Article 2: Predicting intraindividual changes in teacher burnout: The role of perceived school environment and motivational factors
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    Article 2: Predicting intraindividual changes in teacher burnout: The role of perceived school environment and motivational factors Article 2: Predicting intraindividual changes in teacher burnout: The role of perceived school environment and motivational factors Document Transcript

    • Service quality in higher education: The role of student expectations Roediger Voss a , Thorsten Gruber b , Isabelle Szmigin c,⁎ a University of Education Ludwigsburg, Pädagogische Hochschule Ludwigsburg, Institut für Bildungsmanagement Postfach 220, 71602 Ludwigsburg, Germany b The University of Manchester, Manchester Business School, MBS West, Booth Street West, Manchester M15 6PB, United Kingdom c The University of Birmingham, Birmingham Business School, University House, Birmingham B15 2TT, United Kingdom Received 1 June 2006; received in revised form 1 December 2006; accepted 1 January 2007 Abstract The study aims to develop a deeper understanding of the teaching qualities of effective lecturers that students desire and to uncover the constructs that underlie these desire expectations to reveal the underlying benefits that students look for. An empirical study using the means–end approach and two laddering techniques (personal interviews and laddering questionnaires) gives a valuable first insight into the desired qualities of lecturers. While the personal laddering interviews produced more depth in understanding, the results of the two laddering methods are broadly similar. The study results indicate that students want lecturers to be knowledgeable, enthusiastic, approachable, and friendly. Students predominately want to encounter valuable teaching experiences to be able to pass tests and to be prepared for their profession. This study also shows that students' academic interests motivate them less than the vocational aspects of their studies. © 2007 Published by Elsevier Inc. Keywords: Service quality; Higher education; Means–end; Laddering 1. Introduction In January 2005, Germany's highest court overturned a federal law that had banned the introduction of fees, thereby paving the way for universities to charge student tuition fees for the first time. By 2009/2010 German universities will also switch to the two-cycle system of higher education (bachelor– master) to achieve the Bologna objectives; all German students will be able to complete a Bachelor degree at one university and follow this with a master's degree at a different university. One consequence of these changes is that German universities need to pursue a more customer friendly approach with the aim of retaining students for postgraduate study as evidence shows that the recruitment of students is several times more expensive than their retention (Joseph et al., 2005). The new environment will also force German universities to compete for the best students and to monitor the quality of the educational services they offer more closely in order to retain current students and attract new ones. Students in Germany will probably also become more selective and demanding, making the understanding of student expectations a priority for universities. Student expectations are a valuable source of information (Sander et al., 2000; Hill, 1995). New undergraduate students may have unrealistic expectations of the university experience and if higher education organizations have a good understand- ing of such students' expectations, they should be in a better position to both manage and bring them to a realistic level. Universities could for example inform students of what is realistic to expect from lecturers (Hill, 1995). The knowledge of student expectations can also help lecturers in the design of teaching programs (Sander et al., 2000). Hill (1995) finds that student expectations in general and the expectations of academic aspects of higher education services such as teaching quality, teaching methods, and course content in particular, are quite stable over time. Telford and Masson (2005) point out that the perceived quality of the educational service depends on students' expectations and values. They cite several studies that indicate the positive impact of expectations and values on variables such as student participation (Claycomb et al., 2001), role clarity, and motivation to participate in the service encounter (Lengnick-Hall et al., 2000; Rodie and Kleine, 2000). Such work clearly points to the importance of Journal of Business Research 60 (2007) 949–959 ⁎ Corresponding author. E-mail addresses: (R. Voss), (T. Gruber), (I. Szmigin). 0148-2963/$ - see front matter © 2007 Published by Elsevier Inc. doi:10.1016/j.jbusres.2007.01.020
