J. Design Research, Vol. 10, No. 3, 2012
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‘Soft’ usability problems with consumer electronics: the interaction between user characteristics and usability Chajoong Kim* and Henri Christiaans Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628 CE Delft, The Netherlands E-mail:
[email protected] E-mail:
[email protected] *Corresponding author Abstract: The paper reports a study into consumers’ ‘soft’ usability problems they experience using electronic household products. These problems cannot be traced back to a specification violation failure, classified as no failure found (NFF). The aim of this study is to find a relationship between consumers’ soft usability problems and their personal characteristics, encompassing demographical and cognitive aspects. The complaints collected through an exploratory survey were classified into three categories of soft usability problems. The findings indicate that demographic, socioeconomic and cultural characteristics as well as personal traits show significant correlations with these problem categories. Based on the data preliminary user profiles were made. By providing a new definition of usability problems and by user profiling, this study is expected to help design teams to get a better understanding of their target group. The implications of these findings for the product development process are discussed. Keywords: soft usability problems; user characteristics; user profile; user experience; cultural diversity. Reference to this paper should be made as follows: Kim, C. and Christiaans, H. (2012) ‘‘Soft’ usability problems with consumer electronics: the interaction between user characteristics and usability’, J. Design Research, Vol. 10, No. 3, pp.223–238. Biographical notes: Chajoong Kim is a PhD candidate at the Faculty of Industrial Design Engineering, Delft University of Technology in the Netherlands. He holds an MSc in Industrial Design Engineering from the same university. He has worked for Design for Usability project since 2007, within which his focus is on how user characteristics are related to usability experience. His main research interests include user diversity, cultural differences in design and cognitive aspect in usability. Henri Christiaans is an Associate Professor at the Faculty of Industrial Design Engineering, Delft University of Technology and Visiting Professor at Lisbon Technical University (UTL), Portugal. He received his PhD in the field of creativity in design. His research specialisations are in the areas of design thinking, information processing and cognitive ergonomics. He has published a lot in these areas. He wrote a book on research methods for the designer and engineer. He is the Editor-in-Chief of the Journal of Design Research.
Copyright © 2012 Inderscience Enterprises Ltd.
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Introduction
Most content management professionals know very well the importance of user-based acceptance testing, and understand the high stakes involved. If users fail to embrace a system (e.g., due to poor usability), generally speaking the project fails. Optimised user interface design requires a systematic approach to the design process. However, to ensure optimum performance usability testing is required. This empirical testing permits naïve users to provide data about what does work as anticipated and what does not work. Only after the resulting repairs or improvements are made can a product be deemed to have a user optimised interface. The importance of good user interface design can be the difference between product acceptance and rejection in the marketplace. But in spite of the many usability tests and methods available, and the thousands of papers about the importance of acceptance testing, in particular usability testing, by now examples of poorly designed consumer products are legion. Consumer electronics service centres are triggered by the increasing number of returned products caused by ignorant users (de Visser, 2008). Although there are good products, many others have a poor usability for two reasons: the lack of incentive and the lack of a usability culture in manufacturing companies. The current culture of many companies is that direct costs and profits always have priority. Consequently, they are facing increasing difficulties to obtain an acceptable level of consumer satisfaction and to guarantee the success of new products when released on the market. So, what kind of usability aspects are we talking about? Figure 1
Percentage NFF in modern high-volume consumer electronics
Source: Brombacher (2002)
Since consumer electronic products were launched on the consumer market, most complaints made by consumers have been about technical failure or malfunction of products. And over the years this type of problem slowly decreased, as did the complaints about them. However, from the late 90s this trend bent towards an increase in complaints but this time regardless of the technology. Research has demonstrated the increasing number of customer complaints on new products in consumer electronics industry (den Ouden, 2006). According to recent studies, nowadays about half of the reasons for product returns have nothing to do with technical problems, but are based on so called
