, Norman, & Moshinsky, 1999). Usability is the measure of the quality of the user experience when interacting with something whether a website, a traditional software application, or any device the user can operate in some way or another (Nielsen, 1997a). It is often the most neglected aspect of web design, yet it is one of the most important aspects (Nielsen, 1997a, 1997b). Many web usability problems may also arise due to variations in behaviors and cultural differences. Such variations may be found in color, graphics, phrases, icons, character sets, pictures, symbols, date and time format, and so forth (Onibere, Morgan, Busang, & Mpoeleng, 2000). Users from different cultures may understand the same websites in totally different ways. Some metaphors, navigation, interaction, or appearance might be misunderstood and might confuse, or even offend those users (Mahemoff & Johnston, 1998; Marcus & Gould, 2000).
As e-learning grows, more organizations will conduct business globally, and thus more companies will be required to move training into the global arena. As a result, the biggest challenges will be overcoming the worldwide variations in social, cultural, political, and economic situations (Wellins & Rioux, 2000). Because of the greater amount of interaction on the worldwide business stage, many scholars are becoming increasingly concerned with the cultural differences among technology users (Branch, 1997; Hites, 1996; Kim, 1996; Petterson, 1982). According to del Galdo and Nielsen (1996) when a new system is developed, consideration of cultural variables benefits the usability and usefulness of the system. Nielsen (1993) indicated that a system must match the user's cultural characteristics. This goes beyond simply avoiding offensive icons; it must accommodate the way business is conducted and the way people communicate in various cultures. For instance, Dunbar (1991) indicated that technology is encoded with the characteristics of the culture that developed it. For example, individualistic values are implicit in the systems developed in the United States, whereas these techniques may be totally inappropriate for students from the collectivistic culture. Day (1991) indicated that cultural factors must be taken into account if human computer interfaces are to be effective, whether in the Third World or in the culturally diverse subcultures of industrialized countries.
Appropriate awareness of cultural differences and their effects on the individual user is vital to the success of e-learning systems. According to Chute and Shatzer (1995) instructional design strategies must consider cultural differences and their effects to promote successful teletraining experiences. Most research of cultural differences has been conducted primarily in the fields of anthropology, sociology, business, communication, or international, cross-cultural, multicultural education (Barrett, 1996; Demeester, 1999; Kim, 1996; Sherry, 1996). While those fields focus primarily on cultural differences and address academic and/or practical questions, this study focuses on the relationship between national culture and the usability of an e-learning system. Also, as trainers experiment with e-learning, they may encounter some rather difficult-to-define problems and, perhaps, misunderstandings of the interface that are likely to originate from differences in the user's background (Moore, 1986). In most corporate communication, collaboration, and training, there will be some cultural barriers to cross. Any training efforts in today's global organizations must take into account these potential barriers (Demeester). Sherry (1996) indicated that instructional designers need to address users' characteristics, including the awareness and responsiveness to cultural differences of the learners. According to Murphy (1990), the aspect of instructional design valuable for learning technology with respect to a user requires the course designer to design systems that are appropriate from the learner's perspective. Since e-learning is a new medium of learning delivery, it is not clear what the relationship between users' cultural backgrounds and the usability of e-learning systems will be.
McLoughlin, (1999), reported that e-learning systems often appear to be tailored to the needs of a particular cultural group, recognizing the specific learning needs, preferences and styles of a single perhaps homogeneous group of learners. However, in designing e-learning programs, there is a need to ensure flexibility and access to learners of "multiple cultures," because culture and learning are interwoven and inseparable. When users from different cultures interact with an e-learning system it can result in harmony or tension. If designers of e-learning systems do not recognize the significant impact that cultural and personal cognitive styles have in learning, the instructional design may be at odds with the learning styles of the learners (Henderson, 1996). According to Collis, Parisi, and Ligorio (1996) there is little existing research on instructional design for cross-cultural online learning programs. Wild and Henderson (1997) called for investigative research on cultural appropriateness of web-based instructional delivery. When coupling the growth of e-learning with globalization, it becomes imperative to gain a better understanding of the relationship between the user's cultural differences and usability of e-learning systems. This study, therefore, focused on the relationship between national culture and the usability of an e-learning system.
PURPOSE OF THE STUDY
The purpose of this study was to explore possible relationships between national culture and the usability of an e-learning system. The following research questions guided the study:
1. What is the relationship between power distance and the usability (learnability, memorability, and user's satisfaction) of an e-learning system?
2. What is the relationship between individualism/collectivism and the usability (learnability, memorability, and user's satisfaction) of an e-learning system?
3. What is the relationship between masculinity/feminity and the usability (learnability, memorability, and user's satisfaction) of an e-learning system?
4. What is the relationship between uncertainty avoidance and the usability (learnability, memorability, and user's satisfaction) of an e-learning system?
Theoretical Frameworks
The theoretical frameworks that guided this study are Hofstede's cultural dimensions, and Nielson's usability attributes.
Hofstede's Cultural Dimensions
Hofstede (1980) identified cultural dimensions that affected individuals' interactions in established systems, social organizations, and education. It was these factors that the researchers thought may influence user interface and web design. These national dimensions are: power versus distance, individualism versus collectivism, masculinity versus femininity, and uncertainty versus avoidance. Later, Hofstede (1997) produced a fifth dimension--long-term versus short-term orientation, which refers to the extent to which a culture programs its members to accept delayed gratification of their material, social, and emotional needs. Only the first four dimensions were used in this study because they were more focused on national culture and therefore more relevant to the study. Each of the first four dimensions are discussed in more detail.
