Master of Science in Software Engineering October 2020
Exploring the relation between
stakeholder inertia and product
requirements
This thesis is submitted to the Faculty of software engineering at Blekinge Institute of Technology in partial fulfilment of the requirements for the degree of Master of Science in Software Engineering. The thesis is equivalent to 20 weeks of full time studies.
The authors declare that they are the sole authors of this thesis and that they have not used any sources other than those listed in the bibliography and identified as references. They further declare that they have not submitted this thesis at any other institution to obtain a degree.
Contact Information: Author(s): Ting Fang E-mail: tifa18@student.bth.se Yiweihua Huang E-mail: yihu18@student.bth.se University advisor: Krzysztof Wnuk Software engineering
Faculty of Software engineering Blekinge Institute of Technology
A
BSTRACT
User inertia is a real innovation adoption problem that cannot be seen or grasped. How to overcome user inertia while introducing innovation has become a key factor in today's society. Based on the background of requirements engineering, the goal of this thesis is to study and understand the relationship between user inertia and innovation adoption, including whether the type of innovation has an impact on user adoption and how to use strategies to reduce this impact. Through an online survey of 60 users and a systematic literature review of a series of articles, we have concluded the following points: RI has a greater impact on user inertia, while II has almost no impact; neither RI nor II has a significant impact on user satisfaction. Not only that, but through literature review, we have also concluded a comprehensive strategy to deal with the adoption problem caused by user inertia.
Design/methodology/approach
The customer inertia was tested by using online survey research of 60 people who use iPhone X. Meanwhile, after analysing the data of survey, combined the outcome with a literature review to get the final expected outcome for this thesis.
Findings
O1: Have an understanding of the causes and benefits of both incremental and radical innovations. O2: Organize and combine literature on both user inertia, product management, innovation, and software product lines for further research.
O3: Create guidelines how to realize both incremental and radical innovation and handle potential user inertia: what requirements engineering techniques should be used and why.
Value
This paper is from the perspective of users to take care of the problem of requirement engineering, which is useful for the organization. The strategy of expected outcomes can not only improve brand image and competitive advantage but also increase productivity and reduce costs. Most importantly, if the company adopts relevant strategies and successfully executes them, this series of behaviours can attract more employees and investors joined. The problems and aims of this paper will be a long-term study and will continue to affect the future of software engineering.
A
CKNOWLEDGEMENT
Foremost, we would like to express our sincere gratitude to our supervisor Prof. Krzysztof Wnuk, for the continuous support of this thesis, for his patience, motivation, enthusiasm, and immense knowledge. His guidance helped us in all the time and enlightened us the first glance of this research.
Besides our supervisor, we would like to thank our friend who gave a lot of significant opinions: Huizhong Pang, for her encouragement, insightful comments, and hard questions.
We sincere thanks also goes to participator who participated in the questionnaire survey and helped us got the data that we needed. We are over helmed in all humbleness and gratefulness to acknowledge depth to all those who have helped us to put these ideas, well above the level of simplicity and into something concrete.
C
ONTENTS
ABSTRACT ... III ACKNOWLEDGEMENT ... IV CONTENTS ... V 1 INTRODUCTION ... 0 1.1 BACKGROUND ... 01.2 DEFINING THE SCOPE OF YOUR THESIS ... 1
1.3 OUTLINE ... 2
2 RELATED WORK ... 3
2.1 RESISTANCE TO INNOVATION ... 3
2.2 RADICAL INNOVATION AND INCREMENTAL INNOVATION ... 3
2.3 USER INERTIA AND CUSTOMER HABIT ... 5
3 METHOD ... 7
3.1 RESEARCH QUESTION AND HYPOTHESIS ... 7
3.2 SURVEY ... 10
3.3 SYSTEMATIC LITERATURE REVIEW... 11
3.4 VALIDITY THREATS ... 12
3.4.1 Survey ... 12
3.4.2 Systematic literature review ... 13
4 RESULTS AND ANALYSIS ... 15
4.1 DATA FROM SURVEY ... 15
4.2 DATA FROM SLR ... 19
5 DISCUSSION... 31
5.1 RQ1: WHAT IS THE IMPACT OF DIFFERENT TYPES OF INNOVATION ON USER EXPERIENCE AND SATISFACTION IN TERMS OF USER INERTIA?... 31
5.2 RQ2:WHAT REQUIREMENTS ENGINEERING TECHNIQUES SHOULD BE USED TO CREATE THE GUIDELINE THAT HELPS TO HANDLE POTENTIAL USER INERTIA? ... 32
6 CONCLUSION AND FUTURE WORK ... 35
1
I
NTRODUCTION
1.1
Background
Since the last two decades, the update and iteration of software products, whether it is hardware or software, have been developing faster than ever before. People seem to have a feeling that new products or new changes are continually flowing and dominating the market every day [1]. It is not difficult to see that if companies want to preserve their market position, they need to keep on publishing innovations in order to face it, which means that innovation has become a priority for many companies. New changes often use innovative interfaces to determine how consumers interact with new products to gain their functionality [6]. At the same time, whether these innovative technologies can meet the needs of users or whether users can easily accept these new changes has become a key factor in the success of the technology [25]. Users determine the success of the product, so this technology also drives the development of demand engineering.
