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Int. J. Human-Computer Studies 69 (2011) 496–508 www.elsevier.com/locate/ijhcs
Using behavioral patterns to assess the interaction of users and product Stefanie Harbicha,n, Marc Hassenzahlb a
E D SGA-EM R&D, SIEMENS AG, Gru¨ndlacher Str. 260, 90765 Fu¨rth, Germany User Experience and Ergonomics, Faculty of Design, Folkwang University of the Arts, Universitatsstraße 12, 45141 Essen, Germany ¨
b
Received 8 February 2010; received in revised form 23 February 2011; accepted 23 March 2011 Communicated by S. Wiedenbeck Available online 30 March 2011
Abstract We hypothesized that users show different behavioral patterns at work when using interactive products, namely execute, engage, evolve and expand. These patterns refer to task accomplishment, persistence, task modification and creation of new tasks, each contributing to the overall work goal. By developing a questionnaire measuring these behavioral patterns we were able to demonstrate that these patterns do occur at work. They are not influenced by the users alone, but primarily by the product, indicating that interactive products indeed are able to support users at work in a holistic way. Behavioral patterns thus are accounted for by the interaction of users and product. & 2011 Elsevier Ltd. All rights reserved. Keywords: Evaluation method; Behavior; HCI; Motivation; Interaction
1. Introduction A new concept emerges in the field of Human–Computer Interaction: the User Experience (UX) described and summarized in a number of approaches (e.g., Forlizzi and Battarbee, 2004; Hassenzahl, 2010; Hassenzahl and Tractinsky, 2006; McCarthy and Wright, 2004). While those approaches differ in detail, they all share a holistic notion of the interaction between user and product, thereby extending the task-oriented view beyond the mere instrumental. They emphasize the experiential with its situational, temporal and emotional aspects and the interplay of thinking, feeling and doing. Hassenzahl (2010), for example, bases his notion of experience on action theories, such as Activity Theory (Kaptelinin, 1995; Kuutti, 1995). It highlights the objectorientedness of activities as well as their mediation by tools, shaped and developed by usage, which in turn shape people’s activities. Activity Theory and many other action theories postulate a hierarchical structure of activity, which can well n
Corresponding author. Tel.: þ 49 911 654 2417; fax: þ49 911 654 3003. E-mail addresses: s.harbich@gmx.de, stefanie.harbich@siemens.com (S. Harbich), marc.hassenzahl@folkwang-uni.de (M. Hassenzahl). 1071-5819/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhcs.2011.03.003
be applied to the work place. At work, several levels of goals have to be fulfilled. On the lowest level are operations on a sensomotoric level such as clicking on a button (i.e., operations). They are a part of the higher level self-contained goals (i.e., actions), such as completing a spreadsheet analysis. Activities then consist of several of such goals and are motivated, for example, by the aspiration to be a good clerk (see also Carver and Scheier, 1998). To fulfill high level goals, plans will be defined (Miller et al., 1970)—either to some extent beforehand or as action unfolds. There may even be several plans to choose from, each leading to the goal, but at different costs and at different results (e.g., Stock and Cervone, 1990). While action per se was always in the focus of Human–Computer Interaction, the inclusion of high-level goals and aspirations, motives and psychological needs, and related emotions and experiences is new. It is nevertheless a necessary step, especially important for the evaluation of interactive products. The object of investigation must be extended beyond product qualities to the users and their individual perception of and experience with the product. According to Carver and Scheier (1998), experience includes the users’ behavior and is, thus, accessible through the users’ behavior. In Information Science there has likewise been a shift from a system-centered approach to a person-centered
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approach (Wilson, 2000). Ellis’ (1989) behavioral model of information seeking strategies, for example, is concerned with behaviors like browsing, i.e., semi-directed searching, or extracting, i.e., selectively identifying relevant material. In our opinion, it is a promising approach to evaluate the interaction of user and product by taking into account the behavioral patterns users reveal when working with a product and the thoughts and opinions around these patterns. In previous work, we (Harbich and Hassenzahl, 2008) have focused on the work context, a context surprisingly neglected by User Experience research as a recent review concluded (Bargas-Avila and Hornbæck, 2011). We identified four sets of behavioral patterns related to the use of interactive products at work: execute, engage, evolve and expand. We start with characterizing those sets in more detail. After formulating our research questions, we summarize our initial work aimed at developing a questionnaire. We will then describe an analysis of the behavioral patterns in our main study, present its results and suggest the implications for our questions. 2. Execute, engage, evolve, expand In the domain of usability, it is widely accepted that interactive products can support users to change their behavioral patterns and feelings to the positive when executing tasks. In our opinion, other aspects of the work context are capable of such assistance, too. As described above, people have goals on different hierarchical levels and plan their steps to achieve these goals. To fulfill the higher-level goals, ‘‘be-goals’’ in terms of Carver and Scheier (1998), people even have to generate lower-level goals themselves, as well as the plans to achieve them. For being a popular and well-accepted employee (be-goal) in marketing, for example, I might have to succeed in implementing the advertising campaign for a new film. Related lower-level goals (do-goals) might be to create film posters, to devise the storyboard for the TV commercial and to design toys for merchandising. The creation of the film posters again can be broken down into several steps, one of them being the assembly of photos, title, textual information, etc. Inserting a photo into the poster, scaling and placing it are even lower-level goals that again can be broken down into the single operations like selecting a photo by clicking on it, etc. Miller et al. (1970) do not speak of goals in this context but refer to plans that are hierarchically organized. These plans are the more detailed the closer they are to the present situation (see also Cropanzano et al., 1995; Oesterreich, 1981). From a traditional usability perspective, an interactive product has to allow its users to pursue existing plans, that is, completing their tasks (i.e., lower-level goal like inserting a photo) effectively, efficiently and satisfyingly (International Organization for Standardization, 1998, 2006). Together with the adequate functionality, such interactive products enable users to implement the behavior necessary for pursuing their plans and attaining the low-level goals.