    • understanding expectations and values of students in higher education. This paper investigates the nature of service quality in higher education and in particular what qualities and behaviors students expect from their lecturers. The paper begins by reviewing the literature on service quality in higher education and the role of the lecturer, and then describes a study that uses the means–end approach and laddering technique to develop a deeper understanding of the attributes of lecturers preferred by students. The study uncovers constructs that underlie students' desire expectations and the paper concludes with a summary of findings and suggestions for further research. 2. Quality in higher education and the role of lecturers Quality in higher education is a complex and multifaceted concept and a single appropriate definition of quality is lacking (Harvey and Green, 1993). As a consequence, consensus concerning “the best way to define and measure service quality” (Clewes, 2003, p. 71) does not as yet exist. Every stakeholder in higher education (e.g., students, government, professional bodies) has a particular view of quality dependent on their specific needs. This paper is concerned with one particular stakeholder in higher education, students, and as outlined above, the introduction of tuition fees and the new degree structure, is likely to increase the attention which German universities will pay to this stakeholder's requirements. The services literature focuses on perceived quality, which results from the comparison of customer service expectations with their perceptions of actual performance (Zeithaml et al., 1990). Thus, O'Neill and Palmer (2004, p. 42) define service quality in higher education as “the difference between what a student expects to receive and his/her perceptions of actual delivery”. Guolla (1999) shows that students' perceived service quality is an antecedent to student satisfaction. Positive perceptions of service quality can lead to student satisfaction and satisfied students may attract new students through word-of-mouth communication and return themselves to the university to take further courses (Marzo- Navarro et al., 2005; Wiers-Jenssen et al., 2002; Mavondo et al., 2004; Schertzer and Schertzer, 2004). Zeithaml et al. (1993) distinguish between three types of service expectations: desired service, adequate service, and predicted service. Customers have a desired level of service which they hope to receive comprising what customers believe can be performed and what should be performed. Customers also have a minimum level of acceptable service as they realize that service will not always reach the desired levels; this is the adequate service level. Between these two service levels is a zone of tolerance that customers are willing to accept. Finally, customers have a predicted level of service, which is the level of service they believe the company will perform. This paper examines how lecturers should behave and which qualities they should possess (desire expectations) from a student's point of view. The issue of customer expectations in general and desire expectations in particular is still a neglected area (Yim et al., 2003; Pieters et al., 1998). Customers can use such desire expectations as reference standards for satisfaction judgments (Singh and Widing, 1991). In addition, Zeithaml et al. (1993) point out that desire expectations are more stable and less dependent on the particular service situation than other types of expectations. Thus, examining the nature of desire expectations is an important contribution to the area of service quality in higher education. Pieters et al. (1998, p. 757) suggest that the “extent to which customers attain their goals depends partly on the behavior of service employees” and Oldfield and Baron (2000) characterize higher education as a “pure” service and point to the importance of the quality of personal contacts. Thus, the underlying assumption of this paper is that for students, the qualities and behaviors of lecturers have a significant impact on their perceptions of service quality. Several research findings in the services literature support this assumption; Hartline and Ferrell (1996) for example believe that the behaviors and attitudes of customer contact employees primarily determine the customers' perceptions of service quality. Studies also indicate that the human interaction element is essential to determine whether customers consider service delivery satisfactory (Chebat and Kollias, 2000). Bitner et al. (1994) recognize that in services, the nature of the interpersonal interaction between the customer and the contact employee often affects satisfaction. In the context of higher education, Hansen et al. (2000) develop a valid instrument to evaluate modules or units of study. Their findings indicate that the instructional quality of the lecturer is the main influence on the perceived quality of modules. Likewise, Hill et al. (2003) find that the quality of the lecturer belongs to the most important factors in the provision of high quality education. Pozo-Munoz et al. (2000, p. 253) maintain that “teaching staff are key actors in a university's work”. Therefore, the behaviors and attitudes of lecturers should be the primary determinant of students' perceptions of service quality in higher education. If lecturers know what their students expect, they may be able to adapt their behavior to their students' underlying expectations, which should have a positive impact on their perceived service quality and their levels of satisfaction. Given the current lack of knowledge concerning desire expectations (Pieters et al., 1998) the research study will be explorative in nature. The study aims to develop a deeper understanding of the attributes (qualities and behaviors) of effective lecturers that students desire and to uncover the constructs that underlie these desire expectations and reveal the underlying benefits students look for. To address these issues, the research study uses a semi-standardized qualitative tech- nique called laddering as O'Neill and Palmer (2004, p. 41) suggest that qualitative methods “provide an interesting insight into the mindset of individual students”. Laddering allows researchers to reach deeper levels of reality and to reveal what Gengler et al. (1999 p. 175) refer to as the “reasons behind the reasons”. Apparently, no research study applies the means–end chain framework and the laddering technique to the issue of service quality in higher education. The paper details how the means–end approach is appropriate and useful in this research study. Another aim of this paper is to compare two laddering techniques (laddering interviews and laddering questionnaires) to see whether as Grunert et al. (2001, p. 72) suggest, “different 950 R. Voss et al. / Journal of Business Research 60 (2007) 949–959
