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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.18 No.2 pp.163-172
DOI : https://doi.org/10.7232/iems.2019.18.2.163

Exploring the Relationship between Idea Quality and Satisfaction on New Ideas for Smart Products

Ilsun Rhiu, Myung Hwan Yun*
Division of Big Data and Management Engineering, Hoseo University, Asan, Republic of Korea
Department of Industrial Engineering & Institute for Industrial System Innovation, Seoul National University, Seoul, Republic of Korea
Corresponding Author, E-mail: mhy@snu.ac.kr
February 19, 2019 April 8, 2019 April 16, 2019

ABSTRACT


Many firms have been struggling with selecting optimal new ideas and evaluating various new ideas of smart products or services which may satisfy users enough. This study aims to understand the idea quality dimensions and how these affect the overall satisfaction in regards to new smart product ideas. Thus, a research model for exploring the relationship between idea quality dimensions and overall satisfaction was developed. This study was conducted on eighteen new ideas of smart products which were developed by researchers and practitioners. The evaluation was conducted among 51 participants. The findings partly support the research model and confirm that idea quality dimensions are influencing factors in satisfaction for new smart product ideas. ‘Relevance’ and ‘Attractiveness’ have significant positive influence on overall satisfaction of new smart products ideas. But, ‘Workability’ has no significant influence on overall satisfaction. The research advances the understanding of the effect of idea quality on overall satisfaction for smart product ideas.



초록


    1. INTRODUCTION

    Recent years were a period where Internet of Things (IoT) became a very disruptive force in the growth of the IT industry. People are now surrounded by products that can collect data and process on their own. These products which are called as a smart product contains microchips, softwares, and sensors, so it can collect, process, and produce information. Everyday products can now keep a track of how often we use them and remind us then it is time to order new batteries or replacement parts. For example, mobile phones can download our email, display digital photos, remind us of today’s appointment/agenda, and let us scan the internet for breaking news over breakfast.

    However in reality, researchers have discovered that consumers actually do not often utilize smart product’s “smart” functions (Korzun et al., 2013). Consumers don’t see smart products as smart, because they consider products “smart” only when they actually experience subjective advantages (Rijsdijk et al., 2007), far from novelty or functionality of the product.

    The example of smart TV shows that even though the manufacturers focus on adaptation of the novel interface, the consumers would have no interest, if it is not apt to traditional norms and habits (Shin et al., 2013;Dou et al., 2018). More than half of the owners only use smart TVs to watch broadcastings, being only interested in video quality or design factors (Bachelet, 2013;Tarr, 2013). This is due to the fact that manufacturers did not consider user experience (UX) enough to improve their usability of functions (Shin et al., 2013;Dou et al., 2018). This example portrays that researchers should identify which services are in need and how to make the consumers use it before adopting the new technologies or ideas. Therefore, developing new ideas or technologies which users will satisfy becomes more important than just improving new technologies.

    Moreover, many firms have been struggling with selecting optimal new ideas and evaluating various new ideas of smart products or services which may satisfy users enough (Eliashberg et al., 1997;Fjermestad and Hiltz, 1998). Thus, many researchers have made their efforts on evaluating new ideas. For years, researchers and practitioners have studied methods of increasing the idea output of individuals and groups, respectively. Particular emphasis has been placed on improving the tools and methods used to support idea production because the ability to generate ideas is critical to enhance innovation and managerial problem-solving abilities (Barki and Pinsonneault, 2001;Diehl and Stroebe, 1987).

    According to Briggs et al. (1997), two challenges confront researchers wishing to evaluate the output of an idea generation or new product development process. First, a reliable way to rate each individual idea must be devised, which is especially difficult as in idea generation studies the number of ideas commonly ranges from several hundred (Dennis et al., 1996) to more than a thousand (Hender et al., 2002). Second, the ratings of individual ideas must be aggregated into an overall score in order to assess the performance of the individual or group that produced the ideas. Thus, it is important to identify the relationship between overall satisfaction and idea quality dimensions.

    Previous studies about idea quality have mainly focused on creativity and novelty of idea itself. Several previous studies also indicated that idea quality should consider feasibility of ideas and conformity to design specifications (Shah et al., 2003). In engineering design, this metric is required because an engineering idea needs to be feasible and practical (Charyton et al., 2011). Also, previous studies on the relationship between idea quality and user satisfaction/preference have not been conducted sufficiently. Therefore, identifying the important dimensions of idea quality for overall satisfaction of new ideas can be helpful for developing or evaluating new ideas.