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‘soft usability problems’, a term introduced by Brombacher et al. (2005) to refer to the familiar category of problems in electronic equipment called no-failure-found or no-fault-found (NFF), problems that cannot be traced back to a specification violation failure. Although in the traditional failure analysis NFF has mainly been referred to as material problems such as oxidation, defective connections of electrical components and so on (Qi et al., 2008), the same term became now useful to characterise consumer complaints about non-material aspects as well. Figure 1 illustrates this trend of increasing NFF over the last 30 years (Brombacher, 2002). One of the reasons why the causes for such complaints were unknown is that it is common in consumer electronic industry that customer complaints are dealt with by call centres. There are hardly any direct links between these centres and the product development departments. Consequently, companies were confronted more and more with a significant portion of product returns for which a technical problem was not found. But at the same time it didn’t get high priority. One of the reasons for that might have been the rapid economic growth and consequently the time-to-market pressure. Manufacturers were too much involved in developing new electronic products without identifying increasing customer complaints. Another source of dissatisfaction came from the tendency of manufacturers to include more and more functions into an electronic product. Technology made it easily possible to equip a whole range of products, from domestic appliances to the computer and telephone market with those extras. This policy was partly stimulated by research indicating that the number of product features is an important buying criterion: the more the product ‘can do’, the better it seems and the more attractive to buy even though many functions will never be used or understood. But at the same time this technological progress made it also possible to miniaturise products resulting more and more in black box designs that confuse consumers in perception, expectation and use (Broadbridge and Marshall, 1995). For example, individual electronic products such as radio, digital camera and mobile phone have become integrated into one single product. This situation resulted into an unexpected gap between actual product use by users and intended use by the manufacturer (Broadbridge and Marshall, 1995; Goodman et al., 2002) and a growing lack of product understanding (Norman, 2004). Many manufacturers continued developing consumer electronics from a technology point of view neglecting the user. Technical excellence of products only is not enough to make consumers satisfied as products have been absorbing technological progress, resulting in larger complexity regarding its characteristics and functionality (De Melo and Gontijo, 2000). Additionally, since the era of mass-production manufacturers tend to look at the similarities between people. Indeed, they have not taken into account the differences between people based on personal and cultural diversity. This is particularly so since electronic products are used by a much bigger variety of users than in the past. For instance, in the 1980s computer science engineers were the only users of the computer, while nowadays computer users range from children to elderly people. Apart from this lack of user focus by producers, nowadays the tolerance of consumers and end-users for a lack of quality and (soft) reliability problems with products is decreasing (Brombacher, 2005). Despite increased consumer dissatisfaction with consumer electronics caused by soft usability problems, there are only a few studies that investigate what soft usability problems consumers experience. One of the few studies to mention is that of den Ouden et al. (2006) who evaluated over 20 new product development projects to understand the
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reasons behind the rising number of consumer complaints. However, no soft usability problems were specified in detail in the study. In one of our earlier studies (Kim et al., 2007) a survey among Dutch and South-Korean people was conducted in order to get more insight in the kind of soft usability problems consumers experience. On the basis of the complaints seven categories of soft usability problems could be defined as follows: low understanding, poor performance, sensory problems, lack of structure, (expected) health problems, maintenance failures, and product constraints. However, in order to develop products that meet consumers’ expectations and decrease dissatisfaction the root cause of these soft usability problems should be found as well. At present, there is a lack of information on the causes of such soft usability problems.