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We call this behavior ‘execute’. It is the first of a set of four behavioral patterns we assume useful for achieving the high-level goal of doing a good job with the aid of interactive products. Our second behavioral pattern is called ‘evolve’. To achieve a goal regardless of its hierarchical level, people have to formulate a plan consisting of the single steps. When doing routine tasks, they can make use of existing plans, but when they try to achieve a new goal, people have to make a new plan. To work out the details of the plan, people need to know as many alternatives and constrains as possible to attain the goal effectively and efficiently. For interactive products, this means knowing about most functions of the product and being able to use them. Only when users are aware of their possibilities, they are able to tap the full potential of their product and are therefore able to efficiently figure out the best plan. By using newly discovered functions, users may even reformulate their goals (Carroll et al., 1991) and evolve not only the plans to reach their lower-level goals but their higher-level goals as well and thus their work in general. A higher-quality goal may come into reach, enabling users to enhance the quality of their work without enhancing their efforts—simply by choosing the best plan. For instance, when inserting a photo into the film poster, the plan might be to open the photo, scale and position it (which can be broken down even further, of course). Those are several steps containing various possible sources of errors. The interactive product might provide a function for defining different areas and for automatically scaling the photo after having assigned it to the area. First, the photo then has the right size and resolution, and, second, when trying another photo for the poster, it simply has to be substituted without the subsequent steps. This alteration of the lowerlevel goals of opening, scaling and positioning can result in a more efficiently reached goal and even in a better accomplished goal because of the easy possibility to try different photos—if the users know their interactive product well. So in our opinion, interactive products are able to support their users by not only allowing them to pursue their goal as they had planned (execute), but also to devise that plan, modify their goals and improve in efficiency and quality (evolve). As described, we expect users to modify their existing goals because of functionality provided by their interactive product. However, goals such as creating a film poster are not always given at work. Employees may only have given the higher-level goal of implementing the advertising campaign for a new film but are left to figure out how to realize this on their own. Of course, they do not have to always invent new ways to do this but can rely on established methods. Nevertheless, sometimes it may be necessary to come up with a new, innovative lower-level goal to accomplish the higher-level goal (Hacker, 1986). If employees succeed outstandingly with this new method, they complement the be-goal of being a ‘‘good’’ clerk. Interactive products can help them by being flexible enough to allow implementing the new methods. Instead of the traditional methods of advertising, the employee might, for
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example, discover the functionality of embedding graphics as an overlay into the satellite view of Google Earth to make it available via the Internet, as it was done for the film Pirates of the Caribbean: Dead Man’s Chest (The Walt Disney Internet Group, 2009). A realistic looking island with the shape of a skull was placed into the sea with rotating gold coins on it opening different Internet pages with further information about the film or a lottery. Using this feature of Google Earth for advertising purposes has been both an innovative new goal for the employee and an innovative new form of usage for the interactive product. The scope of both the user’s work and the interactive product has been expanded. For Google Earth, the use of overlays is not extraordinary, but using them for advertising purposes is an innovative application that even the software developers might not have intended. The concept of innovativeness plays a major role in Diffusion of Innovations Theory (Rogers, 1995). Innovators are the first to buy a new product on the market and allow others to adapt to their behavior. Hirschmann (1980) extended this theory and introduced use-innovativeness. It describes the deployment of a previously adopted product to solve a new problem. This is similar to our third behavioral pattern called ‘expand’. It describes the invention of new higherlevel goals with the help of interactive products by offering enough flexibility to implement the new goal or by even inspiring the invention of a new goal. With execute, evolve and expand, we cover most aspects from plans and lowlevel goals to higher-level goals, namely pursuing low-level plans without impairment (execute), formulating plans and eventually modifying goals (evolve) and even inventing new goals (expand) by the use of interactive products. However, we have not covered one important aspect yet. To pursue their plans and to reach and evolve and expand low- and high-level goals, users have to be motivated. When they are motivated, they do not avoid the execution of their tasks or finish with the least possible effort (and corresponding results, see Norman, 2004). In the above example, the employee might, for instance, settle with the first photo inserted into the film poster without trying other alternatives. On the contrary, with more motivation he might instead have tried other photos as well and would have created a much nicer film poster. Intrinsic motivation enhances well-being in general, job satisfaction and performance (Baard et al., 2004; Gagne´ and Deci, 2005). It can be evoked by the fulfillment of basic needs (Ryan and Deci, 2000), i.e., need for competence, need for autonomy and need for relatedness, forming a holistic approach similar to Hassenzahl’s (2003) ‘‘hedonic aspects’’ in the context of interactive products. Isen et al. (1991) induced positive affect in medical students and let them decide which patient out of six might have cancer on the basis of a set of data. The positive-affect group performed as well as the neutral control group but reached their decision earlier and, thus, achieved their goal more efficiently. Additionally, they went beyond their assigned task and mentioned diagnoses for other patients and even considered treatments in some cases. In other words, they made use of
the extra time they got because of their fast decision process and were able to evolve their initial goal into a higher-level goal. In the context of interactive products, intrinsic motivation can mean examining interactive products to discover their functionality and to use them in a playful way to be able to expand their scope. Google Earth, for example, is a very intriguing product, inviting for exploration of the earth’s surface and for exploration of the product itself, having led to its expansion of scope as described. Studies show that perceived enjoyment while working with the product can lead to more usage, even in spite of usability problems (Davis et al., 1992; Igbaria et al., 1994). Hence, we suggest that, when users are motivated at work, they show our three hypothesized behavioral patterns execute, evolve and expand more easily and pursue plans, devise plans to reach or even exceed goals and invent new lower-level goals to complement higher-level goals more easily. Being motivated thus forms our fourth behavioral pattern, called ‘engage’. With it, our model of four patterns execute, engage, evolve and expand called e4 is complete. With this study, we want to explore the model and examine whether the hypothesized behavioral patterns do occur at all in connection with interactive products. Our approach differs from most approaches in HCI in that we do not ask the users about their perceptions of the product. Instead, we focus on their self-reported behavior. In our opinion, interactive products can help users to show the described behavioral patterns. Yet, one might argue that it is either the users themselves who account for their behavior by their individual user characteristics or that it is the product through its product attributes that elicits and shapes the behavioral patterns of its users. If it was solely the users that accounted for their behavior, this would mean that interactive products could add nothing to facilitate work, which would contradict every theory of Human–Computer Interaction (e.g., Fogg and Nass, 1997; International Organization for Standardization, 1998; Nielsen, 1993; Shackel, 1991). Thus, our second question is what causes the behavioral patterns. In an initial study, we developed a short questionnaire to be able to address two questions of our main study: Do the supposed behavioral patterns occur at work? Are they depending on the users, the products or both? 3. Initial work When evaluating the interaction between users and interactive products, a wide range of different behaviors plays a role. Some of them can be observed frequently from the very beginning, but some behavioral patterns will only rarely occur and some are almost impossible to be observed directly. Especially when investigating a large population of users and products and rare behaviors, we have to rely on the users’ self-reports (Howard, 1994). An appropriate approach might have been to use momentary assessment techniques similar to the Experience Sampling Method (Csikszentmihalyi and Larson, 1987) or the Day