The power distance (PD) cultural dimension focuses on the nature of human relationship in terms of hierarchy. It refers to the recognition and use of power in a society. This idea is often described in animal culture as a pecking order. Those at the top of the order have limited interaction with those below, and so forth. It is defined as "the extent to which the less powerful members of institutions and organizations accept that power is distributed unequally" (Hofstede & Bond, 1984, p. 419). Hofstede claimed that high PD countries tend to have centralized political power and exhibit tall hierarchies in organizations with large differences in salary and status. Low PD countries tend to view subordinates and supervisors as closer together and more interchangeable, with flatter hierarchies in organizations and less difference in salaries and status (Marcus & Gould, 2000). In other words, people in high power distance cultures are much more comfortable with a larger status differential than those in low power distance cultures.
The individualism versus collectivism cultural dimension focuses on the relationship between individual and the group. Highly individualistic cultures believe that the individual is the most important unit, while highly collectivistic cultures believe that the group is the most important unit. Some cultures consider relationships and trust important. Some prefer to work in teams and others prefer to work individually. According to Hofstede (1997), individualism pertains to societies in which the ties between individuals are loose and everyone is expected to look after himself or herself and his or her immediate family. Collectivism as its opposite pertains to societies in which people from birth onwards are integrated into strong cohesive ingroups, which throughout people's lifetime continue to protect them in exchange for unquestioning loyalty. In a collectivist culture (e.g., Japan), it is not likely that individuals explicitly show their strong opinions against their organizations. Their contributions to their organizations are appreciated as teamwork rather than as individuals. In a collective culture many people will not speak up when their bosses are present (Fernandez, 1995).
The masculinity versus femininity cultural dimension focuses on gender roles. Masculinity and femininity are defined as gender roles, not physical characteristics. High-femininity countries blur the lines between gender roles, while high-masculinity countries display traditional differences in how age, gender, and family are viewed. In different cultures different professions are dominated by different genders. Masculinity is seen as the trait, which emphasizes ambition, drive, acquisition of wealth, and differential gender roles. Femininity is seen as the trait, which stresses caring and nurturing behaviors, environmental awareness, sexual equality and more fluid gender roles. According to Hofstede (1997), masculine cultures tend to have very distinct expectations of male and female roles in the society. Those with feminine cultures have a greater ambiguity in what is expected of each gender.
The uncertainty avoidance cultural dimension focuses on how cultures adapt to changes and cope with uncertainty. It involves how cultures vary in the magnitude with which people vary in the extent that they feel anxiety about uncertain or unknown matters. This idea does not involve the more universal feelings of fear caused by known or understood threats, but the fear of potential difficulties, situations or events. Emphasis is on extent to which a culture feels threatened or is anxious about ambiguity. Strong or high uncertainty avoidance indicates that a culture tends to perceive unknown situations as threatening and that people will trend to avoid these situations. Weak or low uncertainty avoidance indicates that a culture is less threatened by unknown situations; therefore, they do not try to avoid uncertain situations. Cultures vary in their avoidance of uncertainty, creating different rituals and having different values regarding formality, punctuality, legal-religious-social requirements, and tolerance for ambiguity (Hofstede, 1997; Marcus & Gould, 2000). Hofstede indicated that in cultures with high uncertainty avoidance, businesses might have more formal rules, require longer career commitments, and focus on tactical operations rather than strategy.
It should be stated that cultural variables as defined by Hofstede (1980) were not designed specifically for usability studies. However, Hofstede's cultural dimensions have been used and tested by several researchers to explore influence on user-interface and systems design (Bernard, 2000; Dunbar, 1991; Evers & Day, 1997; Marcus & Gould, 2000). It is because of this research-based validation and the widespread acceptance of these factors in the business and education fields that these cultural dimensions were used in this study.
Nielsen's Usability Attributes
Nielsen (1993) defined the usability of a computer system in terms of learnability, efficiency, memorability, errors, and satisfaction. Efficiency was not be used in this study because it deals with a high level of productivity or effectiveness of an experienced user, which is beyond the scope of this study. Nielsen (1993, p. 26) usability attributes that are relevant to this study are described below.
Learnability focuses on the ease of leaning. A system should be easy to learn so that the user can rapidly start getting work done. To measure learnability, one picks individuals who have had no previous experience with the systems and measures the time it takes them to reach a specified level of proficiency in using the system.
Memorability focuses on how easy it is to remember how to use the system. It also shows how easily the casual user is able to return to the system after some period of not having used it, without having to learn everything all over again. A memory test may also be performed with users after they finish a test session with the system and ask them to explain the effects of various commands or to name the commands that perform certain functions.
Errors focus on the amount of inaccuracy displayed by users while they are using the system (e.g., the number of incorrect mouse clicks). It also explores the ease with which the users recover from any errors they make. The system should have a low error rate, so that users make few errors during use and to enhance performance.
Satisfaction focuses on how pleasant the systems is to use, so that users are subjectively satisfied when using it. Subjective satisfaction typically is measured by simply asking the users for their opinions. This is normally measured by a short questionnaire and/or interview that are given to users after a usability testing. Satisfaction can also be simply the result of how pleasing the interface seems to be to the individual user.
Nielsen's usability attributes have been used by many researchers to guide usability studies (Borges, Morales, & Rodriguez, 1995; Instone, 1997; Kurosu & Kashimura, 1995; Rajani & Rosenberg, 1999). It is because of their well research validation and their universal acceptance in the software engineering domain that they were selected for use in this study.
METHODOLOGY
This section describes the methodology aspect of the study. Included in these descriptions are discussions on the study's variables, population and sample, instrumentation, data collection, and data analysis procedures.
Study Variables
This study required the examination of usability and cultural variables that may influence the relationship between national culture and the usability of an e-learning system.
Usability Variables
Learnability (ease of learning), memorability (easy to remember), error (rate of errors), and satisfaction (user satisfaction) were the four usability attributes that were used to guide the usability aspect of this study (Nielson, 1993). In this study, error rate was incorporated into the learnability and memorability attributes. The four relevant attributes for this study are further explained next.