    • techniques may lead to different sets of attributes, leading to the measurement of different excerpts from cognitive structure”. 3. Means–end chain approach and laddering technique The means–end chain approach (Gutman, 1982; Howard, 1977; Olson and Reynolds, 1983; Young and Feigin, 1975) attempts to discover the salient meanings that consumers associate with products, services and behaviors. The focus is on associations in the consumer's mind between the attributes of products, services or behaviors (the means), the consequences of these attributes for the consumer, and the personal values or beliefs (the ends), which are strengthened or satisfied by the consequences. These linkages between attributes, consequences and values are the means–end chains, the mental connections that link the different levels of knowledge (Reynolds et al., 1995). Grunert et al. (2001, p. 63) describe the means–end approach as “one of the most promising developments in consumer research since the 1980s”. Researchers are able to examine the consumer's individuality in depth while still producing quantifiable results. Early work in this area helps to resolve product — or brand positioning problems and to link the consumer's product knowledge to his/her self-knowledge (Gutman, 1982; Olson and Reynolds, 1983). Researchers apply the means–end framework to the domain of consumer behavior (e.g., Bagozzi and Dabholkar, 1994; Pieters et al., 1995, 1998), sales management (e.g., Botschen et al., 1999; Deeter-Schmelz et al., 2002; Reynolds et al., 2001), and strategic marketing (e.g., Norton and Reynolds, 2001; Reynolds and Rochon, 2001). This research suggests that the ability of students to attain their personal goals and values (ends) depend to a certain degree on the qualities and behaviors of lecturers (means) during the personal interaction in class. The means–end approach assumes consumer knowledge to be hierarchically organized, spanning different levels of abstraction in the consumer's memory (Reynolds et al., 1995). At higher levels of abstraction, the connections to the self are more direct and stronger than at lower levels of abstraction. Such an approach assumes that the extracts from the cognitive structure are of linear type with cognitive concepts linked by one-to-one associations. The interviewer deduces this linear structure from a possibly larger cognitive network during the laddering interview (Grunert and Grunert, 1995). Researchers criticize the means–end approach for assuming a hierarchical knowledge structure (Herrmann, 1996) while modern cognitive psychology research indicates that cognitive structures are complex networks. Van Rekom and Wierenga (2002) for example present knowledge representations as association patterns or semantic networks (Chang, 1986). In this alternative model, consumers have patterns of interconnected concepts in their minds, with each concept gaining meaning from links with other concepts. Van Rekom and Wierenga (2002) also stress the importance of the network over the hierarchies within the network. Olson and Reynolds (2001) reinforce this issue by maintaining that the critical elements of networks are the connections between components, the attributes, consequences and values, as they carry the weight of the meaning. Following this development in thinking, the current study is primarily interested in the relations between the concepts of meaning both as hierarchies and within the broader framework of the network. 4. Two laddering methods: soft and hard laddering This section of the paper considers in more detail two alternative methods, soft and hard laddering (Botschen and Thelen, 1998; Grunert et al., 2001). Soft laddering involves in- depth interviews with respondents following as far as possible their natural flow of speech; the researcher aims to understand the meaning of the given answers and to link them to the means– end model (Grunert et al., 2001). Hard laddering uses data collection techniques (interviews and questionnaires) where respondents have to “produce ladders one by one and to give answers in such a way that the sequence of the answers reflects increasing levels of abstraction” (Grunert et al., 2001, p. 75). In soft laddering the approach is to use semi-standardized qualitative in-depth interviews during which interviewers follow a process of digging deeper by asking probing questions to reveal attribute–consequence–value chains by taking the subject up a ladder of abstraction (Reynolds and Gutman, 1988). Prior to laddering, an elicitation stage takes place to derive preference based distinction criteria (Grunert and Grunert, 1995; Reynolds and Gutman, 1988). Techniques such as triadic sorting, direct