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Reconstruction Method (Kahneman et al., 2004), which better take the characteristics of the human cognition and ability to correctly recall the execution of a behavior into account by avoiding or minimizing retrospection. Yet we wanted to construct a method that can be easily used by researchers without much effort and that helps us to examine several hundred users. For these purposes a questionnaire seemed more appropriate. Thus, in the first pilot study, we developed a short questionnaire to obtain users’ behavioral patterns and to understand the relation of these behavioral patterns to user characteristics and product attributes, which were then examined in the main study. To address not only Human–Computer Interaction experts and to facilitate rating of the items, the items were formulated from a first-person view describing behaviors, feelings and situations. The items do not directly refer to the used interactive products but to the users’ perceptions of themselves or of their use of the product. The first pilot study was implemented to provide a short, well-evaluated collection of items, which cover the four described sets of behavioral patterns. These sets thus include one set concerned with task accomplishing behaviors (execute), one set regarding motivational and persistency aspects (engage), one set concentrating on the creation of plans and alteration of goals (evolve) and one set addressing the creation of goals (expand). We assume these behavioral patterns to occur in everyday work with computers and to be able to support in accomplishing routine tasks as well as in accomplishing new and challenging goals and thus to enhance general job performance. 3.1. Initial item formulation Two 3 h workshops with a total of 13 experts (seven usability engineers, six psychologists) were held to gather relevant behavioral items for the survey. Both workshops began with a brief discussion of supporting particular types of behavior when working with interactive products. Then each workshop participant was asked to individually create potential Likert items (seven point, ranging from strongly disagree to strongly agree; all items and labels were in German) which capture relevant behavior. Subsequently, all items were discussed and reviewed by the whole group. Both workshops resulted in a pool of 246 items. The first author reviewed these items and excluded redundant and inappropriate items (for example, representing questions about the interface instead of questions about the behavior of the users, e.g., ‘Are there ways to copy and paste into other applications?’, ‘Do you miss any functionality?’, ‘Is the interface color changeable?’). The remaining 47 items were finally reviewed by an independent communication expert to check and fine-tune intelligibility and grammar. 3.2. Item exploration: first pilot study We administered the 47 items from the workshops to a first pilot sample of 366 participants via an online survey
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(created with www.surveymonkey.com). Participants were recruited by flyers and emails with a link to the survey’s website. A raffle for three book vouchers (value 20 h each) was given as incentive. Surveymonkey allows randomizing the order of items for each participant individually to minimize potential order effects. As the users’ behavioral patterns or the users’ perceptions of an interactive system might be influenced by the degree of experience the users have with the product (Venkatesh and Davis, 2000), attention was paid to not ask complete novice users of a product but users at least to some extent familiar with a product. Accordingly, a period of 1 month of product use was set as a precondition for the study. A minimum of expertise with a product is an important prerequisite for eliciting behavior, because participants have to rely on an existing sample of own behavioral patterns to be able to respond to the items. Participants chose an interactive product used by them in a work context for at least a month and with a minimum of 3 h per week. The free choice of the product ensured a variety of interactive products, which allows for a broader generalization of respective findings and of reliability and validity issues (see Monk, 2004). After choosing the product, the participants were asked to indicate the agreement or disagreement (seven-point Likert scale, with strongly agree and strongly disagree as verbal anchors) with the 47 items that captured different facets of behavior (for example, see Fig. 1). In addition, participants were asked to rate their expertise with computers in general and their chosen product in particular (i.e., months of usage, hours of usage per week) and to provide demographic information (i.e., age, gender, profession). Of the 366 participating persons, 255 completed the survey (70% retention rate, a response rate cannot be computed due to the fact that the total number of invited people is unknown). Twenty-one responses were excluded because either participants used the rated interactive product for less than 3 h a week or the questionnaire was not filled in correctly (e.g., did not specify the rated product, had more than 5% missing answers, etc.). This left 234 responses for further analysis. The majority of participants were male (74%), with a median age of 32 years (min ¼ 19, max ¼ 62 years). The participants rated 115 different products or product versions. We began the exploratory item analysis by identifying and excluding ‘‘skewed’’ items, i.e., items with a mean that tends to either end of the scale. We set a skewness criterion of 91.09 for each item. Seven items exceeded this criterion and were excluded from further analysis. The remaining 40 items were analyzed for substantive coherence by conducting a Principal Component Analysis (PCA) with Varimax rotation. A Scree Test recommended the extraction and rotation of five factors. Subsequently, all items loading with their highest factor loading on other than those five factors were excluded. Another Principal Component Analysis with Varimax rotation was conducted with the remaining items. This time, we set the extraction criterion to five
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execute This product sometimes responds differently than expected. Sometimes I spend a long time searching for functions I need for my work. When working on tasks with this product I often need more time than intended. Sometimes I am surprised about the product's reactions to my entries. The work with this product is sometimes cumbersome. I tend to forget the time now and then when working with this product. This product allows approaching my tasks creatively. In my spare time I'm playfully exploring this product. Even if my actual task already is satisfactorily completed, I sometimes try to improve it with the aid of this product. I can enhance my work's quality without additional effort by using this product. This product helps me to complete my tasks better than expected without additional effort. With this product, I can sometimes even exceed my aim without additional effort. I believe this product has many functions I may need eventually. I occasionally have "misused" this product for purposes beyond its usual range. Now and then I'm completing tasks with this product, it isn't really intended for. Occasionally I use this product in an odd manner to complete my task. I'm sometimes using this product for tasks probably not typical for this product. I believe I sometimes use this product differently compared to other users.