Learnability measured the time users took to reach a specified level of proficiency. In this study it was used to measure the time it took to perform various tasks and also measured their rates of error in performing the tasks. The rates of error were measured by comparing the number of users' clicks with the expected number of clicks required to accomplish a particular task.
Memorability measures user's memory about using a system or about certain features of the system. It focuses on how easy it is for users to remember the system being used. It measures the time it took the users to perform various tasks, and the routes taken (path) when performing the tasks. The rates of error were measured by comparing actual users' paths with the expected paths required to accomplish a particular task. Memorability had two parts: memorability time (MT) and memorability paths (MP).
Error measured the number of errors that occur while users were engaged in a task. An error rate was not used in this study as a separate variable. Rather, error was incorporated into the learnability and memorability attributes and was measured by comparing users' actual clicks to expected clicks required to perform a particular task. Expected clicks were based on average click rates from expert users of the system.
Satisfaction measured how pleasing the system was to users. It represented the degree to which a user's perceived personal needs and the need to perform specific tasks are adequately met by a system (Goodhue & Straub, 1991). Data regarding user satisfaction was collected using a post-task questionnaire to determine the participant's level of satisfaction with the system. Means and standard deviations were calculated for satisfaction. Each participant had one score for satisfaction.
Cultural Variables
The country index scores for the four cultural variables (power distance, individualism/collectivism, masculinity/femininity, and uncertainty avoidance) in this study were based on Hofstede (1980), values survey results collected within subsidiaries of one large multinational business organization (IBM) in 72 countries. The survey was conducted twice, between 1967 and 1973 and produced answers to more than 116,000 questionnaires. The four dimensions on which country cultures differ were revealed through theoretical reasoning and statistical analysis.
The following cultural variables: power distance (PD), individualism/collectivism (INDCOL), masculinity/femininity (MASFEM), and uncertainty avoidance (UA) were used to guide the cultural aspect of this study (Hofstede, 1980). Each cultural variable is described next.
Power distance is defined "as the extent to which the less powerful members of institutions and organizations accept and expect that power is distributed unequally" (Hofstede & Bond, 1984, p. 419). The Power Distance Index (PDI) developed by Hofstede (1980) was used in this study. It has a value between 0 (Low Power Distance, LPD) and 100 (High Power Distance, HPD), but values below 0 and above 100 are technically possible. Thus a score near 0 reflects the least acceptance of the unequal distribution of power while a score near 100 reflects the greatest acceptance of unequal distribution of power within one's culture. For this study a value less than 50 represented LPD and a value of 50 or more represented HPD. The power distance scores represent the relative, not the absolute positions of individual members of the countries.
Individualism and collectivism variables focus on the relationship between the individual and groups. Highly individualistic cultures believe that the individual is the most important unit while highly collectivistic cultures believe that the group is the most important unit (Hofstede, 1980). The variables values typically are between 0 (Strong Collectivist, COLL) and 100 (Strong Individualist, IND), but values below 0 and above 100 are technically possible. For this study a value less than 50 represented COLL and a value of 50 or more represented IND. The individualism and collectivism values represent the relative, not the absolute positions of individual members of the countries.
Masculinity and femininity variables represent one of the dimensions of national culture. "Femininity stands for a society in which social roles overlap; both men and women are supposed to be modest, tender, and concerned with the quality of life" (Hofstede, 1997, p. 261). In contrast, masculine cultures have very distinct expectation of male and female roles in society. The variables values typically are between 0 (Strongly Feminine, FEM) and 100 (Strongly Masculine, MAS), but values below 0 and above 100 are technically possible. For this study a value less than 50 represented FEM and a value of 50 or more represented MAS. The femininity and masculinity values represent the relative, not the absolute positions of individual members of the countries.
Uncertainty avoidance (low and high) variables focus on the extent to which a culture feels threatened or anxious about ambiguity and how hard individuals will work to avoid it. These variables focus on how cultures adapt to change and cope with uncertainty. The variables values typically are between 0 (Low Uncertainty Avoidance, LUA) and 100 (High Uncertainty Avoidance, HUA), but values below 0 and above 100 are technically possible. For this study a value less than 50 represented LUA and a value of 50 or more represented HUA. The uncertainty avoidance variables represent the relative, not the absolute positions of individual members of the countries.
SAMPLE
The sample of this study was composed of 24 international students from a large university in the Midwest in the USA. When selecting the sample, a three step selection criteria were used: (a) culture, (b) formative years, and (c) computer experience. First, culture was one of the criteria used in selecting the sample for this study. The goal was to have a least three participants that represented the various cultural dimensions being studied. The Hofstede's (1997) cultural index table was used to determine the cultural value indices for the different countries. The second criterion used was that the student must have spent his/her formative years in his/her home country. Hambrick, Davison, Snell, and Snow (1998) referred to national culture as "the country in which an individual spent the majority of his/her formative years". In this study, the first 15 years were considered the formative years. The assumption is that individuals who live in a culture other than their native culture for a period of time, especially during their formative years, may begin to adapt to their adopted country's cultural perspective. The third criterion used was that the student must not be an advanced computer user. The researchers did not want to include participants who were advanced computer users because their computer abilities might have affected their usability of the system. An advanced computer user is described in this study as a student who is a computer science major, or who uses a computer to develop web sites, or who writes computer programs.