When it comes to innovation, it is usually divided into two types: incremental innovation (II) and radical innovation (RI). Incremental innovation is known as good certainty and low risk. It focuses on continuously improving the competitiveness of products by reducing costs, improving or adding features [18]. Radical innovation, on the other hand, is the opposite. There are numerous examples of market failures due to its uncertainty and high risk [12]. However, due to its high degree of innovation, once successful, the introduction of breakthrough new products can change the existing market or industry, or even create new markets or industries [3]. One of the most classic examples of RIs in recent years is that Apple Inc. has removed the physical buttons on the new generation of iPhones, turned its screen into a comprehensive screen and introduced Face ID. As we all know, the home button has been one of its logo designs since the iPhone was born. For a long time, user habits have relied on the use of the home button. However, times are changing. Users are increasingly pursuing the practicality and comfort brought by the large screen. If Apple wants to leave enough space for the home button and make the body of the iPhone look harmonious, they need to sacrifice the screen ratio. In order to implement the full screen, Apple must give up the old design of home button, they hope to improve the user experience by increasing the screen ratio. No one has developed a full screen before, and it turns out that Apple has done it right. The introduction of a full screen is not only for beauty, but also a big innovation of FaceID, skipping fingerprint unlocking, and more technologically advanced. Apple led the future of mobile phone unlocking and got a lot of benefits from this technology.
Different from innovation, consumers' ideas may be like or forced to accept these innovations due to the ingestion of a large amount of new information [19].
Whenever we want to introduce innovation, it involves changing old habits. Charles Duhigg in the book
they move through an organization. It matters more than others in remaking businesses and lives”. Breaking habits, or adding new changes into software, could influence people’s life.
A typical human trend is to strive to maintain consistency and existing status, rather than constantly seeking and accepting new things [21]. Therefore, customers often strive to maintain their established usage habits [20]. Whether at work or in daily life, all software users have their own habits, ranging from the position of a button in a system to the brand of a software. The trend about people choosing to buy or continue to use products is called user inertia [2]. When introducing new innovation, due to familiarity with the previous product or version and resistance to change, can lead to user tension between innovation and inertia. The problems between innovative products and habits are worthy of attention. One of the benefits of solving this type of problem is that it can increase user satisfaction and loyalty, making the company's value become higher. Mentioned in the article of Wunk that many companies were failed by RI due to the wrong product innovation strategy. The products of these companies often fail due to inflexible strategies, and few studies have now taken user inertia into consideration [2].
It is worth mentioning here that some of the innovations are generated based on user needs. How to enhance the current demand initiation and analysis technology to capture and analyze the inertia of stakeholders is also where we should pay attention to. Therefore, we need to find or create some effective strategies for software developers or software companies to solve this type of problem.
How to solve the relationship between people’s habits and innovation will be the focus of this thesis. Since this article is from a user perspective, so the stakeholder that we discussed in this thesis refer to users, as long as it reduces the extent to which user inertia affects user habits. Not only that, whether the type of innovations will affect people’s choice is also one of the discussion points in this article. This research will be based on the Apple Inc. system iOS update to discuss the relationship between user inertia and innovation.
1.2
Defining the scope of your thesis
1.3
Outline
2
R
ELATED
W
ORK
2.1
Resistance to innovation
In the past research, everyone realized the importance of innovation. Good innovation can improve the market competitiveness of products and even open up a new market. Jonathan [4] studied how Kodak and Fujifilm responded to the impact of digital cameras. From the number of film cameras and digital cameras sold from 1997 to 2010, it is not difficult to see that digital cameras as a radical innovation have caused a fatal blow to traditional film cameras. Until 2003, the sales volume of film camera was consistently outstripped that of digital cameras. One reason is that although digital cameras are better, users were more willing to use film cameras due to their habits.
According to Sven’sresearch [5], from the perspective of new product development, it seems essential to develop new products with the appropriate level of innovation to achieve market success. A low degree of product innovation may not provide enough potential to create customer value or to differentiate from competitors, but a high degree of innovation may bring too much change and discontinuity to individuals, endanger the actual situation and may trigger the initial competition. However, innovations are often not easily accepted by customers. For example, innovation often brings new interfaces, PL Ziamou [6] examined the factors that affect customers' uncertainty about the performance of new interfaces and customer adoption intentions. He believed that there are two challenges when commercializing new interfaces. One is to determine the most suitable functions for the new interface. The second is to communicate with customers to reduce the uncertainty about the performance of the new interface, thereby increasing customer adoption intentions. When studying the resistance of customers to adopt innovation[7][8], barriers are usually divided into functional and psychological barriers, and functional barriers are divided into three categories, namely usage barrier, value barrier and risk barrier. Psychological barriers are divided into two categories, tradition barrier and image barrier. Many researchers on innovation start at the company level, and study how to use marketing methods to make customers better accept innovation. For example, use specific strategies such as mental simulation and benefit comparison. The literature that studies innovation barriers also uses marketing methods from the company's perspective, rather than starting from how the product itself avoids barriers that cause customers.