    This study aims to understand the idea quality and how these elements affect the overall satisfaction. The research questions explored in this paper are as follows:

    • RQ1. Which idea quality scales and sub dimensions are found?

    • RQ2. How do these idea quality scales affect overall satisfaction for new smart products?

    The remainder of this article is organized as follows. Chapter 2 introduces the existing studies related to this study. Chapter 3 describes research methods, and Chapter 4 shows the results of the experiments. Finally, the discussions and results of this study are described in chapters 5.

    2. RELATED WORKS

    2.1 Evaluating Idea Quality of New Products

    Idea generation (ideation) is identified as a key step in system development, organization planning, decision making, and problem-solving (Whitten and Bentley, 1998). It is therefore understandable that interventions for improving the method of idea generation have been the subject of much research (Barki and Pinsonneault, 2001;Diehl and Stroebe, 1987;Fjermestad and Hiltz, 1998). Thus, many researchers have made their efforts on evaluating idea quality.

    Idea quality is related to feasibility and conformance to design specifications (Shah et al., 2003). In engineering design, this metric is required because an engineering idea needs to be feasible and practical (Charyton et al., 2011). Previous studies about idea quality are summarized in Table 1.

    There are previous studies about quality of idea focused on feasibility (e.g., Lamm and Trommsdorff, 1973;Durand and VanHuus, 1992;Linsey, 2007). Lamm and Trommsdorff (1973) proposed that quality of an idea is effectiveness (the ability of an idea to fulfill the given requirements) plus feasibility (i.e. extent to which an idea can be implemented under the constraints of reality). Durand and VanHuss (1992) suggested that originality, appropriateness, detail, depth, and clarity are important factor for quality of idea. According to Linsey (2007), quality is synonymous to technical feasibility or implementability.

    Moreover, previous studies considered novelty and creativity of idea (e.g., MacCrimmon and Wagner, 1994;Cady and Valentine, 1999;Mumford, 2001;Potter and Balthazard, 2004;Dean et al., 2006). For instance, Dean et al. (2006) suggested workability (acceptability plus implementability), relevance (applicability plus effectiveness), and specificity (completeness) as sub-dimensions of quality.

    Novelty is an important ideation metric (Dahl and Moreau, 2002). According to Shah et al. (2003), novelty is a measure of how unusual or unexpected an idea is as compared with other ideas including those from other individuals. This suggests that uncommon ideas are likely to be seen as novel. Shah et al. (2003) classified novelty into three different types, namely personal novelty (the outcomes of an individual are new according to that individual), societal novelty (a product or idea is new to all people in a particular society), and historical novelty (a product or idea is the first of its kind in the history of all societies and civilizations).

    Jansson and Smith (1991) explain variety as the flexibility of generating a range of ideas. Variety means that how different concepts are from each other (Shah et al., 2003). A low flexibility indicates a narrow range of generated ideas, while a high flexibility shows a broadly searched idea space (Jagtap et al., 2015). To estimate variety, Sarkar and Chakrabarti (2008) compare the number of similar ideas to those with less similarity. Generating a large number of ideas that are very similar to each other does not guarantee an effective idea generation.

    Those measures are usually used for evaluating ideas or concepts in the early stage of NPD process. There are also previous studies about evaluating more developed ideas or concepts such as products and concept. Previous studies mainly focused on the creativity of products. Main dimensions of previous studies are summarized in Table 2.

    Those studies mainly evaluated products or scenario by creativity, feasibility/workability, affect. Novelty is the most important factor for measuring creativity (Amabile, 1983;Besemer and O’Quin, 1986;Christiaans, 2002;Hor and Salvendy, 2006). Many measures (e.g., resolution, elaboration, goodness of example, importance, etc.) are selected and used for evaluating feasibility or workability. Although those measures are limited to evaluating creative products/concepts, some of them could be utilized to evaluating the overall satisfaction on ideas or concepts.