1.1 User characteristics Soft usability problems can be attributed to the product itself, but also to the context and to user. In this study we focus on the user and his/her characteristics. The literature indicates that there is a relationship between user characteristics and complaining behaviour. But complaining behaviour, which means taking action to complain, is different from experiencing soft usability problems with consumer electronic products. Nevertheless, information about actual complaining behaviour may in a way help to get insight in experiencing soft usability problems. Regarding the variables that influence consumers’ complaining behaviour, several studies have shown that there is a relationship between complaining and demographic variables, psychographic variables (e.g., consumer personality and attitude toward firms) and product characteristics. In a study on complaint behaviour in Chile, Valenzuela et al. (2005) give an overview of the literature. This will partly be summarised here. Demographic variables are studied by Keng et al. (1995) and Heung and Lam (2003). They concluded that female consumers are more inclined to complain. However, the study by Manikas and Shea (1997) shows totally the opposite. Regarding the role of education, research has shown that there is a direct relationship between level of education and complaining (Day and Landon, 1977; Jacoby and Jaccard, 1981; Bearden and Mason, 1984; Morganosky and Buckley, 1986). Regarding psychographic factors, such as personality and attitude, Davidow and Dacin (1997) concluded that these factors are the major reasons of complaining behaviour. In the same line, other researchers concluded that consumers who complain are more socially responsible and willing to take risks, such as the risk of embarrassment when complaining (Fornell and Westbrook, 1979; Keng et al., 1995; Lau and Ng, 2001). Non-complainers considered that complaining was done by people with little else to do and would be futile (Keng et al., 1995). Concerning attitude toward firms, several researchers concluded that there is a positive relationship between responsiveness and complaining (Richins and Verhage, 1985; Keng et al., 1995; Lau and Ng, 2001). Day and Landon (1977) and Keng et al. (1995) concluded that consumers are more likely to complain if the product is not performing as promised and this situation can have a negative impact on the image of the firm. It was also demonstrated that there is a direct relationship between price and complaining behaviour, meaning that consumer will engage more in complaining behaviour if the product they are dealing with is more expensive. The aforementioned study by Valenzuela et al. (2005) in Chile also reported that Chileans feel somehow embarrassed when complaining, and if this characteristic is
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added to the fact that Chileans do not consider complaining as a right or social responsibility, it might lead to a low rate of consumers’ complaints. Furthermore, they found evidence that gender and social class are not relevant in this matter, which is different from those conclusions made in other studies (Keng et al., 1995; Phau and Sari, 2004). Statistically significant is the type of complainer. Active complainers have a more positive attitude while passive or non-complainers have a more negative attitude toward complaining. This result is in line with what was concluded by Kim et al. (2003). As can be seen from those studies, the focus is on what kind of people show active complaining behaviour and not on the reasons for complaining: the problems with products. Therefore, this study focuses on what soft usability problems people have experienced and how they are related to user characteristics.
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Method
In order to investigate what soft usability problems users experience with electronic consumer products and to measure their personal characteristics a questionnaire was developed. South Korean and Dutch subjects were recruited to participate in the study.
2.1 Subjects A total of 119 subjects participated in the survey: 60 Dutch (34 males and 26 females) and 59 South Korean (37 males and 22 females) people, who live in their home country. Since culture plays a role in the field of product design two countries were selected (Hofstede, 2003; Kim et al., 2006). They were randomly recruited through discussion forums on the internet and through the network of the researchers. Their age ranged from late teens to 60. It turned out that 15 subjects reported that they had no complaints about their electronic products. It would have been interesting to compare complainers with non-complainers, but this was not the aim of the study and the number of noncomplainers was too small. They were, therefore, excluded in the study. Sample selection in this way is not scientific if the aim is to generalise findings to the total population from which the sample has been selected. However, this study had an exploratory character meant to derive hypotheses for a next study.
2.2 Questionnaire Two open-ended questions were formulated to discover the causes of the soft usability problems experienced by users in the questionnaire (see for the first question Figure 2). The first question was with what product subjects feel most dissatisfied, other than technical problems, regarding interacting with electronic household products. In the second question, participants were asked to explain for the product, mentioned in question 1, what specific dissatisfaction or complaints they had. The other questions were about user characteristics, which consist of demographic, cognitive, personal, and cultural aspects (Table 1). The variables were selected on the basis of research findings in the field of consumer complaining behaviour and consumer (dis)satisfaction (Aykin and
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Aykin, 1991; Leventhal et al., 1996; Keng and Liu, 1997; Mooradian and Olver, 1997; Stephens and Gwinner, 1998; Chen-Yu and Hong, 2002; Khalid, 2006). Donoghue and De Klerk’s (2006) conceptual framework on consumers’ complaining behaviour was another source for our selection. They made a distinction between causal attribution, consumer-related and product-specific variables. This division was used to come up with a conceptual framework in our study as well. For most questions a five-point scale was used while some were dichotomous (yes or no) or multiple choice (e.g., locus of control). In order to increase the reliability of the scores on some variables questions were asked twice, with the same content but with different formulation. In the analysis, the mean of the two similar questions was taken as data. In Table 1 the variables with an asterisk (*) include that type of questioning. Figure 2
An example of question in the questionnaire (see online version for colours)
Table 1
List of user characteristics measured
User characteristic
Measured variable
Demographic aspect
Age, gender, educational level (the higher number the more educated)
Cognitive aspect
Memory (the higher number the more memorising ability)* Use fixation (the higher number the higher use fixation)*
Personality
Patience (the higher number the more patient)* Locus of control (the higher score the stronger external locus of control) Uncertainty avoidance (the higher score the higher uncertainty avoidance)
Cultural aspect
Nationality
2.3 Procedure The subjects participated in the survey by filling in either a web-based questionnaire or a questionnaire on paper. Through discussion forums for product review and the researchers’ network, people were invited to visit a website where the questionnaires were uploaded. The answers given by them were automatically saved into a database on the internet. The second way to recruit participants was through the researchers’ network of people who live either in Korea or in the Netherlands. They were asked to fill in the questionnaire on paper. All the answers from both the web-based and the paper questionnaire were input into a SPSS data sheet and were then statistically analysed.