2.2
Radical innovation and Incremental innovation
environments [10]. Although most existing companies seldom or never choose to pursue RI project, some studies have pointed out that, due to the new technologies that involved by RI, such innovations are often the driving force of economic growth for companies, which have the ability to essentially eliminate old products and fundamentally replace existing ones. Not only that, the success of RI can create an industry economy and make a company which promotes it become a market leader [11]. As M. König and L. Neumayr [12] said, only when the relative advantage or compatibility is higher and the complexity is lower, the probability of innovation being adopted will be higher. Compared with existing products, RI products can provide more benefits to customers, which is more in line with customers' psychological expectations. However, due to its novelty, RI products require more time and energy to make it become skilled at, causing the low understanding of RI products to customers [9]. Therefore, companies should also attach importance to the acceptance of RI from customers. Whether customers are willing to change their mental models is one of the essential factors. This is related to the user's inertia and habits, which will be highlighted in later part. It has never been easy to successfully implement and complete RI. Birgitta Sandberg and Leena Aarikka Stenroos [14] pointed out that the barrier to the implementation of RI is divided into external barrier and internal barrier. They believe that the degree of novelty of RI is not related to the type of obstacles, but depends on the innovation environment and the activities of the innovation process. In addition, some companies are so fascinated by the success of existing products that prevents them from launching next-generation brand-new products; what’s more, because of some force majeure within the company, such as personnel transfer or bureaucracy, will cause RI to fail to proceed successfully [11]. Many published studies related to RI are started from the enterprise level rather than putting in the customer‘s shoes. For instance, innovative reports working on customer satisfaction of RI are lacking specific strategies. Even if these academic articles tackled the implementation of RI for the company, the fundamental problem of RI would be solved, which refer to questions like how to improve RI’s acceptance or how to maintain the new RI products. Taking customers into account would help the development of RI not on the implement level but a marketing perspective.
to become more intense. Some studies have indicated that since the market competition is too fierce, the total amount of II will also decrease [17]; One of the reasons behind this is that if II is not handled properly, it will make other competitors tend to vigorously develop RI, and this behavior may reverse the market. Then the absorptive capacity of companies that do not want to give up this kind of market is particularly important in this case. Only after the participants in the competition have effectively exchanged and absorbed existing knowledge with each other can they improve II at a higher quality level [18]. None of the study related to II miss out the importance of customers. But articles in this area still lack of topics on how could customers get involved in this innovation to make II more complete. Besides, instead of considering customers’ obstacles to II, the previous studies only take the technical challenges faced by the producers into account. The customer inertia and individual habits are essential factors for customer acceptance and satisfaction when it comes to using a product (whether or not it is a software product). Although II would not change the customer inertia or individual habits fundamentally, the inconvenience caused by this innovation needs to be taken into account. In another word, the effect of II on user experience, including the number of innovations, both strengths and weaknesses, needs a further discussion.
2.3
User inertia and customer habit
The typical human tendency is to strive for consistency and status quo rather than constantly seeking and accepting new behaviors. Passive innovation resistance [19] is caused by customers' general tendency to resist innovation before new product evaluation. Passive innovation resistance is defined as the resistance to the change imposed by innovation. Innovation of passive resistance mainly depends on the will or their individual resistance to change the status quo of satisfaction, or a combination of these. customers usually without considering potential cases refused to innovation, so that the process before the start of the real end. Resistance to innovation is a normal response to customers and must be identified and managed to promote the adoption of new products. Individuals often develop an emotional attachment to the products they own.
are considered the main premise of behavior. The meaning of the basic situational response mechanism behind the habit is that through the strengthening of the habit, the behavior becomes less and less sensitive to the current goals and plans, and the continued desire to perform the old behavior or the failure of willpower. The main challenge with the changes that accompany innovation is that recurring environmental prompts will continue to automatically activate old habits. [20]
Wunk [2] analyzed the instrument cluster changes of BMW and Citroen manufacturers and found that customer inertia has a big impact on user satisfaction and incremental innovation may be better than radical innovation. And in their research, it was found that too many new features will lead to user exclusion, but there is currently no suitable method to solve the problem of user inertia. Wunk conducted a literature review and found that the main reasons for the stakeholders not to adopt the product are: brand inertia, customer inertia, product failure to meet customer needs and the company's choice of wrong strategy leading to product failure. Customer inertia is the key content of this research. For customers, using new products means economic costs and learning costs. In addition, social influence is also a factor. If consumers resist the product, the negative influence will spread everywhere. In the innovative communication framework proposed by Everet Roger [22], relative advantages, compatibility, complexity, trialability and observability are five factors that affect product acceptance. Overall, the company needs innovation to gain competitiveness in order to win a larger market. But too much innovation will lead to customer dissatisfaction and cause market losses. What strategies should be used for disruptive innovation and progressive innovation to avoid users' habit resistance? There is no perfect way to solve customer inertia problems.