    2.2 Research Model and Hypotheses

    In this study, the research model is developed as follows: First, the dimensions and attributes related to idea quality from previous studies were collected by an extensive literature review. Second, in order to identify the structure of idea quality, appropriate usage and similarity of meaning among the collected attributes were evaluated by five product design experts. Since some attributes are defined broadly and some are identified in too much detail, the meanings of some characteristics were merged, specified, or refined in the process of re-organization. As a result, the idea quality dimensions could be recategorized into the three main dimensions: ‘Workability’, ‘Relevance’, and ‘Attractiveness’. ‘Workability’ means that an idea/concept can be easily implemented and be acceptable to users. ‘Relevance’ means that an idea/concept applies to the stated problem and will be effective at solving the problem. Also, ‘Attractiveness’ means that an idea/ concept is attractive if it is novel and provides excitement and interest to users. Previous studies related to idea quality could be re-categorized to above three main dimensions (Table 3).

    Finally, we used the crowdsourcing platforms to avoid bias. Five product design experts were recruited to merge or adjust some dimensions that were defined too broadly or in too much detail, and the meanings of some dimensions were similar. We classified each sub dimension into the predefined category only when at least four of the five experts agreed on the classification.

    ‘Workability’ is related to how an idea is feasible (Shah et al., 2000, 2003). It could be categorized into three sub dimensions: Feasibility, Adoptability, and Acceptability. MacCrimmon and Wagner (1994) claimed that the concept of workability is composed of two aspects: easy to be implemented and not violate known constraints. Diehl and Stroebe’s (1987) definition of feasibility (preciseness and ease of implementation given available constraints) includes both aspects of feasibility/workability, which have also been recognized by other authors who have used this definition in other studies (Gallupe et al., 1992). Cady and Valentine (1999) define quality as, among other things, the degree to which an idea can be successfully adopted by users. In addition, that idea must not violate known constraints. Valacich et al. (1994) use implementability, while Cooper et al. (1998) use social acceptability.

    ‘Relevance’ is related to that an idea should apply to the stated problem and must be expected to solve the problem. It could be categorized into three sub dimensions: Completeness, Effectiveness, and Applicability. Researchers have previously used completeness, applicability and effectiveness in relation to relevance. Complete and clear idea can be more useful for solving the problem. MacCrimmon and Wagner (1994) developed this dimension from U.S. Patent Office specifications, which require that ideas be “full, clear, concise, and exact.” Other researchers have emphasized different aspects of the specificity dimension. For example, Durand and VanHuss (1992) judged ideas on the basis of clarity, depth, and amount of detail, whilst Cady and Valentine (1999) judged them by how well they were described. Some previous studies focused on applicability such as relation to topic (Aiken and Vanjani, 1997;Aiken, Vanjani et al., 1996), usefulness for purpose (Fern, 1982), and appropriateness (Runco and Charles, 1993).

    ‘Attractiveness’ is related to the novelty of idea and users’ affection on idea. It could be categorized into three sub dimensions: Novelty, Interest, and Excitement. Novelty is one of a key factor for the creativity of idea. MacCrimmon and Wagner (1994) defined a novel idea as one that had not been previously expressed. According to this definition, then, a novel idea is unique or, at least, rare. Also, affect is important to attract users to use products or services.

    According to above literature review and crowdsourcing platforms, the hypotheses can be developed as below.

    H1. Idea quality (H1a: ‘Workability’, H1b: ‘Revlevance’, H1c: ‘Attractiveness’) has a significant positive influence on satisfaction of ideas/concepts of new products.

    The conceptual model was developed as shown in Figure 1. This model argues that the idea quality has an impact on the satisfaction of ideas/concepts. The model further points out that the overall satisfactions of new ideas/concepts are based on their assessment of the idea quality dimensions for smart products.

    3. METHOD

    To validate the idea quality dimensions for new smart products/services and to explore the relationship between idea quality dimensions and satisfaction, the evaluation survey was conducted. The overall procedure of this study is as follows. First, we identified the idea quality dimensions for new smart products through literature survey and an expert review. Second, experiments were conducted in order to verify the reliability and the validity of the idea quality dimensions. Finally, we explored the relationship between the idea quality dimensions and satisfaction of new ideas/concepts of smart products through the statistical analysis.

    3.1 Participants and Ideas for Smart Products

    A total of 18 new ideas/concepts for smart products were evaluated (Table 4).