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Results
Demographic variables of the sample are presented in Table 2. The survey came up with 185complaints that have no relation with technical failure. Some subjects reported more than one complaint. The statistical analysis was based on 185 complaints in total. First, the complaints reported are classified based on the three soft usability problem categories and on two consumer electronic product properties such as cognitive load and interaction density. Next, the relationships between soft usability problems and product properties will be explored, followed by the interaction between user characteristics and soft usability problems. The sample will not be representative for the total population between 20 and 60 years old. Because most participants are not recruited or selected other than through a web-platform, they will be probably representative for the population of internet visitors: more men than women, most of them from the age group between 20 and 30, and highly educated. Nevertheless, for the purpose of this study this ‘biased’ sample can offer interesting insights into the relationship between user characteristics and soft usability problems. Table 2
Demographic characteristics of complainers (N=104)
Demographic variables
Frequency
% per variable
17–29
64
61
30–39
23
22
40–49
8
8
50–59
9
9
Male
67
64
Female
37
36
1
1
Age at time of survey (years)
Gender
Highest education level completed H.S. grad Some college
9
9
College grad
22
21
Postgraduate (Master degree)
46
44
Postgraduate (Doctoral degree)
26
25
South Korean
59
57
Dutch
45
43
Cultural background
3.1 Soft usability problems and product properties Since there was variance in consumer complaints across different types of products, the products were first divided in two categories according to the cognitive load involved (Figure 3). For instance, more mental load is invested in using a laptop computer, or mobile phone that belongs to high cognitive load category, than a coffee machine, which belongs to low cognitive load category. They were also divided in two categories
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according to interaction density (Figure 4). For example, more interaction between user and product occurs in using a vacuum cleaner or shaver that belongs to the high interactive category than a washing machine or alarm clock that belongs to low interactive category. Figure 3
Examples of electronic products according to cognitive load
Figure 4
Examples of electronic products according to interaction density
Finally, the types of problems people report were in our earlier studies categorised in the aforementioned seven types. Based on other studies [among others Madureira (1991), mentioned in Dantas (2011)] this number was for reasons of clarity further reduced to three in the current study. According to Madureira consumers evaluate the purchased product during its useful life, in three ways: •
Sensory quality: product qualities related to the sensory perception of the form, appearance, texture, touch, size, weight, level of noise and vibrations, qualitative standard, general behaviour of the product, and trend.
•
Functional quality: product qualities related to function, performance, compatibility or technical error in making use of the product.
•
Operational quality: product qualities related to the durability of the product, the need for maintenance and repairs, understandability in finding or understanding functions, feedback and feed forward during the useful life of the product.
In Figure 5 the findings of our previous study are re-organised according to the three quality types. There are no significant differences in the number of complaints in each of the three usability problem types.