3
M
ETHOD
3.1
Research question and Hypothesis
As mentioned at the beginning of this article, how to introduce innovative products to the market and make users accept it is hugely challenging companies nowadays. Innovation, no matter from which point of view, is related to change and the essence of this behaviour is the demand of users for the change [21]. According to data, innovation is not a smooth event. The failure rate of innovative products is as high as 50% [46]. The reason for the backside of high innovation failure rates is diverse. This article mainly studies the contradiction between user resistance due to habit and innovative products. Starting from the perspective of software requirements engineering, we determine customer requirements, help them to analyze and understand problems, and make decisions to help users integrate with innovative products.
We will introduce our research questions and conjectures in this chapter, and then introduce our research methods. In order to better understand how we carry out our research, we would like to introduce the following knowledge points.
The classification of INNOVATION
The importance of innovation has been widely recognized, and it can be divided into two types: radical innovation (RI) and incremental innovation (II). Previous research has shown that the impact of RI and II on users is entirely different [47]. A study by the C. Page Moreau [28] finds that RI is a discontinuous innovation, which means that it will be used differently from previous products and requires high-volume learning. On the contrary, II is named as continuous innovation, can enhance the user experience and satisfaction without breaking existing knowledge. Therefore, there have been more studies in the literature on RI disorders than on II. RI barriers is a sophisticated, scattered, manifold phenomenon [14]. Existing research on RI barriers tend to starts from the company level [48], with the primary purpose of obtaining profitability, and rarely thinking from the user perspective. As for II, it has the characteristics of high success, and due to its degree of innovation and change are limited [32] making it difficult to implement the marketing strategy together with RI. Since there is no clear definition of these two innovations, the single-latitude marketing strategy advocated in previous studies cannot really solve the hidden needs of users.
The existence of HABIT
Therefore, even if users may know the benefits of innovative products, the habit will still drag them back in the past. Under the guidance of the pattern, customers will have user inertia, which is to resist new products. Therefore, we have to think about the existence of user resistance.
The introduction of PIR
One of the essential reasons for the failure of new products is consumer resistance to innovation. These resistances can be differentiated into two different types: Active innovation resistance (AIR) and Passive innovation resistance (PIR) [23]. According to Klejinen's research [24], AIR is a negative attitude after actively using a new product. At the same time, PIR is triggered by resistance to change or satisfaction with the status quo before using the new product. Through the classification of user resistance, habits are mainly distributed in PIR. Correspondingly, Table 3.1 from Jennifer’s study [21] indicates that in terms of innovation resistance, habit belongs to the PIR type.
Driver Description Examples references Mechanism of resistance
Physical risk Consumers decide not to adopt a new product due to possible physical risks Stone and Grønhaug 1993 Active
Functional risk
Consumers decide not to adopt a new product due to uncertainty about complementarity with existing or upcoming products
Antioco and Kleijnen 2010;
Szmigin and Foxall 1998 Active
Social risk Consumers decide not to adopt a new product because of concern about others’ evaluations Dholakia 2001 Active
Economic risk Consumers decide not to adopt a new product because of difficulty determining its true value or whether the price will change over time
Kleijnen et al.2007;
Parasuraman and Grewal 2000 Active
Perceived switching costs
Consumers decide not to adopt a new product because of difficulty in learning the new product or recognized costs to learning the new over keeping the old
Murray and Häubl 2007;
Zauberman 2003 Active
Considerable exposure to information about or aspects of an alternative product
Consumers decide not to adopt a new product because they prefer products they have encountered repeatedly in the past based on heightened familiarity, at least when these new products are reasonably complex
Cox and Cox 2002; Moreau et
al. 2001 Active
Choosing an established habit over a new product that conflicts
Consumer decide not to adopt a new product when it cannot be integrated into a well-established pattern of
use that consumers have no desire to change Kleijnen et al. 2009 Active Slipping back into an
established habit for using an alternative product
Consumers continue to use existing products rather than adopting new ones when existing usage habits are strong and they fall back into these old patterns
Ram and Sheth 1989; Wasson
1979 Passive
Unawareness or indifference to new products
Consumers continue to use existing products rather than adopting new ones when they do not know about
or think the new product is relevant for them Joseph 2010 Passive
Preference for status quo, disinclination to change
Consumers continue to use existing products rather than adopting new ones when they are generally resistant toward change or are content with their current situation
Bagozzi and Lee 1999; Ellen et al. 1991; Gourvile 2006; Oreg 2003; Ram 1987; Sheth 1981; Szmigin and Foxall 1998; Talke and Heidenreich 2014
Passive
Therefore, we define this type of habit-induced PIR as HPIR. In order to solve the problem that starts from habit, we need to start with HPIR, which is a crucial inhibitor for users to accept innovative products.