    Fifty one participants were recruited for the survey. Among them, 31 participants have minimum 3 years experiences in product engineering and ergonomics research, and 20 participants have minimum 5 years experiences in designing and developing new products/services in electronic company. The participants were on average 29.51 years old.

    3.2 Questionnaire and Statistical Analysis

    The online survey system was used to build the questionnaire. The participants rated every idea quality dimensions (7-points scale) and overall satisfaction (7- points scale). The descriptions of idea quality dimensions and overall satisfaction are presented in Table 5.

    The collected data was analyzed using the statistic package software SPSS version 25.0. Data analysis of the experiment consists of two parts. The first part is about the reliability and the validity of the idea quality dimensions. The reliability as well as the validity of the questionnaire was verified through Cronbach’s α values and factor analysis. The second part aims to identify and explore the relationship between the idea quality and satisfaction of new ideas/concepts. Pearson correlation analysis was used to examine the correlation of the variables of dimensions. Also, multiple linear regression analysis was adopted to test the hypotheses.

    4. RESULTS

    4.1 Scale Reliability and Validity

    Cronbach’s alpha is an index of reliability associated with the variation accounted for by the true score of the underlying construct. Construct is the hypothetical variable that is being measured. The idea quality includes a variety of dimensions, and a higher reliability coefficient represents a higher correlation of respective dimensions, which illustrates higher internal consistence. As each idea quality was defined as multiple variables and evaluated by Likert scale, Cronbach’s alpha statistic as a measure of reliability test was conducted.

    Alpha coefficient ranges in value from 0 to 1 and may be used to describe the reliability of factors extracted from dichotomous (that is, questions with two possible answers) and/or multi-point formatted questionnaires or scales (i.e., rating scale: 1 = poor, 5 = excellent) (Santos, 1999). When Cronbach’s alpha value is greater than 0.6 ~ 0.7, it is referred to as high reliability; when the value falls between 0.6 and 0.35, it is considered as fair reliability, and the value smaller than 0.35 is taken as low reliability (Nunnally, 1967).

    The results of the reliability analysis show that the Cronbach’s alpha value of ‘Workability’ is 0.790, ‘Relevance’ is 0.723, and ‘Attractiveness’ is 0.858 (Table 6). Given its variables all reaching a level of high reliability, it illustrates that the overall consistence of the questionnaire of this study is in high reliability.

    To show how valid a questionnaire is, it is necessary to measure variable characteristics (Chow, 2004). Since the questionnaire was designed by referring to the research scales developed by the researchers within and without, and modified by reviewing various kinds of literature, it would meet the requirement of content validity. If factor in facet measurement is between 0.5 and 1.0, the values of respective dimensions are all greater than 1, and the accumulated explained variances of respective variables are all greater than 50 per cent, the overall measurement quality of the questionnaire is good and the questions in the questionnaire are appropriate, then the questionnaire has construct validity (Chiou, 2000).

    As shown in Table 6, the eigenvalues of idea quality are all greater than 1, each facet’s factor loading is between 0.500 and 0.900, and accumulated explained variances are greater than 50 percent. Also, the variance for the first factor is less than 50%, these falls within the acceptable level (Field, 2013;Podsakoff and Organ, 1986). The Kaiser-Meyer-Olkin (KMO) overall measure of sampling adequacy (MSA) was 0.807, which is acceptable (Kaiser, 1974; Hutcheson and Sofroniou, 1999). In addition, the Bartlett’s test of sphericity was 4787.553, df = 36, and significant at p < 0.001 which showed a significant correlation among the variables (Field, 2013). Therefore, those illustrated that the questionnaire used in this study meet the requirement of construct validity.

    4.2 The Relationship between the Idea Quality Dimensions and Satisfaction

    Pearson’s correlation analysis was conducted to confirm the correlation of two dimensions and the correlation coefficients of respective variables are shown in Table 7. As the results shown in Table 7, all of correlations are significant (p < 0.05). However, though ‘Workability’ has significant correlation with satisfaction, the coefficient value is quite low (0.210).

    To identify the importance of idea quality dimensions for satisfaction of new ideas/concepts, a regression analysis was conducted. The result of the regression analysis was shown as Table 8. The regression model for user satisfaction containing the three idea quality accounted for 74% of observed variance (R2 = 0.740, F (3,769) = 727.866, p < 0.001). As shown in Table 8, all hypotheses except H1a were significant at least the 0.05 level, strongly supporting the proposed research model. H1b and H1c hypothesizing the association between idea quality and satisfaction were supported with standardized coefficient (β) of 0.515 and 0.405, respectively. Thus, according to the results, H1 is partly supported.