The sample was selected using student records from the Office of International Student Affairs at the large Midwest university in the USA. First, in order to have representation from all cultural dimensions used in this study and also to have representation of countries with high and low cultural index, the researchers using Hofstede's Cultural Index developed a list of 20 countries. The Office of International Student Affairs was given the list and asked to provide a list of students who were newly enrolled and who represented the different countries. The Office of International Student Affairs provided the researchers a list that included 200 students from the 20 countries. The list included names, phone numbers, and e-mail addresses of 200 students. Of the 200 international students on the list, the researchers were able to contact only 75 of the international students (via telephone or e-mail). Of the 75 students, 24 met both the criteria and agreed to participate in the study. The 24 participants represented 11 different countries. The countries included: Malaysia, 3 (12.5%); Guatemala, 3 (12.5%); Argentina, 3 (12.5%); South Korea, 3 (12.5%); Poland, 2 (8.3%); USA, 2 (8.3%); Nigeria, 2 (8.3%); Japan, 2 (8.3%); Indonesia, 2 (8.3%); New Zealand, 1 (4.2%); and Venezuela, 1 (4.2%).
The study participants represented all the cultural dimensions of the study. Table 1 shows the distribution of study participants based on their cultural dimensions.
PROFILE OF STUDY PARTICIPANTS
Of the 24 participants in this study, 12 (50%) were female and 12 (50%) were male. They represented 11 different countries from Africa, Asia, Europe, North America, South America, and Australia. All participants spent their formative years (first 15 years) in their home countries. Additionally, all participants from countries other than the United States had been in the United States for less than three years. In regard to the participants' ages, 1 (4.2%) was under 20 years of age, 5 (20.8%) were from 20 to 24 years old, 13 (54.2%) were from 25 to 29 years old, 3 (12.5%) were from 30 to 34 years old, and 2 (8.3%) were from 40 to 44 years. The participants represented various educational fields. Accounting, agricultural economics, agricultural science, business administration, civil engineering, community health, chemistry, communications, economics, engineering, food science, human resource development, leisure study, marketing, music, natural resources, and speech communications were the educational majors listed by the students. Of the 24 participants, 5 (20.8%) were undergraduate students, 15 (62.5%) were in a master's degree program, and 4 (16.7%) were doctoral students. All of the 24 study participants had basic computer skills (e.g., regular use of e-mail and a web browser).
Instrumentation
Three instruments were used in this study with each instrument collecting information regarding unique aspects of the research. Separate descriptions are provided for each of the three instruments, which include: User Background Information Form, User Tasks & Observation Guide, and Post-Tasks Questionnaire.
The User Background Form was developed by the researchers to captured demographic information regarding each user's nationality, educational level, major area of study, computer experience, and so forth. This questionnaire was piloted with a group of six international students at a large university in the Midwest of the USA, who were not included in the study.
The User Tasks & Observation Guide consisted of 10 tasks commonly performed by users of the e-learning system used in the study. The e-learning system used in the study was a multi media web based e-learning system designed for delivery of courses, modules, or workshops. The 10 tasks were related to using the e-learning system in general. Therefore, tasks such as logging into the system, locating specific modules within the system, locating the course syllabus, electronically submitting a class assignment, and opening synchronous class archives were used. The 10 tasks were purposefully ordered so that processes learned during the completion of one tasks would not directly impact the performance of later tasks. Clarity of language used in the task statements was established through a multistage review and testing process using four e-learning system experts and pilot tested with six international students who were representative of some of the 11 national cultures encountered in this study, but were not included in the study. As study participants completed each task, the researchers recorded the participant's performances in terms of time required (total seconds) and number of steps (mouse clicks) required for task completion. Included in the mouse click counts were intentional but errant clicks made by the study participants (e.g., incorrect navigational selection, etc.) This practice of recording errant clicks is consistent with established usability practices in research and industry and forms an integral component of system learnability measures (Nielson, 1993).
The Post-Tasks Questionnaire consisted of a 21 item, Likert scale-based survey, which was completed by the study participants immediately upon completion of the 10 tasks associated with the User Tasks and Observation Guide. The questionnaire targeted the study participants' opinions regarding issues such as satisfaction with navigation schemes, color selections, information presentation, page layout, and overall usability. This instrument measured the usability attribute of satisfaction. The instrument, itself, was generated through a multiple expert review process to ensure validity, clarity of language, and usability of data captured and pilot tested with six international students who were representative of some of the 11 national cultures encountered in this study, but not included in the study.
Data Collection Procedures
Data collection for Phase One focused on measuring learnability and error rate. Learnability and error rate measured the time it took to perform tasks and also the paths taken in performing the tasks. To begin the process, participants sat at a usability laboratory workstation equipped with the necessary software and hardware to complete each of the 10 tasks required of the users. The 10 tasks were related to the use of an e-learning system developed by staff at the National Center for Supercomputing Applications at the University of Illinois. At the workstation along with the participant was an observer who instructed the participants on their task and monitored their performances using the User Tasks & Observation Guide previously described. The observer handed each participant a printout stating the first of the 10 tasks to be completed. Together, the participant and observer read the printout to ensure proper understanding of the task statement. Once the participant understood what was being asked of him/her, the observer would say "begin" and start monitoring the participants' performance. Upon completion of the task, the observer would record the results for that task in the observation guide. This process was repeated until all 10 tasks were completed. At the completion of the tasks, participants were asked to return to the laboratory after two weeks for Phase Two of the data collection.
Data collection for Phase Two focused on measuring memorability and error rate. Memorability had two parts: Memorability Time (MT) and Memorability Paths (MP).
MT measures time participants spend on tasks. MP measure error rate by counting the number of clicks (actual paths as compared to expected paths). There were a total of 10 tasks in Phase Two. The task completion process served the same purpose as stated in data collection Phase One and the same procedures used in the first phase were used. It measured how easy it is to remember the system used in Phase One and the error rates of the participants two weeks later.
Upon completion of the 10 tasks, the study participants moved to another table at which time they completed the Posttasks Questionnaire and the User Background Information Form. Their responses were recorded on separate forms and stored for future analysis.