elicitation or free sorting may be used, although research shows that complex methods are time consuming and do not outperform free sorting techniques such as direct questioning and ranking (Bech-Larsen and Nielsen, 1999). The derived criteria from the elicitation stage act as the opening for the laddering probes to uncover the complete means–end structure which will reveal cognitive relationships of personal relevance to the respondent (Gengler and Reynolds, 1995). For this, the interviewer repeatedly questions why an attribute/consequence/value is important to the respondent. The answer to this question serves as the starting point for further questioning. Although the majority of published means–end chain studies employ in-depth laddering interviews (Botschen and Thelen, 1998), some use questionnaires (hard laddering). In 1991, Walker and Olson (1991) developed a paper-and-pencil version of the laddering interview where respondents fill in a structured questionnaire identifying up to four attributes that are of relevance to them and then giving up to three reasons why each attribute is of importance (Botschen and Hemetsberger, 1998). The main advantage of the paper-and-pencil version is the lack of interviewer bias (Botschen and Hemetsberger, 1998) and with no social pressure involved, respondents themselves decide when they want to end the laddering process. According to Botschen et al. (1999), another advantage of the paper-and- pencil version in comparison to the traditional in-depth interviewing technique is the cost-efficient data collection. Several examples of successful projects employ the paper-and- pencil version (e.g., Botschen and Hemetsberger, 1998; Botschen and Thelen, 1998; Pieters et al., 1995; Goldenberg et al., 2000). Fig. 1 presents the laddering questionnaire used in this research study. Having outlined the means–end approach and the two laddering techniques used in the study, the next section covers 951R. Voss et al. / Journal of Business Research 60 (2007) 949–959
    • the research carried out to explore the desired expectations of teacher education students in general and to reveal the desired attributes (qualities and behaviors) of lecturers in particular. As stated, one aim of this paper was to compare these two laddering techniques and investigate whether the techniques would lead to different results. 5. The study Laddering interviews and questionnaires took place amongst students at a European University of during 2004 and 2005. The researchers conducted personal laddering interviews with twenty-nine students aged between 19 and 33 years (X=22.6) and handed out laddering questionnaires to 53 students aged between 19 and 32 years (X=22.9). Respondents enrolled in two business management courses and took part on a voluntary basis. Grunert and Grunert (1995) suggest that researchers should collect ladders that are from a group of homogeneous respondents, and teacher education students at this university all have similar backgrounds, come from the surrounding area, and have the common goal of wanting to become teachers. The number of conducted interviews and distributed questionnaires was theory-driven as qualitative researchers should always theoretically reflect on gathered data to decide whether to collect more. Researchers should sample respondents until they believe that their categories achieve theoretical saturation. Theoretical saturation means that no new or relevant data emerge concerning a category, that the category is well- developed, and that the linkages between categories are well- established (Strauss and Corbin, 1998). Qualitative researchers face the problem of not knowing the optimum minimum sample size at the start of a study (Bryman, 2004). The study originally planned to hand out 78 laddering questionnaires in three courses. Analysis of the questionnaires from the first two courses, however, showed that respondents did not provide any new categories. As the categories reached theoretical saturation, no additional questionnaires were necessary from the third course thus completing the laddering process after 53 questionnaires. Similarly, the categories based on the laddering interviews reached theoretical saturation after 29 interviews. Table 1 sums up the details of the two samples. 6. Data analysis and results The analysis of the means–end data comprised of three stages (Reynolds and Gutman, 1988). Firstly, the coding of sequences of attributes, consequences and values (the ladders) takes place in order to make comparisons across respondents using the software program LADDERMAP (Gengler and Reynolds, 1993). LADDERMAP allows entry of up to ten chunks of meaning per ladder and to categorize each phrase as an attribute, consequence or value. The second phase involved the development of meaningful categories by grouping together phrases with identical meanings. The identification of catego- ries was through phrases and key words that respondents used during the interviews and from concepts derived from the literature review. For example, if respondents mentioned that lecturers should have sufficient knowledge of the subject they teach, this statement linked to the concept “expertise”. The research followed an iterative process of recoding data, splitting, combining categories and generating new or dropping existing categories, followed by an aggregation of codes for Fig. 1. Paper-and-pencil version of laddering. Source: Adapted from Pieters et al. (1998, p. 760) and Botschen and Hemetsberger (1998, p. 154). Table 1 Characteristics of samples Number of respondents Gender Age Female Male Min Max Average Laddering interviews 29 17 (59%) 12 (41%) 19 33 22.6 Laddering questionnaires 53 34 (64%) 19 (36%) 19 32 22.9 952 R. Voss et al. / Journal of Business Research 60 (2007) 949–959