User satisfaction
User satisfaction is one of the most important criteria to measure the success of demand engineering. In the case of innovation, it is also the most important customer-related performance indicator for innovative products [49]. In Ruth's article [22], they found that innovation-alone cannot improve user satisfaction, the relationship between innovation and user satisfaction presents as nonlinear.
Fig 3.1. The relationship between satisfaction and innovativeness [22]
In other words, customer satisfaction cannot be directly proportional to the improvement of innovation. Even if the innovation is high enough, it doesn't guarantee that users will pay for it. What's more, there is a special case that when users feel satisfied with the product, they will choose the old product instead of the new one. Considering the uncertainty and perceived value of customers, it becomes a difficult thing to improve user satisfaction. If we want to make the innovation succeed, do we need to consider controlling the degree of innovation or other strategies? The answer will be obtained in this paper.
Research question
RQ 1: What is the impact of different types of innovation on user experience and satisfaction in terms of user inertia?
Hypothesis
Through the analysis of the above points, we put forward the following hypothesis, corresponding to our RQ.
H1: The HPIR of RI is higher than II, leading to a decline in user experience and satisfaction and a tendency to reject innovation.
H2: The single strategy (such as bundling) in previous research does not work well for innovative product adoption.
Based on research questions and hypothesis, we conducted two studies (surveys and literature reviews) to come up with strategies for how not to break user habits and let them accept innovative products.
3.2
Survey
According to research goals, this article decided to start research from two aspects. One is to understand the extent to which their habits are affected by innovation and their satisfaction with innovation from the perspective of consumers, and the other is to study how to overcome consumer inertia from the perspective of the company.
Since there are many successful and failed innovation cases in the market, it is difficult to compare the results through product evaluation surveys. Due to the rapid development of science and technology, the functions of mobile phones are varied, and mobile phones have become one of the products around us that are updated relatively quickly. In recent years, Apple has been a leader in the mobile phone industry, and every time Apple launches a new iPhone, it incorporates many innovations. iPhone X is the representative mobile phone in the iPhone series, because it began to use innovative elements such as full screen. Therefore, this survey will select iPhone X as a related product.
the survey, the conditions of the participants will be reconfirmed whether they meet the requirements to ensure the accuracy and authenticity of the data.
3.3
Systematic literature review
Systematic Literature Review
In this part of the study, to collect sufficient and practical strategies to cope with HPIR, systematic literature review was chosen as the research method, which is defined as the topic area searched in a given topic and evaluated or selected by existing literature.
Because of the sheer volume of literature and the risk of missing out on important literature, Therefore, in order not to lose relevant papers but also to improve the accuracy and reduce time consumption [50], we used the snowballing procedure as the main procedure of SLR.
Under the guidance of Wolhlin et al [50], this paper strictly followed the snowballing procedure to studied the thesis topic. The specific steps are shown in the figure below.
Fig 3.2 Snowballing procedure [50] Keyword setting
For the sake of obtaining the start set accurately and make it more valuable for research, a series of keywords were set:
Keyword 1: Customer habit
Extend keyword: Radical innovation; Incremental innovation; Innovation barriers; Innovation management
Keyword 3: Product management
Extend keyword: Customer satisfaction; Marketing management; Risk management; Business goal After searching for the above keywords, seven articles in related fields were selected as the start set of the snowballing procedure.
Inclusion and Exclusion criteria
At the same time, in order to quickly screen out important documents, this article has formulated a series of criteria:
For the inclusion criteria:
1. Selected articles must be readable for full 2. Selected articles must be from after 2000
3. Selected articles must be related to the requirement engineering field 4. Selected articles must be related to the product innovation field 5. Selected articles must be related to innovation resistance 6. Selected articles must have strategies for product innovation For the exclusion criteria:
1. The abstract of the article cannot be read 2. The article is mainly about AIR rather than PIR 3. Unable to extract relevant policies
4. The same strategy repeatedly appears in the same iteration
5. The limitations of the article include the validity of the verification strategy
The selected article in each iteration has forward iteration and backward iteration with a blanket search. The forward iteration refers to the reference of the selected article, while the backward iteration refers to the citation of the selected article. Through the above keywords and criteria, this article has formed the final result set for SLR after 5 iterations of forward iteration and backward iteration. The specific results will be shown in the next chapter.
3.4 Validity Threats
3.4.1 Survey
The potential threats to the validity of our survey and the used measure to reduce its impact are shown below:
year and used iPhone before, thereby reducing the threat. In addition, we have published the survey in Chinese and English on multiple social platforms around the world, and we have received 60 valid questionnaires within the regulations. Based on this number, we think we can get a general summary result. However, the age distribution of the 60 participants was uneven, which may reduce the validity of the results.