Data Analysis
Given the parameters of this study a Pearson correlation was deemed an appropriate statistic for data analysis. Inferential statistics presented an overall correlation between the Hofstede's Cultural Dimensions and the usability attributes. The intent of this correlation was to confirm any relationship between national cultural dimensions and the usability attributes. Specifically, bi-variate correlation using a product-moment correlation coefficient (also called the Pearson r) was used to test for relationships outlined in the research questions.
FINDINGS
This section describes the key findings emerging from this study. This section presents the findings organized around the four research questions of the study.
Research Question One: What is the Relationship Between Power Distance and the Usability of an E-Learning System?
The variables used in this question are Power Distance as identified by Hofstede (1980) and usability attributes (e.g., Learnability, Memorabilty, and Satisfaction) as identified by Nielsen (1993). For all four research questions, Satisfaction is further broken into: Satisfaction with Graphics (SATG), Satisfaction with Explanations (SATE), Satisfaction with Navigation (SATN), Satisfaction with Language (SATL), and Satisfaction with General Usage (SATGU).
Learnability indicates that the system is easy to learn so the user can rapidly start getting work done. This study focused on Learnability in the first phase. Learnability was measured by recording the time it took to reach a specified level of proficiency in using a system and the rate of error (paths taken) in performing the tasks.
Memorability indicates that the system is easy to remember, so a casual user is able to return to the system after some period of not having used it, without having to learn everything all over again. This study focused on Memorability in the second phase of the study. Memorability was measured by recording the paths taken and compared them to expected paths.
Satisfaction indicates that the system should be pleasant to use, so users are subjectively satisfied when using it. Satisfaction was measured by using a questionnaire to determine user satisfaction of the system.
Correlation analyses are reported in Table 2 for measures of power distance and the nine usability variables. The table revealed that none of the correlations between Power Distance and the nine usability variables were significantly different from zero at the p = .05 level. This suggests that Power Distance was not systematically related to any of the nine usability attributes.
Research Question Two: What is the Relationship Between Individualism/Collectivism and the Usability of an E-Learning System?
The variables used in this question are Individualism/Collectivism (INDCOL) as identified by Hofstede (1980) and usability attributes (e.g., Learnability, Memorabilty, and Satisfaction) as identified by Nielsen (1993).
Correlation analyses are reported in Table 2 for measures of Individualism/Collectivism and the nine usability variables. Although none of the correlations in Table 2 were significant at the p = .05 level, the respondents from individualistic culture (high INDCOL) had higher satisfaction with navigation (r = .35, p = .09).
Research Question Three: What is the Relationship Between Masculine/Feminine and the Usability of an E-Learning System?
The variables used in this question are Masculine/Feminine (MASFEM) attributes of culture as identified by Hofstede (1980) and usability attributes (e.g. Learnability, Memorabilty, and Satisfaction) as identified by Nielsen (1993).
Correlation analyses are reported in Table 2 for measures of Masculine/Feminine attributes and the nine usability variables. The table reveals that none of the correlations were significantly different from zero at the p = .05 level. This is an indication that MASFEM was not systematically related to any of the nine usability variables.
When comparing the usability scores, MASFEM were divided into high and low: Masculine (n = 14) and Feminine (n = 10). The t tests for independent means were then employed to compare these two groups for the nine usability attributes. High MASFEM (masculine) were compared with low MASFEM (feminine). The analysis revealed that feminine had higher scores for Learnability Time (p = .06) and Learnability Path (p = .02), while masculine had higher scores for Satisfaction with Navigation (p = .03) and Satisfaction with General Usage (p = .06).
Research Question Four: What is the Relationship Between Uncertainty Avoidance and the Usability of an E-Learning System?
The variables used in this research question are Uncertainty Avoidance as identified by Hofstede (1980) and usability attributes (Learnability, Memorabilty, and Satisfaction) as identified by Nielsen (1993).
Correlation analyses are reported in Table 2 for measures of Uncertainty Avoidance and usability variables. There is a significant relationship between Uncertainty Avoidance and Learnability Time. This is an indication that respondents with higher levels of Uncertainty Avoidance were likely to have higher scores for Learnability Time (r = .40, p = .05).
The relationship between Uncertainty Avoidance and the nine usability variables were further explored. The t test comparisons between those respondents with Low Uncertainty Avoidance scores (n = 8) and those with High Uncertainty Avoidance scores (n = 16) were also computed. Respondents in the Low Uncertainty Avoidance group had higher Memorability Path scores (p = .10), but no further significant difference was found.
USER SATISFACTION WITH THE E-LEARNING SYSTEM
This section further analyzes user satisfaction variables. The questionnaire used in this study consists of 21 items constructed on a 5-point Likert-type scale with anchors of 1 "Strongly Disagree," 2 "Disagree," 3 "Neutral," 4 "Agree," 5 "Strongly Agree." Percentage, means, and standard deviations are presented in Table 3. The means for items 10, 14, and 16 are reversed scores because the items were negatively worded.
The lowest mean score for the items in Table 3 was found in item 2 "The graphics are visually appealing" (M 3.33; SD .70). The highest mean scores were found in items 14, 15, 19, 20, and 21; "Using this system was a very frustrating experience" (M 4.38; SD 1.06) "I feel I could be a productive user of the system" (M 4.7; SD .56), "It was easy to learn to use this system" (M 4.2; SD .59), "When I made an error using the system, I was able to recover easily and quickly" (M 4.0; SD .59), "Overall, I am satisfied with the system" (M 4.0; SD .46).
Only three items had low frequency percentage. First, when asked if graphics are visually appealing, 46% indicated they agree with this statement, 42% were neutral about the statement, and 13% disagree. Second, when asked if the amount of screen explanation was adequate for performing the tasks, 46% indicated they agree, 8% indicated strongly agree, 38% were neutral about the statement and 8% disagree. Third, 54% agreed that the system was pleasant to use while 4% strongly agreed that it was pleasant to use, and 33% were neutral, and 8% disagree.