engage
evolve
expand
.804 .800 .757 .746 .698 .801 .728 .677
.515 .811 .735 .723 .414
.602 .850 .804 .801 .773 .618
Eigenvalue
3.37
2.53
2.40
3.39
% explained variance
18.7
14.1
13.4
18.8
Note. Principal Component Analysis with Varimax rotation. Loadings < .400 are not shown. N = 90. Fig. 1. Item structure.
factors. Four of the five components clearly matched the four behavioral patterns (i.e., execute, engage, evolve and expand), with a range of 5–12 items loading on each of the four factors. The fifth represented the functional range of the product (e.g., ‘I think I use only a small amount of the product’s functionality’) and was not related to a behavioral pattern at all. We excluded those three items (i.e., the fifth component) and ran a third PCA with Varimax rotation and an extraction criterion of four components. The structure of those four components remained stable. We then examined the validity of the four resulting scales. The results turned out to be not satisfying for the application of the scales as questionnaire. An analysis of
Cronbach’s a of the items of each factor revealed that the factors related to the behavioral pattern engage had an internal consistency lower than .70. In addition, some were not coherent in respect of content. For example, for the scale expand, those items had to be excluded that assured the diversity of this scale and the coverage of the whole pattern. This left only rather similar items (e.g., ‘Sometimes I use this product in an unusual manner to achieve my goal’ and ‘Sometimes I try to outsmart this product to achieve my goal’). Thus, in cooperation with the communication expert, the whole set of the remaining 37 items were further revised, refined or removed and were complemented by additional items, resulting in a set of 44 items capturing
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Further analysis demonstrated the acceptable statistical characteristics of the item set (see Fig. 2). The scale means range around the midpoint of the theoretical scale (4). The standard deviations and the skewness indicate a normal distribution. All scales have good internal consistency (see Cronbach’s a), so their items capture the same behavioral pattern per scale. The interscale correlations show some correlation between scales. Especially engage seems to correlate with the other scales with values ranging from r¼ .22 (engage) to r ¼ .50 (expand), suggesting an interdependency of motivation and the extent of being supported in goal attainment, in improving quality and in future goals. Whether motivation leads to working more focused and creatively, or whether efficient work support is motivating, needs to be examined. The rather small correlations of execute underline the difference of these behavioral patterns related to goal attainment and the patterns related to goal creation. However, the interscale correlations never exceed the internal consistency and the PCA with the orthogonal Varimax rotation supports an acceptable discriminant validity of the four scales. The initial work resulted in an 18 items questionnaire, which covers the targeted four aspects of user behavior at work when using interactive products (i.e., execute, engage, evolve, expand). It has a satisfactory reliability, discriminant validity and acceptable distributional properties. In the following main study, the questionnaire was used to explore the occurrence of the different behavioral categories and their relation to product attributes and user characteristics.
different facets of behavior (see Fig. 1 for examples). These items were then given to the second pilot sample and tested in a second online survey. They were again arranged in a questionnaire with randomized order and were answered on a seven-point Likert scale with the poles strongly disagree and strongly agree. 3.3. Final item selection: second pilot study One hundred and thirty individuals participated in the online survey for the second pilot study. Of those, 96 completed the survey (retention rate: 74%). Six responses were excluded due to an intensity of using the rated interactive product that was less than our set minimum of 3 h a week. This left 90 participants (28 women, 31%) for further analysis. The sample’s age ranged from 18 to 56 years, with a median of 33 years. Participants were recruited by flyers and emails, exactly as in the first pilot study. Again, participants were asked to choose an interactive product they were using in a work context for at least 1 month and a minimum of 3 h per week. They were presented with the 44 items in randomized order and were asked to rate their expertise with computers in general and to provide demographic information. An item analysis revealed three items with a skewness that exceeded the value of 91.09, so these items were excluded from further analysis. To test the structure of the remaining 41 items, a PCA with Varimax rotation was performed. We set the extraction criterion to four components (i.e., confirmatory approach) and obtained the expected structure. The best fitting 18 items out of the 41 were chosen (criterions were highest loading on the component and fit of content to the component) and another PCA with Varimax rotation was performed. The four expected components again emerged and the validity of the scales was good, as we show in detail below. Therefore, the four components were set as the four scales capturing the behavioral patterns execute, engage, evolve and expand. They explained each 13–19% variance and 65% in total. Fig. 1 shows the loadings of each item, Eigenvalues and the explained variance of the four components. Only the item ‘‘I believe this product has many functions I may need eventually’’ shows a secondary loading larger than .40 (16% explained variance).
scale execute
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4. Analysis of the behavioral patterns In the main study, we addressed two related issues. First, we were interested in the mere occurrence of the behavioral patterns, their stability as well as their temporal development. Second, we examined the relation of the characteristic of the users and/or interactive products to the occurrence of behavioral patterns. Of course, the differences in users (e.g., expertise) represent a source of variation, but also the different products may vary in the extent they evoke the different sets of behavioral patterns. Typically, in the field of Human–Computer Interaction users are considered the ‘‘subjects’’ and sampled accordingly. This, however, downplays the interactive product as
Cronbach’s α .84
interscale correlation execute engage evolve
mean (SD)
skewness
engage
4.36 (1.49) 3.91 (1.51)
-.31 .16
.74
.22*
evolve
4.58 (1.38)
.64
.78
.35**
.50**
expand
3.45 (1.51)
.31
.86
.00
.42**
Note. N = 90. *p < .05. **p < .01. Fig. 2. Statistical characteristics of the questionnaire.