Internal Validity: The internal threat of the survey is mainly composed of question setting and sample selection. We used a unified format to ask questions and conducted pilot experiments. Through feedback, we changed the description of the questions, reducing the possibility of participants misunderstanding the question. In addition, the survey is designed by the Likert five-level scale, which is convenient for participants to give answer. For the selection of samples, we conducted a manual check after receiving the answers and use SPSS for reliability analysis to improve the validity of the questionnaire.
Conclusion Validity: For the conclusion threat of the survey, the sample size and selected products are the main threats. First of all, due to limited time and ability, we only received 60 valid questionnaires, and the age distribution is uneven, which may lead to deviations in conclusions. Secondly, the selected product, iPhone X, was released three years ago. Participants' sensitivity to both radical innovation and incremental innovation would decrease. The data we got may deviate from when participants first used iPhone X.
Construct Validity: For the construct threat of the survey, we considered the rigor and understandability of the question description. After discussion, we formulated the first draft of the questionnaire, and then conducted a pilot trial. Based on feedback, we made several revisions and were approved by the instructor.
3.4.2 Systematic literature review
The potential threats to the validity of our snowballing process and the used measure to reduce its impact are shown below:
Internal validity: Internal validity determines whether the causal relationship between two variables can be appropriately represented. We conducted five rounds of iterative research to obtain as much literature as possible without any research bias. At the same time, due to the limited database we selected, this may make the documents we find insufficient. Therefore, to mitigate this threat, we chose a snowball-style study to screen selected articles. The steps of snowballing are completely under the guideline of Wolflin et al. [50].
Reliability validity: Reliability validity means that an indicator with good reliability can be repeatedly operated under the same or similar conditions to obtain consistent or stable results. In this thesis, we have recorded all the document collection processes. The audit was conducted under the guidance of the supervisor. To minimize the selection bias of selected articles, we strictly follow the established inclusion and exclusion criteria. Not only that, but we also followed all the guidelines and agreements from the supervisors.
4
RESULTS AND
A
NALYSIS
4.1
Data from survey
A total of 60 valid questionnaires were received for the study. It can be clearly seen from Table 4.1 that the gender distribution of the participants is relatively even, and most of the participants are between 21 and 30 years old. In order to investigate the user's unsuitability to use innovation at the beginning and the evaluation of innovation when used to it, the scale is divided into four dimensions: the user's unsuitability to RI at the beginning, the user's unsuitability to II at the beginning, the user's satisfaction with RI after use, and the user's satisfaction with II after use. We will use the abbreviations RIHabit, IIHabit, RISatis and IISatis to replace the four-dimensional questions. For example, RIHabit2 means the second question of ‘the user's unsuitability to RI at the beginning’. The questionnaire uses the Likert five-level scale which can be found in APPENDIX B.
Category Frequency Percent
Gender Male 29 48.3%
Female 31 51.7%
Age 15-20 years old 8 13.3%
21-30 years old 41 68.3%
31-45 years old 6 10.0%
Over 45 years old 5 8.3%
Table 4.1 Frequency Table
It can be seen from Table 4.2 that the Cronbach's alpha values of the four dimensions of the scale are 0.782, 0.843, 0.734, and 0.775, respectively. Cronbach’s alpha is a measure of internal consistency, that is, how closely related a set of items are as a group [https://stats.idre.ucla.edu/spss/faq/what-does-cronbachs-alpha-mean/]. It is considered to be a measure of scale reliability. In most social science research, reliability coefficients above 0.70 are acceptable, which indicates that these items have relatively high internal consistency.
Dimension Cronbach’s Alpha N of Items
User's unsuitability to II at the beginning .843 5
User's satisfaction with RI after use .734 4
User's satisfaction with II after use .775 5
Overall Scale .746 18
Table 4.2 Reliability Table
As shown in Table 4.3, ‘N’ represents number of answers, ‘Minimum’ represents the smallest value in all answers, ‘Maximum’ represents the biggest value in all answers, ‘Mean’ represents the average value of all answers and ‘Std. Deviation’ means standard deviation which is used to measure the deviation of the data value from the arithmetic mean. In the four radical innovation examples, the user expresses three radical innovations are unsuitable (3 means neutral, 3 or more means unsuitable), only the average value of RIHabit2 is lower than 3. According to follow-up interviews with participants, they believe that although iPhone X supports wireless charging, it can still be charged in traditional ways, and the wireless charger needs to be purchased separately, which results in most people not actually using this feature, so participants generally Think that this innovation does not make them feel uncomfortable. In general, RI will cause obvious discomfort in the first use.
N Minimum Maximum Mean Std. Deviation
RIHabit1 60 1 5 3.50 1.157
RIHabit2 60 1 5 2.85 1.005
RIHabit3 60 1 5 3.18 1.308
RIHabit4 60 1 5 3.32 1.112
Valid N (listwise) 60 Table 4.3 Descriptive Statistics Table of RI unsuitable
The second finding in the survey is that II hardly arouses user resistance based on user inertia. As shown in Table 4.4, in the example of 5 incremental innovations, the user does not feel uncomfortable with them. The average value of II5 is 3.03, which is close to neutral. And others are all lower than 3. After discussing, the reason is that users think that the control center has changed very much, and the layout and opening methods have changed. It can be seen that II basically did not cause discomfort to users.