Although findings from this descriptive analysis did not suggest significant relationships between cultural dimensions and usability variables, the overall frequency analysis among usability attributes and cultural dimensions shows that the majority of the participants (92%) were satisfied with the system.
DISCUSSION AND IMPLICATIONS
First, the finding of this study revealed that Power Distance was not systematically related to any of the usability variables. This finding of no significant difference corresponds to that found by Lee (2000). Lee explored the role culture plays in how people interact with products and online systems through a web site developed for cross-cultural usability testing. Lee's study consisted of demographic, user-attitude, cultural variable, population-stereotype, subjective preference, and usability testing module. Although Lee found some differences in the depth of hierarchical structure of user-interface, cultural dimensions (e.g., Power Distance culture) was not found to bear any significant relationship with usability attributes (i.e., interaction style, subjective preference).
However, another study conducted by Zaharias, Vassilopoulou, and Poulymenakoua (2001), presented a different view. Zaharias, et al. examined learners' preferences of a web-based testing course in a real environment. A questionnaire was used to collect data regarding users' subjective preference while using an online course. They indicated that there was a relationship between respondents from Power Distance and Learnability. Specifically, in learnability, respondents believed that information displayed on the dialog boxes was easily understood, easy to read, and accurate.
The inconsistency in the findings between the current study, Zaharias et al. (2001), and Lee's (2000) study may be due to the following reasons. First, it may be due to the fact that Lee only focused on learnability, interaction style, and subjective preference. Also, it may due to the fact that Zaharias et al. focused on preferences of a web-based testing course in a real environment.
There could be other reasons for not finding a relationship between Power Distance and usability variables in this study. The lack of significant relationships might be a reflection of the influence of factors other than those explored in this study. For instance, some researchers (Onibere, Morgan, Busang, & Mpoeleng, 2000; Evers & Day, 1997) found earlier that many usability problems arise due to noncultural interface factors such as variations in color schemes, graphics, phrases, icons, character sets, pictures, symbols, date, and time formats.
Second, none of the correlations between Individualism/Collectivism and the usability variables were significantly different from zero at the p = .05 level. This suggests that Individualism/Collectivism was not systematically related to any of the usability attributes. Although none of the correlations were significant at the p = .05 level, the participants who had higher scores (more individualistic) had higher satisfaction with navigation (r = .35, p = .09).
Some researchers also found relationships between Individualism/Collectivism and usability variables (Vohringer-Kuhnt, 2001; Zaharias et al., 2001). Specifically, Vohringer-Kuhnt investigated cultural influences on the usability of globally used software products. The survey was conducted online by way of the Internet. The overall results revealed differences in the attitude towards usability across members of different national groups. The study shows that Individualism/Collectivism was significantly connected to the attitude towards product usability. Also, in a survey study of a web-based testing system, Zaharias et al. found a relationship between Collectivism and Learnability.
The findings from this study do not appear to be in complete accordance with the aforementioned studies. First of all, Vohringer-Kuhnt (2001) study was an online survey that investigated cultural influences on the usability of globally used software products while Zaharias et al.'s (2001) study was a survey study of a web-based testing system. Therefore the differences in research methodologies and focuses might have contributed to different findings. Also, it is important to note that often different users, even those with the same national cultural background, may have different perspectives, behaviors, and opinions; even perspectives that may be diametrically opposed.
Third, the findings of this study revealed that there was no significant relationship between Masculinity/Femininity and the usability variables. On the other hand when t tests for independent means were used to compare Masculine and Feminine for the nine usability attributes, subjects who were high on MASFEM (masculine) had higher scores for satisfaction with Navigation and with Satisfaction with General Usage than those who were low MASFEM (feminine). It implies that subjects who were high MASFEM (masculine) are more satisfied with the general usage of the e-learning system. This also implies that with the ever-growing global workforce, increased use of e-learning and virtual learning, and with increasingly blurred national lines, some commonly held views of cultural differences may no longer hold true.
While the present study found no significant relationship between Masculinity/Femininity and the usability variables, Zaharias et al. (2001), found a relationship between Masculinity and Learnability. The finding in this study conflicts with the research conducted by Zaharias et al. (2001). While Zaharias et al.'s findings may be valid; they are limited as they only focused on Masculinity and Learnability and did not consider other usability variables.
Fourth, the finding of this study revealed that there was a significant relationship between Uncertainty Avoidance and Learnability Time. This indicates that respondents with higher levels of Uncertainty Avoidance were likely to have higher scores for Learnability Time (r = .40, p = .05). The higher score in Learnabilty Time is an indication that the respondents spent more time using the system (they are slow in completing tasks). This finding may hint to the fact that various people with high uncertainty avoidance may feel threatened by risky or uncertain situations. The finding here is supported with Hofstede (1997) who indicated that High Uncertainty Avoidance culture viewed what is different or new as a threat.
Also t test comparisons between those respondents with Low Uncertainty Avoidance scores (n = 8) and those with High Uncertainty Avoidance scores (n = 16) revealed that the Low Uncertainty Avoidance group had slightly higher Memorability Path scores (p = .10). Memorability denotes the system should be easy to remember, so that the casual user is able to return to the system after some period of not having used it, without having to learn everything all over again. The higher memorability path score in this finding is an indication that the respondents had less click (less path) than expected. This finding supports the fact that high level risk taking characterizes Low Uncertainty Avoidance cultures; uncertainty is acceptable.