.25*
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a source of variation, which is especially relevant if the issue is the interaction between user and product. To avoid falling for this ‘‘the product as a fixed-effect fallacy’’ (Monk, 2004), one must of course take into account the interactive product as well, that is, one must sample products in the same way as one samples participants to provide substantial heterogeneity. One objective of this study was to determine whether there are structural differences when using the variance stemming from people versus the variance stemming from interactive products. Therefore, a second PCA was conducted with the products as objects of research and a third analysis that deliberately disregarded the variance produced by the rated products and that therefore observed solely the participants. Concepts of interactive product use tend to focus on the product itself, but they also strongly take the user into account. Usability, for example, depends by definition of the widely accepted norm ISO 9241-11 (International Organization for Standardization, 1998) on specified users. Thus, apart from potential structural differences, we examined the relation between user characteristics, such as age, expertise or skills, and the behavioral patterns, as well as the relation between perceived product attributes and the behavioral patterns. All data collected and analyzed is self-report data. It should be taken into account that the ratings might be biased. Especially when certain attributes are difficult to assess, for example, when users do not have enough experience with a product, other more easily accessible or more apparent criteria are consulted. Venkatesh and Davis (2000) found that perceived usefulness was influenced by subjective norms in the first month of system usage, before actual first-hand experience took over. Similarly, beauty has been found to influence judgments about interactive products because beauty is more directly accessible than, for example, usability (Hassenzahl and Monk, 2010). The overall evaluation of a product plays an important role, as it is often used to infer judgments about less accessible aspects. In the present study, we accordingly controlled for these effects. 4.1. Method 4.1.1. Participants Three hundred and sixty-three individuals participated in the main study. They were recruited by flyers and emails with a link to the survey’s website and were announced a reward in the form of a raffle for three book vouchers. Two hundred and seventy-eight completed the survey (77% retention rate) and were working with their interactive products for at least 1 month and more than 3 h per week. The remaining participants, 65 female (24%), were 16–63 years old, with a median of 37 years. They had 16 years and 6 months of experience with computers on average (SD ¼ 6.7, min ¼ 1, max ¼ 37) and used them 36 h per week (SD ¼ 12, min ¼ 4, max ¼ 70). Seventy-six different interactive products were evaluated and people worked with them on average for 33 months (SD¼ 32, min ¼ 1,
max ¼ 216) and 16 h per week (SD¼ 11, min ¼ 3, max ¼ 80). Participants estimated their skills in using their chosen products as 3.9 on average on a scale of 1 (not at all) to 5 (very good) (SD¼ .8, min ¼ 1, max 5). To examine the stability of the behavioral patterns over time, we asked the participants to take part in the study 6 weeks later. Forty-six participants responded to this call and filled out the survey again after 7–10 weeks. Fortythree of them remembered their previously evaluated product correctly, were using their products for at least 3 h per week and fully completed the survey. Their age ranged from 23 to 59 years (median ¼ 37). Participants were using their products for 35 months on average (SD ¼ 42, min ¼ 1, max ¼ 200) and 17 h per week (SD ¼ 11, min ¼ 4, max ¼ 45). They rated their skill in using their chosen products as 4.0 on average on a scale ranging from 1 (not at all) to 5 (very good) (SD¼ .6, min ¼ 2, max ¼ 5). 4.1.2. Procedure We conducted the study as an online survey consisting of the 18 earlier identified items. Additionally, participants were asked to provide some information about themselves and their computer experience, as well as to answer the AttrakDiff2 questionnaire (Hassenzahl et al., 2003). This allowed for addressing the relationship between product attributes, user characteristics and users’ behavioral patterns. Participants could choose any interactive product used at work regularly for at least 3 h per week and at least for 1 month. At the end of the survey, participants were asked to leave an identifier and their email address for the second part of the survey. Six to ten weeks later they received an email inviting them to fill out the survey for the same product a second time. Hassenzahl (2003) argued that the perceived qualities of an interactive product can be divided into instrumental, pragmatic and non-instrumental, self-referential, hedonic aspects. Pragmatic quality refers to a judgment of a product’s potential to support particular ‘‘do-goals’’ (e.g., to make a telephone call) and is akin to a broad understanding of usability as ‘‘quality in use’’. Hedonic quality is a judgment with regard to a product’s potential to support pleasure in use and ownership, that is, the fulfillment of so-called ‘‘be-goals’’ (e.g., to be admired, to be stimulated). The AttrakDiff2 is a questionnaire to measure those perceived qualities and to generally evaluate an interactive product. It consists of a semantic differential with 28 bipolar items, constituting four scales with seven items each: perceived pragmatic quality (PQ), perceived hedonic quality-stimulation (HQS), perceived hedonic quality-identity (HQI) and appeal (AP). 4.2. Results and discussion 4.2.1. Occurrence of the behavioral patterns Our first aim was to investigate whether the assumed behavioral patterns occur at all at work and whether users feel that the behavioral patterns are associated with the product they are using. In the present sample, 278
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participants rated 76 different products or versions of products. The means and standard deviations for the 18 items are shown in Fig. 3. As some of the items describe behavioral patterns that are counterproductive at work, we adjusted the poles for the analysis. High values thus imply that those behavioral patterns were elicited, which seem desirable at work. The means ranged from 2.92 (SD ¼ 1.98, min ¼ 1, max ¼ 7) to 5.38 (SD ¼ 1.49, min ¼ 1, max ¼ 7). Minimum and maximum of each item was 1 and 7, respectively, the overall mean was 3.94 (SD¼ .95). This shows that behavioral patterns and opinions of the four described behavioral patterns associated with a specific interactive product did in fact occur. So users experienced those behavioral patterns and felt that their products were or were not able to support them. 4.2.2. Structure We examined the potential structural differences between the group of products and the group of participants by two separate Principal Components Analyses (PCA) with Varimax rotation on the 18 behavioral items. For the product analysis, we first averaged each item across those participants that rated the same product. This resulted in mean item ratings for 76 different products (see Fig. 3; prod.) and eliminated variance stemming from participants. If the behavioral patterns depend solely on users’ personal characteristics and were not influenced by the products they use, a PCA of the mean product ratings should not reveal any meaningful structure. Quite the opposite, in line with our expectations, a similar pattern as the one in the initial study emerged (see Fig. 1). Only four out of the 18 items had their highest loadings on other scales than identified in the initial study, hence illustrating a not as well-defined structure (‘‘I tend to forget the time now and then when working with this product’’, ’’I believe I sometimes use this product differently compared to other users’’ and ‘‘This product lets me approach my tasks creatively’’). Overall, the initial study and the product analysis revealed a similar, four component structure. The product analysis explained 13–22% variance for each component and 71% variance in total. For the participant analysis, we subtracted the mean rating for the corresponding evaluated product from each individual rating. This eliminates variance stemming from the different products (i.e., centering). A second PCA on this (see Fig. 3) revealed again a structure close to the structure in the initial study, except for the item ‘‘This product lets me approach my tasks creatively’’, which switched from the factor execute to evolve. The factors explained 11–18% variance and 59% in total, indicating a slightly decreased fit compared to the initial data, but nonetheless a similar structure. So the allocation of behavioral items to the four aspects of work is accounting for both users and interactive products and, thus, describes their interaction. Unfortunately, this analysis could not be done reversely for the products’ sample, as no participant rated several products.