N Minimum Maximum Mean Std. Deviation
IIHabit1 60 1 5 2.55 1.199
IIHabit2 60 1 5 2.53 1.112
IIHabit4 60 1 5 2.68 1.200
IIHabit5 60 1 5 3.03 1.193
Valid N (listwise) 60
Table 4.4 Descriptive Statistics Table of II unsuitable
The last finding in the survey is that neither RI nor II ultimately affect innovation satisfaction. Table 4.5 and Table 4.6 are the user satisfaction ratings for RI and II respectively. The user satisfaction ratings for RI and II are relatively average, between 3 and 4, indicating that users are relatively satisfied with these innovations. It can be seen that users have similar satisfaction with RI and II.
N Minimum Maximum Mean Std. Deviation
RISatis1 60 1 5 3.33 .933
RISatis2 60 1 5 3.52 .911
RISatis3 60 1 5 3.88 .846
RISatis4 60 1 5 3.47 .724
Valid N (listwise) 60 Table 4.5 Descriptive Statistics Table of RI satisfaction
N Minimum Maximum Mean Std. Deviation
IIHabit1 60 1 5 3.68 .770 IIHabit2 60 1 5 3.63 .802 IIHabit3 60 1 5 3.57 .963 IIHabit4 60 1 5 3.50 .893 IIHabit5 60 1 5 3.82 .770 Valid N (listwise) 60
Table 4.6 Descriptive Statistics Table of II satisfaction
The survey results are shown in Table 4.7. At the beginning, users have a certain degree of unsuitability with RI and II. Relatively speaking, users' discomfort with RI at the beginning is significantly higher than the unsuitability with II at the beginning. But users' satisfaction with RI and II are almost the same. This provides strong evidence for H1.
N Minimum Maximum Mean Std. Deviation User's unsuitability to RI at the beginning 60 1.00 5.00 3.2125 .89469
User's satisfaction with RI after use 60 2.50 5.00 3.5500 .63912
User's satisfaction with II after use 60 2.80 5.00 3.6400 .61153
Valid N (listwise) 60 Table 4.7 Descriptive Statistics Table
In order to test whether there is a significant difference in the degree of unsuitability caused by different types of innovations (RI and II), the normality analysis of the two sets of samples is first necessary. In general, if the sample size exceeds 50, it is considered a large sample, and Kolmogorov-Smirnov (K-S) is used to test its normality. Since the number of samples in this survey is 60, the samples will use the K-S method to analyze normality. As shown in Table 4.8, the K-S Significance (Sig) value of the two sets of samples is greater than 0.05, which indicates that the two sets of samples are in accordance with the normal distribution.
Tests of Normality
Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
RIHabit .111 60 .065 .963 60 .069
IIHabit .102 60 .189 .971 60 .171
a. Lilliefors Significance Correction
Table 4.8 Tests of Normality (user’s unsuitability to RI and II)
Two sets of paired samples that conform to the normal distribution can use the paired sample T test. As shown in Table 4.9, the significance of the test result (Sig. (2-tailed)) is 0.000, which is less than 0.05, which indicates the degree of unsuitability caused by different types of innovations (RI and II) has a significant difference, the degree of unsuitability caused by RI to users is higher than that of II.
Paired Samples Test Paired Differences t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 RIHabit - IIHabit .5391 7 .96915 .12512 .28881 .78952 4.309 59 .000 Table 4.9 Paired Samples Test
4.10, the K-S Sig values of the two sets of samples are 0.004 and 0.000, both are less than 0.05, indicating that the two sets of samples do not conform to the normal distribution.
Tests of Normality
Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
RISatis .143 60 .004 .946 60 .010
IISatis .197 60 .000 .894 60 .000
a. Lilliefors Significance Correction
Table 4.10 Tests of Normality (user’s satisfaction to RI and II)
Two sets of paired samples that do not conform to the normal distribution can use the Wilcoxon signed rank test of paired samples. As shown in Table 4.11, the significance (Sig.) of the test result is 0.365, which is greater than 0.05, which indicates that there is no significant difference in user satisfaction with different types of innovations (RI and II).
Test Statisticsa
IISatis - RISatis
Z -.906b
Asymp. Sig. (2-tailed) .365
a. Wilcoxon Signed Ranks Test b. Based on negative ranks.
Table 4.11 Test Statistics
4.2
Data from SLR
Result for SLR
After five snowballing iteration, this article selected 32 related articles from 6011 articles. The detail analysis of the selected articles are as follows.