Although this study found a significant relationship between Uncertainty Avoidance and Learnability, the relationship between other usability variables (i.e., Memorabilty and Satisfaction) were not significant. The lack of significant relationships between other usability variables (i.e., Memorabilty and Satisfaction) could be a reflection of the influence of factors other than those explored in this study. For instance, earlier studies (Mahemoff & Johnston, 1998; Marcus & Gould, 2000) found that users from different cultures might understand similar websites and systems in totally different ways. This might be a reflection of the misunderstanding of certain metaphors, navigation cues, interaction, or systems' appearances by users. In their studies, Evers and Day (1997) indicated that Chinese (High Uncertainty Avoidance) found usefulness a more discernable variable. Indonesians (also High Uncertainty Avoidance) found ease of use more important.
To conclude, the overall strength of the relationship between national culture and the usability variables were not shown to be significant; the only significant relationship was found between Uncertainty Avoidance and Learnability Time. However, organizations conducting training or e-learning operations in cultures where power-centric, collectivist, and change accepting societies exist must give consideration to a few logistics. First, trainers need to consider the level of leadership expected by the learners. Learners from cultures where strong authority figures are common (e.g., those from high power indicator cultures) will expect greater leadership and guidance from their instructors. As a result, training might take on a more traditional teacher-centered approach, whereas individuals from other cultures may desire a more student-centered approach.
Another factor to consider is the level of group interaction and support offered to students. Training conducted in strong collectivist cultures might employ strategies where group work, collaboration, and socially oriented approaches are more prevalent. Conversely, training and e-learning activities in more individualistic societies might give the learners greater freedom in terms of creativity and expression of knowledge gained (e.g., alternative assessments) or possibly employ more competitive learning environments (i.e., normative testing and grading practices). An effective e-learning system needs to be usable: easy to learn and satisfying to use. The findings in this study should help e-learning system designers to design effective and appropriate e-learning systems that will meet the needs of users from various cultural backgrounds. The findings could provide educators with information that could assist them in understanding the cultural implications of using e-learning systems in various cultural settings.
In today's increasingly global market, many e-learning systems designers are faced with the task of ensuring that their systems are equally as usable in foreign countries as in the United States. Given the impact that culture has on people's behavior, truly functional global e-learning systems should reflect the cultural orientation of its users and not just be a translation of an American interface. Conceptual localization that fits the user's culturally specific mental model of the c-learning system with functionality, feedback, and support for learning is a much more effective way to design e-learning systems for global use.
It should be noted that this study is subject to some limitations. It used only Hofstede's (1980) Cultural Dimensions to guide the cultural aspect of this study; therefore, the result of this study may be limited to the likelihood of omitting important local facets of culture that were present but not captured by the Hofstede's Cultural Dimensions. It is also important to note that if other cultural models (Hall, 1990; Triandis, 1994; Trompenaars, 1993; Victor, 1992) were used, it is possible that different findings would have resulted from the study. Although these other cultural models have some elements that could have applied to this study, the researchers felt that the most relevant model for this study was the Hofstede's cultural dimensions because Hofstede's model has been used and tested by many researchers (e.g., Bernard, 2000; Dunbar, 1991; Evers & Day, 1997; Fernandez, 1995; Marcus & Gould, 2000), to explore influence on user-interface and systems design.
Another limitation of the study is the sample size and representativeness of the sample to their represented national cultures. The sample size of 24 participants is relatively small and if a greater number of participants would have been used it would have increased the generalizability of the study's immediate findings. However, bearing in mind the comparatively small amount of research to date in cross-cultural testing of e-learning systems the researchers chose to utilize a smaller sect of participants in order to gather some basic information that is necessary to control and refine exploratory studies in this emerging area of e-learning.
More importantly, this study established a new foundation for further research. For researchers, this study brings together a range of usability testing methodologies and provides structure and direction for expanding the scope of inquiry for usability of other e-learning systems. Although the sample of this study was relatively small, future research could validate this study by researching different types of e-learning systems and with larger sample sizes to make the results more generalizable. Further comparison of the results from this study with results obtained from larger, representative user studies of similar systems will add to the value of this study. Also, further work will validate the tools and the approach used in this study. Further research is needed to determine the impact of culture on interface perception and modeling to assist in setting a standard practice for e-learning system design.
CONCLUSIONS
Based on the findings of this study, a summary of the final conclusions are presented. The conclusions are presented according to the major research questions of the study.
Research Question One: The Relationship Between Power Distance and the Usability of an E-Learning System
1. This study revealed that Power Distance was not systematically related to any of the usability variables. Correlation analysis and comparison for t tests of independent means revealed no significant relationship.
2. The findings of this study may hint to the fact that various cultures have different preferences, satisfactions, and understandings of e-learning systems.
Research Question Two: The Relationship Between Individualism/Collectivism and the Usability of an E-Learning System
3. There was no significant relationship between Individualism/Collectivism and the usability variables.
4. The participants who had higher scores (more individualistic) had higher satisfaction with navigation.
5. The findings imply that often different users, even those with the same national cultural background, may have different perspectives, behaviors, and opinions; even perspectives that may be diametrically opposed.
Research Question Three: The Relationship Between Masculinity/Femininity and the Usability of an E-Learning System
6. The correlation analysis revealed that there was no significant relationship between Masculinity/Femininity and the usability variables.
7. Comparison of t tests for independent means revealed that subjects who were high on MASFEM (masculine) had higher scores for satisfaction with Navigation and Satisfaction with General Usage than those subjects who are low MASFEM (feminine).
8. The finding in this research question implies that subjects who were high on MASFEM (masculine) are more satisfied with the general usage of the e-learning system.
Research Question Four: The Relationship Between Uncertainty Avoidance and the Usability of an E-Learning System?
9. Correlation analysis revealed that there was a significant relationship between Uncertainty Avoidance and Learnability Time. It shows that the respondents spent more time using the system (they are slow in completing tasks). This finding may hint to the fact that various people with high uncertainty avoidance may feel threatened by risky or uncertain situations.