‘Soft’ usability problems with consumer electronics Figure 5
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Three types of soft usability problems
The results indicate that soft usability problems are partly dependent on product property in terms of cognitive load [see Figure 6(a)]. The operational quality problem is the biggest in high cognitive load products while the sensory quality problem plays a dominant role in low cognitive load products. The results also indicate that soft usability problems are partly dependent on interaction density [Figure 6(b)]. The operational quality problem is the biggest in the low interactive product group while the sensory quality problem plays a dominant role in high interactive products. Figure 6
Relation between type of soft usability problem and (a) cognitive load (b) interaction density (in %)
(a)
(b)
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3.2 User characteristics and soft usability problems Frequencies of user characteristics in each soft usability problem type are presented in Table 3. Table 3
Mean values or frequencies for predictor variables as the occurrence of soft usability problem Sensory quality (k = 61)
Variable
Functional quality (k = 58)
Operational quality (k = 66)
Memory
3.52
3.53
3.55
Use fixation
2.48
2.73
2.86
Uncertainty avoidance
61.80
59.40
64.85
Locus of control
74.84
71.29
73.18
Educational level
4.84
4.72
4.97
Age
31.82
28.10
32.86
Patience
2.70
2.68
2.86
Male (%)
34.80
27.80
37.40
Female (%)
30.00
37.10
32.90
South Korean (%)
42.7
29.8
35.5
Dutch (%)
29.7
34.4
35.9
In order to get a deeper understanding of the correlations a multinomial logistic regression was used for predicting the probability of occurrence of a particular soft usability problem type together with a particular user characteristic. A multinomial logistic regression for three categories compares sensory quality complaint group to functional quality complaint group and operational quality complaint group to functional quality complaint group (see Table 4 and Table 5). Table 4
Summary of multinomial logistic regression analysis predicting sensory quality complaint relative to functional quality complaint
Variable
B
SE
Odd ratio
Wald statistic
Memory
–.113
.224
.893
.256
Use fixation
–.391
.229
.677
2.909
Uncertainty avoidance
.015
.014
1.105
1.047
Locus of control
.017
.017
1.017
1.075
Educational level
–.109
.208
.897
.274
Age
.073
.028
1.075
6.930**
Patience
.241
.332
1.273
.529
Gender
.458
.408
1.581
1.264
Culture
.415
.515
1.514
.647
Note: **Coefficients are significant at p < .01.
‘Soft’ usability problems with consumer electronics Table 5
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Summary of multinomial logistic regression analysis predicting operational quality complaint relative to functional quality complaint
Variable
B
SE
Odd ratio
Wald statistic
Memory
–.097
Use fixation
.186
.228
.908
.180
.219
1.204
.721
Uncertainty avoidance Locus of control
.036
.15
1.037
1.037*
.004
.016
1.004
.072
Educational level
.183
.215
1.201
.728
Age
.079
.028
1.082
8.106**
Patience
–.346
.339
.708
1.041
Gender
.412
.405
1.509
1.034
Culture
.474
.496
1.606
.911
Notes: *Coefficients are significant at p < .05. **Coefficients are significant at p < .01.
From the tables it becomes clear that the only user variables which show significant relations are age and uncertainty avoidance. Age differences are significant between all three quality problems: on average, people mentioning sensory and operational problems are older, while those reporting functional problems are the youngest. The average score on uncertainty avoidance is higher among subjects who complained about operational quality than among those who complained about functional quality. Although the other correlations are not significant, there are some interesting conclusions to draw. The probability that a person experiences dissatisfaction related to sensory quality is 1.581 times higher for male than for female, 1.514 times higher for South Korean than for Dutch participants, 0.677 times lower for a person who has high use fixation than for a person who has low use fixation, and1.3 times higher for a person who is patient than for a person who is impatient, all other factors being equal. The probability that a person experiences dissatisfaction related to operational quality is 1.509 times higher for male than for female, 1.606 times higher for South Korean than for Dutch, and 0.708 times lower for a person who is patient than for a person who is impatient, all other factors being equal.
4
Conclusions and discussion
As an explorative study, the findings offer some interesting results in relation to the original aims. The study focused on the relationships between soft usability problems, product properties, and user characteristics. In the data some relationships were observed between soft usability problems and product properties, between demographic variables and product properties, and between user characteristics and soft usability problems. They will be discussed below.