    • individual means–end chains across subjects. A matrix presented the aggregations to express the number of associations between the conceptual meanings (attributes/consequences/ values). This implications matrix details the associations between the constructs and acts as a bridge between the qualitative and quantitative elements of the technique by showing the number of times one code leads to another (Deeter-Schmelz et al., 2002). Finally, the research generates a Hierarchical Value Map (HVM) that Gengler et al. (1995, p. 245) define as “a graphical representation of a set of means–end chains which can be thought of as an aggregate (e.g., market-level) cognitive structure map”. The map consists of nodes, which stand for the most important attributes/consequences/values (conceptual meanings) and lines, which represent the linkages between the concepts. The map graphically sums up the information collected during the laddering interviews (Claeys et al., 1995). To ensure readability and usefulness, the map only displays associations up to a specific “cutoff” level, which meant that a certain number of respondents had to mention linkages in order for the map to include them. For example, a cutoff level of 1 means that the map includes every connection between constructs mentioned by respondents. The resulting HVM is “a mass of links and concepts that usually is unintelligible” (Christensen and Olson, 2002, p. 484). The higher the chosen cutoff level is, the more linkages and constructs of meaning disappear and the more interpretable the map becomes. However, if the cutoff level is too high, too many constructs will have disappeared and the resulting map will not be interesting. Researchers, therefore, have to find a balance between data reduction and retention (Gengler et al., 1995) and between detail and interpretability (Christensen and Olson, 2002) to create a clear and expressive map with sufficient information. The HVM based on the interviews only displays associations beyond the cutoff level of 4, which means that the map only graphically represents linkages that at least 4 respondents mentioned during the interviews. The chosen cutoff level creates a map that keeps the balance between data reduction and retention and between detail and interpretability. Similarly, the study applies a cutoff level of 5 for the HVM based on the questionnaires. The two hierarchical value maps in Figs. 2 and 3 reveal that the most critical attributes of lecturers are: teaching skills, teaching methods, communication skills, approachability, enthusiasm, expertise, humor, and friendliness. These findings are similar to previous research that indicates the importance of these instructor factors (e.g., Patrick and Smart, 1998; O'Toole et al., 2000; Willcoxson, 1998; Westermann et al., 1998). In particular, Hill et al. (2003) find that students want lecturers to be knowledgeable, well-organized, encouraging, helpful, sympathetic, and caring to students' individual needs. Sander et al. (2000) find that students Fig. 2. Hierarchical value map of teacher education students (interviews). 953R. Voss et al. / Journal of Business Research 60 (2007) 949–959
    • at the beginning of their university life want lecturers to have good teaching skills and to be approachable, knowledgeable, enthusi- astic, and organized. According to Lammers and Murphy (2002), students have a high regard for lecturers who are enthusiastic about their subject, inspiring, knowledgeable, and helpful. Similarly, Shevlin et al. (2000) mention “lecturer charisma” and Andreson (2000) points out that students want lecturers to be caring, enthusiastic, and interested in the students' progress. Brown's (2004) research indicates that competent lecturers know their subject, are willing to answer questions, are approachable, and have a sense of humor. In addition, they should be flexible enough to explain things in different ways, and to treat students as individuals. As the size of the circles in the HVM stands for the frequency respondents brought up a certain concept, expertise is the most important attribute of lecturers. This supports findings by authors such as Pozo-Munoz et al. (2000), Husbands (1998), Patrick and Smart (1998), and Ramsden (1991) who also point to the importance of lecturer expertise. For example, Pozo- Munoz et al.'s (2000) study indicates that competency is by far the most important characteristic of ideal teachers. Teachers should have knowledge of their subject and be able to communicate their expertise clearly to students. According to Greimel-Fuhrmann and Geyer (2003), good teachers should give explanations, answer questions, adapt their teaching methods, and be interested in and show concern for their students and their learning progress. Good teachers should also be humorous, friendly, patient, and fair graders. Similarly, students in this study want lecturers to answer their questions (problem solution), to choose the most suitable teaching method (teaching methods), and to be friendly (friendliness) and humorous (humor). In addition to displaying the most important attributes of lecturers, the hierarchical