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4.2.3. Stability and development of the behavioral patterns Roughly 8 weeks later (M ¼ 56 days; SD ¼ 12; min ¼ 39, max ¼ 79), users reported similar rates of behavior, confirming the stability of the behavioral patterns (see Fig. 4). Forty-three participants of the main sample rated the items again for the same product. The rated behavioral patterns were stable, as the significant correlation of the clustered behavioral patterns of the first and the second measurement indicated, ranging from r¼ .73 (expand) to .86 (evolve and execute). Thus, participants showing the behavioral patterns at the first measurement did so 8 weeks later, too. Comparing the means of the four patterns for t1 and t2 showed that execute and evolve did not differ significantly, whereas engage and expand revealed a significant increase from the first to the second measurement. This suggests that at the first point of measurement the patterns execute and evolve may have been already fully developed. Usage of the product beyond this point did not further increase the occurrence of these behavioral patterns. In contrast, engage and expand may require more time. However, for a definitive answer to the question of whether the observed differences represent systematic change over time a longitudinal study is needed, which allows for controlling the specific product expertise. 4.2.4. User characteristics and product attributes In addition to the behavioral patterns, we obtained product perceptions with the AttrakDiff2 questionnaire and a number of user characteristics, such as age, general computer expertise in years, intensity of general computer usage in hours per week, specific product expertise in months, intensity of specific product usage in hours per week and self-rated product expertise. Fig. 5 shows the mean values for each measure, its standard deviation and their inter-correlation. Given the reported measure was a scale, Cronbach’s a is reported in the diagonal. The analysis of the product attributes and user characteristics showed coherent results and interrelation of the measures. To determine the relation of user characteristics, product attributes and behavioral patterns, we conducted four separate stepwise multiple regression analyses with each of the four behavioral patterns as criterion, six user characteristics (age, computer expertise [years], computer usage [hours/ week], product expertise [months], product usage [hours/ week], self-rated product expertise) and three product attributes (PQ, HQS, HQI) as predictors (see Fig. 6). A stepwise regression determines a set of meaningful predictors by a sequence of F-tests. It selects the variables with the largest probability to explain the criterion variable and provides information about the contribution of each remaining predictor to explain the criterion’s variance. We chose this analytical approach because of the relatively substantial intercorrelation of predictors. For each behavioral pattern, at least one model emerged. As expected, execute is predicted by pragmatic
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execute
engage
evolve
M (SD)
prod.
part.
This product sometimes responds differently than expected.
278
3.95 (1.84)
.839
.825
Sometimes I spend a long time searching for functions I need for my work.
278
3.84 (1.77)
.799
.752
When working on tasks with this product I often need more time than intended.
278
4.77 (1.82)
.814
.623
Sometimes I am surprised about the product's reactions to my entries.
278
4.11 (1.91)
.812
.758
The work with this product is sometimes cumbersome.
276
3.87 (1.84)
.763
.768
I tend to forget the time now and then when working with this product.
273
4.07 (1.99)
This product allows approaching my tasks creatively.
276
4.28 (1.87)
In my spare time I'm playfully exploring this product. Even if my actual task already is satisfactorily completed, I sometimes try to improve it with the aid of this product.
277
2.92 (1.98)
.763
.709
277
4.08 (1.91)
.699
.776
I can enhance my work's quality without additional effort by using this product.
272
5.00 (1.60)
.494
This product helps me to complete my tasks better than expected without additional effort.
276
3.97 (1.57)
.472
prod.
part.
prod.
.477
.455
.555
.426
.497
expand part.
prod.
part.
.858
.602
.707
.657
.709
.612
With this product, I can sometimes even exceed my aim without additional effort.
275
3.73 (1.75)
.686
.604
I believe this product has many functions I may need eventually.
278
5.38 (1.49)
.721
.689
I occasionally have "misused" this product for purposes beyond its usual range.
278
3.22 (1.99)
.868
Now and then I'm completing tasks with this product, it isn't really intended for.
276
3.12 (1.88)
.884
.854
Occasionally I use this product in an odd manner to complete my task.
274
3.59 (1.95)
.684
.720
I'm sometimes using this product for tasks probably not typical for this product.
277
3.41 (2.08)
.821
.828
I believe I sometimes use this product differently compared to other users.
277
3.56 (1.86)
.554
.519
.486
.581
Eigenvalue
2.35
3.16
4.00
1.90
3.10
2.39
3.27
3.19
% explained variance
13.0
17.5
22.2
10.5
17.2
13.3
18.2
17.7
Fig. 3. Structure of the behavioral items in regard to both the products (prod.) and the participants (part.) Note. prod.: sample of products (averaged across participants), n¼76; part.; sample of participants (centered on products); n¼278.
S. Harbich, M. Hassenzahl / Int. J. Human-Computer Studies 69 (2011) 496–508
N
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Cronbach’s α n = 278
Mt1 n = 43
.84
4.04 (1.45)
4.04 (1.31)
0.04
.86
engage
.70
4.04 (1.40)
4.32 (1.18)
2.31*
.82
evolve
.76
4.84 (1.13)
4.87 (1.13)
0.40
.86
.86
3.72 (1.42)
4.19 (1.28)
3.10**
.73
Scale execute
expand
Mt2 n = 43
T n = 43
505
rre-test n = 43
Note. *p < .05. **p < .01. Fig. 4. Stability of the behavioral patterns.