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4.1 Soft usability problems and product properties: cognitive load and interaction density Products requiring low cognitive load are relatively simpler and easier to use and vice versa. It makes sense that participants had fewer complaints on understanding or finding functions in using such simpler products; in contrast, complaints related to products requiring high cognitive load were dominant on operational quality level. An obvious explanation is that people have more difficulty in understanding functions of complex products than of simple products. An interesting finding is also that when there are complaints about products requiring low cognitive load they are more often related to sensory qualities. Opposed to this, complaints related to sensory quality were least reported in using products requiring high cognitive load. However, the percentage (38%) is not too low to be neglected. Looking at interaction density, a second product property, high interactive products give more complaints related to sensory quality than low interactive products. This correlation makes sense considering that the higher interactive products are associated with more frequent exposure to our senses. Complaints related to operational quality were most reported in using low interactive products. One of the reasons is that low interactive products require physical efforts in starting to operate and maintaining them compared with the high interactive products.
4.2 User characteristics and soft usability problems To some extent cognitive and personal characteristics are related with the type of soft usability problems. This implies that consumer electronic products are experienced in different ways between individuals. Age makes a difference in types of complaints. Older subjects complained more than younger on sensory and operational qualities in the study while younger ones complained mostly about functional quality. It might be because older ones could be more sensitive to sensorial inputs and have less understanding of how electronic products work. For younger subjects, performance and functionality of their electronics are more seriously taken into account in using them. Uncertainty avoidance is also related to types of complaints. Subjects with high tolerance for uncertainty and ambiguity reported problems related to performance and functionality of their electronics while ones with low tolerance complained more about operational quality. A possible explanation is that risk-taking people are adventurous and so to some extent could expect unlimited functionality of electronic products. By contrast, people avoiding uncertainty are reluctant to face any situations that they are not familiar with. This might be related to why they complain more about operational quality such as understanding of complex functions or poor durability of their electronic products.
4.3 Improvement of the design process The group of customer complaints for which no cause can be determined is denoted as no failure found (NFF). Research into this increasing number of customer complaints by Den Ouden (2006) indicates that 85% of the complaints can be traced back to decisions
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made in the product creation process. In other words, most of the customer complaints in consumer electronics are predominantly caused by a wrong decision in the product creation process. In order to reduce the number of future problems with consumer electronics products, she suggests improving the decision making processes in the product creation process by supporting it with up-to-date and rich information about customer use preferences. However, as can be seen from practice, just information will not be sufficient. According to Geudens (2008), six major market trends can be distinguished that lead to a higher complexity and therefore more soft usability (reliability) problems. These trends are: •
increasing product functionality (i.e., performing multiple tasks)
•
increasing market globalisation (i.e., the same products are sold around the world)
•
increasing sales price reduction (i.e., high competition causes lower prices)
•
increasing warranty coverage (i.e., consumers have a high warranty demand)
•
decreasing time to market (i.e., to gain market share a product has to be one of the first on the market)
•
increasing industry globalisation (i.e. products are developed and realised in factories around the world).
As a consequence of the six major market trends, companies are forced to design their products according to changing conditions. Some of these conditions are the shorter development time and the need for a product that is ‘adoptable’ by a wider variety of consumers, all of whom have different needs. Although these conditions have been changed during the last decade, most companies still use the same approach when developing new products in which the increasing numbers of soft problems are not taken care of. Due to this insufficient approach, companies fail to focus on the specific consumer needs and the individual consumer expectations are not fully known. The contribution of the present study lies foremost in the emphasis on the importance of considering user diversity related to the occurrence of soft usability problems. The aim of this exploratory study was to find any relationship between soft usability problems and the personal background of the participants. It also attempted to investigate if soft usability problems are dependent on product properties. The results indicate that 1
soft usability problems differ between product properties according to required cognitive load and interaction density in use
2
soft usability problems are to some extent dependent on users’ characteristics.
However, the limited number of participants, including sample bias, compared with the number of variables measured gives a limitation to this study. This study is meaningful in the sense that it gives an overview of how user characteristics interact with product usability. This study can contribute to a better understanding of user profiles in estimating the seriousness of the complaint and in designing better products people love to use.
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Acknowledgements The authors gratefully acknowledge the support of the Innovation-Oriented Research Programme ‘Integrated Product Creation and Realization (IOP IPCR)’ of the Netherlands Ministry of Economic Affairs.
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