value map also shows why these attributes are important to the respondents. In this way, the HVM offers a deeper understanding of the attributes of lecturers that teacher education students desire by uncovering the constructs that underlie these desire expectations and graph- ically illustrating the underlying benefits that students look for. In this connection, respondents mentioned several conse- quences. Students' desire to learn something (learning) appears to be the most important consequence. As the width of the line in the HVM reveals, learning is strongly associated with performance and knowledge. Students believe that they need valuable learning experiences at university and in particular that they must acquire skills and methods (knowledge) which will help them prepare for their profession (professional qualifica- tion). The linkage between learning and knowledge supports findings in psychological literature which indicate that the learning process builds on existing knowledge leading to new knowledge (e.g., Schönpflug and Schönpflug, 1995). Students Fig. 3. Hierarchical value map of teacher education students (questionnaires). 954 R. Voss et al. / Journal of Business Research 60 (2007) 949–959
    • also want to have valuable teaching experiences to enable them to pass examinations (performance) necessary to obtain their degree and embark upon their careers. Students believe they will be able to pass such tests if they are motivated (motivation) and the lecturer's enthusiasm has a positive impact on their motivation. In addition, the lecturers' expertise, enthusiasm, and their teaching skills are associated with learning. The strong focus on learning and performance supports findings by Rolfe (2002) that suggest students may increasingly regard their university education as ‘instrumental’ as they enter higher education mainly for career reasons. The ability of lecturers to choose the most suitable teaching method from a variety of teaching tools (teaching methods) is important to students as lecturers can then offer interesting lessons (interesting lessons), which results in students being observant and paying attention to what their lecturers are saying (attentiveness). This, in return, helps students to learn (learning). The lecturer's communication skills also have a positive impact on students' attentiveness. Students also believe they can save time (save time), through a quick learning process (learning). Lecturers need to take time for their students during and after lessons (approachability). Approachable lecturers provide direction and advice (counseling) and solve students' problems (problem solution). According to the HVM, students particularly want to satisfy the following values: “well-being”, “security”, “satisfaction”, “universalism”, “self-esteem”, and “hedonism”. Students who believe that they are able to pass their tests, who feel prepared for their profession, and who receive advice, feel freed from doubt (security). Students feel good (well-being) if they can relax, save time, and receive advice from friendly lecturers. Students who acquire skills and methods are satisfied (satisfaction) and they feel they are in good hands (well-being) and better about themselves (self-esteem). Students who can save time due to a quick learning process are also able to enjoy life and have fun (hedonism). The HVM also reveals that students who are prepared for their profession feel safe and certain (security) and they want to positively influence society by educating young people by imparting knowledge and values (universalism). This strong association between the consequence “professional qualification” and the value “universalism” that respondents mention during the laddering interviews, however, could be a social desirability effect as teacher education students may try to give the impression of being particularly concerned about educating their pupils. This link appears in the interviews, but only from a few questionnaire respondents. A comparison of the two value maps reveals that the HVM based on the interviews is more complex than the HVM based on the questionnaires. Although the interview HVM comprises the same number of attributes and one consequence less than the questionnaire HVM, the interview value map reveals far more values than the map based on the laddering questionnaires (6 values in comparison to 2). Moreover, the interview HVM displays more associations between concepts than the HVM based on the questionnaires (28 associations in comparison to 23). During the laddering interviews, respondents mention three concepts that appear in the questionnaire HVM but not in the interview HVM, namely “interesting lessons”, “humor”, and “atmosphere”. These concepts, however, do not appear in the corresponding interview HVM owing to the chosen cutoff level. As stated, the HVM only displays associations that a certain number of respondents mentioned. Thus, only a few respon- dents mentioned these concepts during the interviews. Similar- ly, respondents wrote down the consequence “relaxation” that appears in the interview HVM but not in the questionnaire HVM but this concept is not graphically represented owing to the cutoff level. Table 2 shows that respondents elicit on average more attributes, consequences, and values during laddering inter- views than in the laddering questionnaires. In particular, respondents mention on average more than five times more values during the