10. Also t test comparisons between respondents with low Uncertainty Avoidance scores and High Uncertainty Avoidance scores revealed that the Low Uncertainty Avoidance group had slightly higher Memorability Path scores which is an indication that the respondents remember how to use the system.
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BLESSING ADEOYE
Nationwide Technologies, Nigeria
adeoye1@msn.com
ROSE MARY WENTLING
University of Illinois-Urbana, USA
rmcwent@uiuc.edu
Table 1 Distribution of Participants Based on Their Cultural Dimensions (n=24) Total Number of Participants Cultural variables Representing Each Cultural Variable LPD 3 HPD 21 IND 5 COLL 19 MAS 13 FEM 11 LUA 8 HUA 16 Note: The number of participants is more than 24 because every country contains participants that fall into more than one cultural variable. Table 2 Overall Pearson Correlations Matrix for Usability Attributes and Cultural Dimensions (N=24) Variables LT LP MT MP SATG SATE SATN SATL SATGU PD INDCOL MASFEM LP .63 .00 MT .57 .58 .00 .00 MP .16 .66 .61 .46 .00 .00 SATG -.14 -.20 -.06 -.29 .51 .36 .78 .17 SATE -.14 .15 .14 .12 .10 .51 .50 .52 .57 .65 SATN -.28 -.05* -.01 .10 .56 .34 .18 .81 .97 .66 .00 .11 SATL -.08 .16 -.11 .25 .04 .16 .40 .71 .46 .62 .25 .84 .45 .00 SATGU -.30 -.02 .04 .04 .32 .55 .32 .42 .16 .92 .83 .87 .13 .04 .01 .13 PD .19 .05* .08 .10 -.28 -.11 -.19 -.02 -.07 .37 .81 .70 .63 .19 .62 .38 .95 .75 INDCOL -.32 -.20 -.15 -.14 .20 .35 .25 .27 -.74 .21 .12 .34 .48 .51 .35 .32 .09 .25 .21 .00 MASFEM -.16 -.26 .14 .11 .00 .28 .04 .20 -.42 .52 -.07 .45 .23 .53 .61 1.00 .75 .19 .86 .35 .04 .52 UA .40 -.08 .06 -.29 .28 -.31 -.17 -.07 -.10 -.08 -.19 .09 .05* .70 .79 .17 .19 .15 .42 .73 .63 .70 .37 .69 Usability attributes and cultural dimensions abbreviations are explained as follow. Learnabilty Time (LT), Learnability Path (LP), Memorability Time (MT), Memorability Path (MP), Satisfaction with Graphics (SATG), Satisfaction with Explanations (SATE), Satisfaction with Navigation (SATN), Satisfaction with Language (SATL), and Satisfaction with General Usage (SATG U). Power Distance (PD), Individualism/Collectivism (INDCOL), Masculine/Feminine (MASFEM), Uncertainty Avoidance (UA). *p = .05 Table 3 User Satisfaction Ratings and Comments (N = 24) % Questionnaire items SD D N A SA M SD 1. The graphics used allowed 0 4.2 16.7 70.8 .3 3.83 .64 me to find information quickly 2. The graphics are visually 0 12.5 41.7 45.8 0 3.33 .70 appealing 3. The amount of graphics was 0 8.3 8.3 62.5 20.8 3.96 .81 appropriate 4. The color schemes used in 4.2 8.3 20.8 37.5 29.2 3.79 1.10 the system are helpful 5. The amount of screen 0 8.3 37.5 45.8 8.3 3.54 .78 explanation was adequate for performing the tasks 6. Navigational features are 4.2 8.3 8.3 50.0 29.2 3.92 1.06 consistent throughout the system 7. Menus in the system were 0 20.8 20.8 41.7 16.7 3.54 1.02 self-explanatory 8. Display messages were easy 4.2 0 33.3 41.7 20.8 3.75 .94 to understand and free of jargon 9. The directions provided 0 16.7 12.5 54.2 16.7 3.71 .95 with the system were clear 10. I had problems and lost my 0 20.8 25.0 41.7 12.5 3.46 .98 place in the system 11. The use of terms throughout 0 8.3 16.7 62.5 12.5 3.79 .78 the system is consistent 12. Terminology used in the 0 8.3 12.5 70.8 8.3 3.79 .72 system is easy to understand 13. It is easy to remember the 0 16.7 8.3 41.7 33.3 3.92 1.06 terms used in the system 14. Using this system was a 0 0 4.2 54.2 41.7 4.38 .58 very frustrating experience 15. I feel I could be a 0 0 8.3 66.7 25.0 4.17 .56 productive user of the system 16. I felt that many of the 0 4.2 29.2 50.0 16.7 3.79 .78 things I did with the system may have been wrong 17. The system was very 0 8.3 33.3 54.2 4.2 3.54 .72 pleasant to use 18. It is easy to remember how 0 8.3 25.0 41.7 25.0 3.83 .92 to locate information in the system 19. It was easy to learn to use 0 0 8.3 62.5 29.2 4.21 .59 this system. 20. When I made an error using 0 0 16.7 66.7 16.7 4.00 .59 the system, I was able to recover easily and quickly 21. Overall, I am satisfied 0 0 8.3 79.2 12.5 4.00 .46 with the system. Questionnaire ratings are explained as follow SD indicates "Strongly Disagree," D "Disagree," N "Neutral," A "Agree," SA "Strongly Agree." The means for items 10, 14, and 16 are reversed scores because the items were negatively worded.
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| Author: | Adeoye, Blessing; Wentling, Rose Mary |
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| Publication: | International Journal on E-Learning |
| Geographic Code: | 1USA |
| Date: | Jan 1, 2007 |
| Words: | 10805 |
| Previous Article: | Engagement in professional online learning: a situative analysis of media professionals who did not make it. |
| Topics: | |