M (SD)
1
2
3
4
5
6
7
8
9
Product 1 2 3
pragmatic hedonic stimulation hedonic identification
4.3 (1.1) 4.3 (0.9) 4.8 (0.9)
(.87)a .48** (.82)a .72**
.61**
(.83)a
Person 4
age
5
computer expertise (years) computer usage (hours/week) product expertise (months) product usage (hours/ week) self-rated product expertise (1 - 5)
6 7
8 9
37.5 (9.9) 16.4 (6.7) 36.5 (11.9) 32.9 (32.2)
-.02
-.02
-.09
.02
.06
-.02
.10
.14*
.16*
-.13*
.05
.04
.07
.12*
15.7 (11.4) 3.92 (0.76)
.06
.03
.08
-.04
-.08
.35** .18**
.37**
.15*
.33** -.03
.09
.12
.71** -.02 .14*
.11
.22**
.20**
Note. *p < .05. **p < .01. aCronbach's α Fig. 5. Intercorrelations of product attributes and user characteristics.
quality (PQ) only. Execute is about accomplishing given tasks efficiently, and PQ subsumes those attributes, which support users with this (i.e., classic view of usability). In addition, the absence of any other variable in the model for execute emphasizes the construct validity of our measures, thereby lending credibility to the results for the other sets of behavioral pattern. For engage, four predictors were identified, by far the strongest of them being hedonic quality—stimulation (HQS). Sheldon et al. (2001) as well as Reiss (2004) assume stimulation and curiosity to be a basic human need. Actually, HQS captures the product’s ability to address those needs, through novelty and creativity. The link to engage underlines the importance of need fulfillment per se and stimulation in product interaction to foster behaviors beyond the mere task execution. The users’ skill also adds to engage. Being good at using their products motivates users and helps them to keep up with working with their interactive products and engaging in their work. Another
predictor for engage was expertise with the product, although the increase in additional explained variation (R2) is rather small. The more hours participants worked with the product per week, the more engagement related behavior was reported. Note, however, that the causal direction of effects cannot be determined here. More intense usage could be rather viewed as a consequence or even an integral part of engage, assuming that specific behavioral patterns (e.g., ‘‘In my spare time I’m playfully exploring this product’’) already imply more intense usage. Finally, the age of the participants emerged as a predictor for engage. The younger they were, the more extra motivation they invested. For evolve, four predictors were identified. Here, PQ and HQS emerged as almost equally important. When users were easily able to use their products without impairment, they more easily implemented their tasks in new ways with their products. In addition, the more stimulating users rated their products, the more they
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Model 1 1 2
included variables Execute PQ Engage HQS HQS self-rated product expertise 3 HQS self-rated product expertise product usage (hours/ week) 4 HQS self-rated product expertise product usage (hours/ week) age Evolve 1 PQ 2 PQ HQS 3 PQ HQS age 4 PQ HQS age product usage (hours/ week) expand 1 self-rated product expertise 2 self-rated product expertise computer usage (hours/week) Note. *p < .05, **p < .01.
changes in R2 .524 .191 .221 .237
.249
.242 .315 .334
.344
.030 .045
.724** .437** .411** .174** .410** .151** .130* .407** .146** .125* -.108* .492** .342** .310** .336** .309** -.136** .330** .308** -.131* .103* .173** .158* .124*
Fig. 6. Relation of user characteristics, product attributes and behavioral patterns.
explored them. Other than execute, evolve requires stimulation (e.g., novel functionality) in addition to pragmatic aspects. Its close relation to work tasks, however, distinguishes it from engage. With a rather small increase in additional explained variation (R2), the age of the users was the third predictor. Younger participants showed more evolving behavior, i.e., found it easier to explore their products. As fourth predictor, the participants’ experience with their products facilitated establishing new ways of using it. Two predictors emerged for expand. Both of them showed an additional explained variation (R2) of less than .10 when adding a variable to the model, which equals a small effect size (Cohen, 1992). One predictor was selfrated product expertise. Only with good knowledge of the product, users may be able to extending their product’s scope and going beyond the functionality it already offers. In other words, expand requires rather out of the box thinking which benefits from specific product expertise. In contrast to the other behavioral patterns, expand is not related to any product attribute. This suggests that expand is less ‘‘designable’’ than execute, engage, and evolve or at least that the set of product attributes the AttrakDiff2 questionnaire provides does not capture according product attributes. The most remarkable finding in this analysis is the fact that no model emerged that suggests user characteristics as
considerable predictor for any behavioral pattern except for expand, but with a very small effect size. Execute even resulted in a model with product attributes only. However, the lack of relation to general or specific expertise may also be due to the fact that a particular level of expertise has already been a requirement for being included into the study. By that, major problems, which may occur when being new to a specific product or technology and which can be solved by general expertise and specific knowledge of the product’s possibilities, may already be all resolved. For engage and evolve, the models containing the user characteristics specific product expertise, skill and age only gained a small increase in effect size compared to the model without any user characteristics. Similarly, the models for expand showed only quite small effect sizes, assuming that even those user characteristic variables that remained in the models rather failed to explain the variation in the behavioral patterns. Thus, user characteristic variables and behavioral patterns are mainly independent of each other. Instead, product attributes and behavioral patterns are strongly related. This means that those behavioral patterns are associated with attributes of the product the users are working with. It does not mean that behavioral patterns are depending on the product alone, as the associations users have with a product are mainly based on their individual experiences with the product.