interviews than in the laddering question- naires. This also explains why the questionnaire HVM (2 values) only displays a small number of values in comparison to the number of values shown in the interview HVM (6 values). Respondents seem to have difficulties with climbing the ladder of abstraction and with eliciting associations on the highest value of abstraction without the presence of interviewers. In face-to-face interviews, interviewers can employ several laddering techniques (e.g., Reynolds and Gutman, 1988) to help respondents reach the value level which researchers cannot employ in the paper-and-pencil version of laddering. Respon- dents also mention more attributes during the personal interviews than in the questionnaires. This is explainable by the fact that the questionnaire design only allows respondents to write down four attributes while they are not limited during personal interviews. The design of the paper-and-pencil version of laddering also explains why respondents mention so many consequences (respondents mention on average 6.8 conse- quences per person in comparison to only 3.2 attributes with consequences accounting for 62% of all concepts of meaning). Respondents can give up to three reasons why a certain attribute Table 2 Comparison of attributes, consequences, and values Attributes Consequences Values Average number of attributes per person Percentage of attributes of all concepts of meaning Average number of consequences per person Percentage of consequences of all concepts of meaning Average number of values per person Percentage of values of all concepts of meaning Laddering interviews 4.3 21% 11.1 54% 5.1 25% Laddering questionnaires 3.2 29% 6.8 62% .96 9% 955R. Voss et al. / Journal of Business Research 60 (2007) 949–959
    • is important to them and the lack of elicited values may have been compensated for by the large number of consequences as respondents were not always able to completely climb the ladder of abstraction to the value level. Table 3 shows the total of 125 ladders collected from the laddering interviews with the 29 respondents providing between 2 and 7 ladders each, with an average of 4.3 ladders per respondent. The longest ladder consists of eight concepts of meaning (attributes, consequences, and values) and the shortest two, with an average of 4.8 concepts of meaning per ladder. By comparison, the laddering questionnaires give a total of 170 ladders and the 53 respondents provide between 1 and 4 ladders each, with an average of 3.2 ladders per respondent. The longest ladder consists of six concepts of meaning (attributes, consequences, and values) and the shortest two, with an average of 3.4 concepts of meaning per ladder. The 29 laddering interviews reveal more concepts of meaning than the 53 questionnaires. These results demonstrate that researchers can collect more ladders with more concepts of meaning during personal laddering interviews than with the paper-and-pencil version of laddering. The ladders collected from the interviews were also on average longer than the ladders from the questionnaires. 7. Limitations and directions for further research The research study has several limitations. The study is explorative in nature as this was the first to compare two versions of the laddering technique in the context of service quality in higher education. The aim of the study is to give a first valuable in-depth insight into what matters for teacher education students by revealing several important constructs. Further research studies, however, should improve knowledge of this topic. Due to the explorative nature of the study in general and the scope and size of the sample in particular, the results are tentative in nature. As the study involves two groups of university students from one university, one may not generalize the results to the student population as a whole. Qualitative researchers, however, can enhance generalizability by carrying out further studies using similar data collection and analysis methods at other research sites with a view to achieving “moderatum generalization”(Bryman, 2004, p. 285) and demonstrating that the findings are valid beyond and outside particular research contexts. Thus, fellow researchers should carry out further studies using similar data collection and analysis methods at other research sites. Researchers could then compare results from these studies and reveal differences. The measurement of service quality in higher education requires researchers to take the perspectives of other stake- holders (e.g., the government, employers, students' families) into consideration as well (Rowley, 1997). Thus, fellow researchers could examine the desire expectations of other stakeholder groups. Further research, for example, could investigate whether student desire expectations differ greatly from what lecturers believe students want. Mattila and Enz (2002) found a large gap between customer and employee perceptions regarding service quality expectations. Thus, fellow researchers could hand out questionnaires to both lecturers and their students. The researchers could then compare the resulting hierarchical value maps to highlight different views. Insights gained should help make lecturers aware of differing percep- tions and identify areas for appropriate training. In the context of service quality in higher education, first research results already indicate that a service expectation gap exists. Shank et al. (1995), for