    5. DISCUSSION AND CONCLUSION

    In this paper, a framework of evaluating new idea/ concept for smart products was developed. Idea quality dimensions were collected from previous studies, and then they were re-organized into three main dimensions: ‘Workability’, ‘Relevance’, and ‘Attractiveness’. To validate the conceptual model, an evaluation experiment was conducted. From the results of the experiment, the reliability and validity of idea quality dimensions were proved. Also, the results of the correlation analysis showed that ratings, according to each idea quality dimension, were related. Also, it shows that overall satisfaction for new smart product ideas is associated with idea quality dimensions. Moreover, the relationship between idea quality dimensions and satisfaction on new idea/ concepts of smart products were identified through the regression analysis. For example, according to Figure 2, ‘Relevance’ is relatively more important than the others (β = 0.515). Also, it is found that ‘Workability’ has no significant influence on satisfaction. Therefore, to develop successful new products or services of smart products, it could be recommended that designers and developers should consider effectiveness, applicability, novelty of new idea rather than technical feasibility.

    This study could give some theoretical and practical implications. Some of the findings in this study are consistent with the viewpoints held by other researchers and empirical study results (Chan et al., 2018). Especially, ‘Relevance’ and ‘Attractiveness’ have significant effect on satisfaction. However, ‘Workability’ has no significant effect on satisfaction. Generally, ‘Workability’ is one of the important factor in accepting new idea (Kijkuit and Van Den Ende, 2007;Ferioli et al., 2010;Rietzschel et al., 2010). As we evaluated new smart product idea in this paper, it differs from the general situations. In the case of innovative ideas for future products, some of the more advanced technologies and methods are introduced, so that even if the idea cannot be implemented right now, some ideas could be preferred (Kim and Mauborgne, 2015). Thus, although developed new ideas for smart products which are innovative and need high technologies in the future are difficult to be implemented or acceptable to users and developers, users and developers will be satisfied if the idea/concept is relevant and attractive. Moreover, the attitude or tendency of users and developers to the innovative product can affect on the relationship between ‘Workability’ and satisfaction on new ideas (Manning et al., 1995;Zaltman and Wallendorf, 1979). Therefore, to generalize the results of this study, specific and diverse further studies should be conducted on the detail relationship between ‘Workability’ and satisfaction of new ideas related high technologies for general products in the future.

    Also, in this study, Pearson correlation analysis and multiple linear regression analysis were conducted. However, there could be two sources of numerical errors in the measurement of perceived dimensions of idea quality and product smartness in this study. First, the semantic differential scale gives ordinal data, and the calculation of mean values is strictly not permissible (Stevens, 1946). In other words, the measurement scales are not necessarily linear. Second, the regression analysis assumes a linear relationship between the rating of dimensions and satisfaction, while it is accepted that these are more likely to be related by a power law (Chen et al., 2009). Therefore, it is possible that the regression models might not capture some important relationships. In the future research, more statistical analysis with more experiments on general users and various situations could be conducted to figure out more relationships.

    ACKNOWLEDGEMENTS

    This research was supported by the Academic Research Fund of Hoseo University in 2017 (20170091).

    Figure

    IEMS-18-2-163_F1.gif

    The conceptual model of idea quality.

    IEMS-18-2-163_F2.gif

    Satisfaction model of new idea/concept based on idea quality dimensions.

    Table

    Previous studies’ dimensions of evaluating idea quality

    Previous studies’ dimensions of evaluating creative products/concepts

    Re-categorization of dimensions of idea quality

    New ideas/concepts for smart products/services

    Descriptions of idea quality dimensions and overall satisfaction

    Results of factor analysis and reliability test for idea quality dimensions

    Pearson Correlation matrix of idea quality dimensions and satisfaction

    <i>Note</i>: * correlation is significant at the 0.05 level (2-tailed).

    Multiple linear regression analysis for the influence of idea quality on satisfaction of new ideas/concepts

    <i>Note</i>: <i>R</i><sup>2</sup> = 0.740, <i>F</i> (3,769) = 727.866, <i>p</i>< 0.001.

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