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5. Conclusion We suggested four sets of behavioral patterns, which occur in the interaction with products used at work. Together, these sets describe a holistic approach to support users when working with interactive products. Users must not only accomplish their tasks efficiently, interactive products should also help them to work focused and persistently on their tasks. Work usually requires finding a way, modifying a way or picking the best way to complete a task, as there is not always an established method of accomplishing a given task. To fulfill one’s overall work goals, it might even be necessary to discover new tasks and sub-goals to supplement the overall work goal. We argued that interactive products have the potential to facilitate accomplishing tasks, persistence and modification of tasks or even creation of novel tasks. We asked a large group of users about their behavioral patterns and opinions when working with an interactive product they chose. All hypothesized behavioral patterns occurred and were stable over a period of 8 weeks. The participants associated these behavioral patterns with the products they used, thereby indicating a relationship between product and behavioral patterns. We further examined the difference in the relation between the behavioral patterns and characteristics of the person and behavioral patterns and attributes of the interactive product used. We started with an analysis of the structure of the behavioral items, which revealed no striking difference in the results of an analysis based on variance stemming from people only (i.e., subjects analysis) or products only (i.e., materials analysis). The behavioral patterns are not attributable to either the users or the interactive product, but to both. We then examined the direct influence of the various user characteristics and product attributes. Overall, user characteristics had little influence on the behavioral patterns. Effects were found regarding participants’ self-rated skill, their age and their expertise with the products or computers in general. Nonetheless, these effects were very small. Regarding the product side, the interactive products do influence the behavioral patterns. So although it is the users’ perceptions of product attributes that correlate with the behavioral patterns, leading to the assumption that the users themselves play an important role in determining a product rating, the user characteristics do not strongly affect this interrelation. To summarize, the four suggested sets of patterns occur in everyday work with interactive products. As the structure of the behavioral items is stable for users and products, the structure holds for both and reflects their interaction. The behavioral patterns do not describe the users’ work or interactive products alone, but the users’ work with these products as a whole. Nevertheless, this interaction is influenced mainly by the product attributes, whereas user characteristics play only a minor role. Of course, our four behavioral patterns do not exhaustively
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describe the requirements of the work context and may well be complemented by other aspects of the work context, such as helping co-workers with using their interactive products. Our approach to explore the users’ behavioral patterns instead of evaluating the product alone has been successful, as it focuses on the interaction of user and product. It emphasizes what is important in the specific context of usage, namely how users do their work and how users behave when using their products. It is independent of technological progress that makes adjustments necessary for conventional methods measuring product quality and it can be used by persons that do not have expert knowledge to judge an interactive product. Asking users about their behavioral patterns and feelings when using an interactive product hence gives evident information about the product used and yet addresses the interaction between users and product. Trying to find out about desired behavioral patterns when using certain interactive products is thus a promising approach, which is easy to apply to research and practice. Marketing departments might define behavioral patterns they aim for with their newest innovation and researchers might analyze different contexts to apply the idea of behavioral patterns to other contexts than the work context, e.g., educational or recreational context, and establish their own models about desired behavioral patterns. One limitation is the lack of detailed formative information to improve interactive products, which do not support certain sets of behavioral patterns. This is certainly a drawback of any brief and structured method. However, the present work not only presents a questionnaire, it also presents a model of aspects, which are important for the work context but not always a subject of a design effort. In this sense, the model and the questionnaire serves as a reminder of the fact that even in a work context, behavior beyond the mere execution of a given task needs to be addressed by a design. Products for work certainly must allow for the accomplishment of given tasks, but they also need to address motivational issues and must allow for changing the way one actually does the work currently. Nevertheless, deriving more detailed design strategies for each set of pattern surely is a future aim. A further interesting issue worth exploring is the temporal characteristics of the sets of behavioral patterns. In the present study, we restricted our sample to people with already a minimum of 4 weeks of expertise with the product. Expanding the time frame will provide interesting opportunities to take a closer look at the formation and developing of the behavioral patterns over time. The present study is one of the first attempts to translate the notion of User Experience (UX) to a work context. While certainly limited, it nevertheless reminds us of the importance of behaviors and according product attributes beyond the mere usability. It thereby questions the popular but obviously limited practice of distinguishing between work and leisure. In fact, people remain humans even in a work context (at least most of the time) and the particular
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tasks—especially when accomplished through interactive technologies—in a work compared to a private context may be less different than expected. More importantly, it renders over-simplistic approaches, which picture work mainly as a sequence of determined, predefined tasks as limited. People need to get their work done, but they also need to be motivated and improve their ways of doing things. This must be properly addressed—hence ‘‘designed’’—by any interactive product, which attempts to provide a good User Experience. Acknowledgments We would like to thank Steffi Heidecker and Nadja Zimmet for contributing considerably to the completion of this paper and for helping to revise the questionnaire items. References Baard, P.P., Deci, E.L., Ryan, R.M., 2004. Intrinsic need satisfaction: a motivational basis of performance and well-being in two work settings. Journal of Applied Social Psychology 34 (10), 2045–2068. Bargas-Avila, J.A., & Hornbæck, K., 2011. Old wine in new bottles or novel challenges? A critical analysis of empirical studies of user experiences. In: Proceedings of the CHI 11 Conference on Human Factors in Computing Systems, ACM, New York. Carroll, J.M., Kellogg, W.A., Rosson, M.B., 1991. The task-artifact cycle. In: Carroll, J.M. (Ed.), Designing Interaction: Psychology at the Human–Computer Interface. Cambridge University Press, pp. 74–102. Carver, C.S., Scheier, M.F., 1998. On the Self-Regulation of Behavior. Cambridge University Press. Cohen, J., 1992. A power primer. Psychological Bulletin 112 (1), 155–159. Cropanzano, R., Citera, M., Howes, J., 1995. Goal hierarchies and plan revision. Motivation and Emotion 19 (2), 77–98. Csikszentmihalyi, M., Larson, R., 1987. Validity and reliability of the experience-sampling method. The Journal of Nervous and Mental Disease 175 (9), 526–536. Davis, F.D., Bagozzi, R.P., Warshaw, P.R., 1992. Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology 22 (14), 1111–1132. Ellis, D., 1989. A behavioural approach to information retrieval system design. Journal of Documentation 45 (3), 171–212. Fogg, B., Nass, C., 1997. How users reciprocate to computers: an experiment that demonstrates behavior change. In: Pemberton, S. (Ed.), CHI ’97 Extended Abstracts on Human Factors in Computing Systems: Looking to the Future. ACM, New York, pp. 331–332. Forlizzi, J., & Battarbee, K., 2004. Understanding Experience in Interactive Systems. Paper presented at the Proceedings of the 2004 Conference on Designing Interactive Systems: Processes, Practices, Methods, and Techniques, Cambridge, MA, USA. Gagne´, M., Deci, E.L., 2005. Self-determination theory and work motivation. Journal of Organizational Behavior 26, 331–362. Hacker, W., 1986. Arbeitspsychologie. Psychische Regulation von Arbeitstatigkeiten. Huber, Bern; Stuttgart, Toronto. ¨ Harbich, S., Hassenzahl, M., 2008. Beyond task completion in the workplace: execute, engage, evolve, expand. In: Peter, C., Beale, R. (Eds.), Affect and Emotion in Human–Computer Interaction, vol. LNCS 4868. Springer, Berlin/Heidelberg, pp. 154–162. Hassenzahl, M., 2003. The thing and I: understanding the relationship between user and product. In: Blythe, M.A., Overbeeke, K., Monk, A.F., Wright, P.C. (Eds.), Funology: From Usability to Enjoyment. Kluwer Academic Publishers, Dordrecht, pp. 31–42. Hassenzahl, M., 2010. Experience Design: Technology for all the Right Reasons. Morgan Claypool, Francisco.
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