example, find that service delivery expecta- tions are lower among professors than among their students. Botschen et al. (1999) point to the fact that the paper-and- pencil version of laddering provides hardly any context information. As a consequence, the development of meaningful categories during content analysis is occasionally difficult to perform (Grunert and Grunert, 1995). In addition, Botschen et al. (1999 p. 55) admit that “little is known about the validity and reliability of the procedure and the comparability of results obtained from traditional laddering interview (soft laddering) and paper-and-pencil laddering”. Due to the lack of personal interviewing techniques (e.g., postulating the absence of an object or a state of being or evoking the situational context), paper-and-pencil laddering loses richness of data. The results of the research study indicate that only a few respondents reach the highest level of abstraction. However, in comparable paper-and-pencil laddering studies by authors such as Pieters et al. (1998), Botschen et al. (1999) and Botschen and Hemetsberger (1998), respondents only express a few values like “feeling good”, “harmony with yourself”, and “satisfaction”. Banister et al. (1994) point out that many people may have difficulties with verbalizing their experiences and with reflecting on their behaviors and attitudes. This may explain why only few respondents who filled in the laddering questionnaires men- tioned values. Without the guidance of interviewers, most respondents are not able climb the ladder of abstraction. 8. Conclusion This paper describes the application of the means–end chain approach and the laddering technique to investigate service Table 3 Comparison of number and length of ladders Number of ladders Number of ladders per respondent Number of concepts of meaning (A/C/V) Number of concepts of meaning per ladder(=length of ladder) Min Max Average Min Max Average Laddering interviews 125 2 7 4.3 597 2 8 4.8 Laddering questionnaires 170 1 4 3.2 582 2 6 3.4 956 R. Voss et al. / Journal of Business Research 60 (2007) 949–959
    • quality in higher education. Given the current lack of knowledge of student desire expectations, this is an explorative research study using the laddering technique to investigate how lecturers should behave and what qualities students look for. The laddering method revealed the constructs which drive the importance of the desired attributes of lecturers and preferred benefits. This explorative study gives a valuable first insight into the desired teaching qualities of lecturers and reveals the linkages between attributes, consequences and values. The results indicate that these teacher education students want lecturers to be knowledgeable, enthusiastic, approachable, and friendly. They should possess sufficient communication and teaching skills and be able to choose the most suitable teaching method from a variety of teaching tools. Respondents also mention several values that they regard as relevant and desirable: security, well-being, satisfaction, self-esteem, hedonism, and universalism. A comparison of two different laddering techniques reveals that although the results of the two methods are broadly similar, the personal laddering interviews produce more depth in understanding and significantly more respon- dents were able to reach the value level. The analysis also reveals why lecturers should possess the desired attributes: students predominately want to encounter valuable teaching experiences to be able to pass tests and to be prepared for their profession. Vocational aspects of their studies motivate students more than academic interest. Such knowledge of student expectations should help lecturers design their teaching programs. German lecturers in particular should pay more attention to vocational aspects in their teaching as they regularly receive criticism for offering courses that are too theory-laden (Voss, 2006). Thus, lecturers should include topics in the curriculum that help students prepare for their profession. Lecturers could also provide assignments that are directly relevant to work, and use interesting and thought-provoking examples and case studies from the “real world”. Lecturers could also stress links between theory and practice more and invite guest speakers who are willing to share valuable experiences with students. The introduction of tuition fees in Germany will probably strengthen this “consumerist” approach and German universi- ties will have to offer value for money while lecturers will have to emphasize the vocational relevance of their courses. Approaches for attracting new students such as a “student satisfaction guarantee” (Gremler and McCollough, 2002; McCollough and Gremler, 1999a,b) might be considered. Such a guarantee could make education appear more tangible and signal the quality of the educational experience to current and new students. McCollough and Gremler (1999a) find that satisfaction guarantees have a positive impact on student confidence in lecturers and they help set clear expectations that both students and lecturers will work hard. As a pedagogical device, satisfaction guarantees set performance standards and help increase the accountability of both students and lecturers. They also influence student evaluations of lecturers and courses positively without losing rigor in the classroom (Gremler and McCollough, 2002). 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