Start Set
By following the keywords and criteria, with the guidance from proposal and related work, We chose 7 articles to combined into the start set. As shown in the table 4.12, it can be clearly seen that P4 and
P7 are conference articles and others are journal articles.
P1 [21]
Labrecque J.S., Wood W., Neal D.T. et al (2017). Habit slips: when consumers unintentionally resist new products. Journal of the Academy of Marketing Science, 45(1):119-33.
P2 [22]
Stock, R.M. (2011). How does product program innovativeness affect customer satisfaction? A comparison of goods and services. Journal of the Academy of
Marketing Science. 39(6):813-27.
P3 [23]
Heidenreich S., Kraemer T. (2016). Innovations—Doomed to Fail? Investigating Strategies to Overcome Passive Innovation Resistance. Journal of Product Innovation
Management. 33(3):277-97.
P4 [14]
Sandberg B., Aarikka-Stenroos L. (2014). What makes it so difficult? A systematic review on barriers to radical innovation. Industrial Marketing Management. 43(8):1293-305.
P5 [9]
Reinders M.J., Frambach R.T., Schoormans J.P.L. (2010). Using Product Bundling to Facilitate Adoption of Radical Innovations. Journal of Product Innovation
Management. 27(7):1127-40.
P6 [16]
Menguc B., Auh S., Yannopoulos P. (2014). Customer and Supplier Involvement in Design: The Moderating Role of Incremental and Radical Innovation Capability: Customer and Supplier Involvement in Design. Journal of Product Innovation
Management. 31(2):313-28.
P7 [2]
Wnuk K., Svensson R.B., Callele D. (2012). The effect of stakeholder inertia on product line requirements. 2012 Second IEEE International Workshop on
Requirements Engineering for Systems, Services, and Systems-of-Systems (RESS), pp. 34-37,
Table 4.12 Result of Start set
Iteration 1
Through the start set, this article found a large number of literatures about strategies to overcome habits. A total of 912 articles participated in this round of screening. According to the standard, we selected 14 articles and combined them into the first round of iterative collection. In this iteration, P15, P19 and P20 are conference articles and others are journal articles.
ID Articles Relevant to the Field
P8 [24]
Kleijnen, M., Lee, N., & Wetzels, M. (2009). An exploration of consumer resistance to innovation and its antecedents. Journal of Economic Psychology, 30, 344–357.
P9 [25]
P10 [26]
Calantone, R. J., Chan, K., & Cui, A. S. (2006). Decomposing product innovativeness and its effects on new product success. Journal of Product Innovation Management, 23(5), 408–421.
P11 [27]
Stock, R. M., & Zacharias, N. A. (2013). Two sides of the same coin: How do different dimensions of product program innovativeness affect customer loyalty? Journal of
Product Innovation Management, 30(3), 516-532.
P12 [5]
Heidenreich S., and M. Handrich (2014). What about passive innovation resistance? Investigating adoptionǦrelated behavior from a resistance perspective. Journal of
Product Innovation Management, 32(6).
P13 [28]
Moreau, C. P., D. R. Lehmann & A. B. Markman. (2001). Entrenched knowledge structures and consumer response to new products. Journal of Marketing Research 1: 14– 29.
P14 [6]
Ziamou, P. L. (2002). Commercializing new technologies: Consumers' response to a new interface. Journal of Product Innovation Management 19 (5): 365– 374.
P15 [29]
Füller, J. & Matzler, K. (2007). Virtual product experience and customer participation—A chance for customer-centred, really new products. Technovation, vol. 27, no. 6, pp. 378-387.
P16 [13]
Stremersch, S. & Tellis, G.J. (2002). Strategic Bundling of Products and Prices: A New Synthesis for Marketing, Journal of Marketing, vol. 66, no. 1, pp. 55-72.
P17 [30]
Sarin, S., Sego, T. & Chanvarasuth, N. (2003). Strategic Use of Bundling for Reducing Consumers' Perceived Risk Associated with the Purchase of New High-Tech Products,
Journal of Marketing Theory and Practice, vol. 11, no. 3, pp. 71-83.
P18 [31]
Veryzer, R. W., & B. B. de Mozota. (2005). The impact of userǦoriented design on new product development: An examination of fundamental relationships. Journal of
Product Innovation Management 22 (2): 128– 143.
P19 [32]
Xue, J. (2019). An investigation into the effects of product design on incremental and radical innovations from the perspective of consumer perceptions: Evidence from China, Creativity and Innovation Management, vol. 28, no. 4, pp. 501-518.
P20 [33]
K. Hoffman, K. Wnuk, & D. Callele (2014). On the facets of stakeholder inertia: A literature review", Software Product Management (IWSPM) 2014 IEEE IWSPM 8th
International Workshop on, pp. 31-37.
P21 [34]
Dahl, D.W. & Hoeffler, S. (2004). Visualizing the Self: Exploring the Potential Benefits and Drawbacks for New Product Evaluation, Journal of Product Innovation
Management, vol. 21, no. 4, pp. 259-267.
Iteration 2
After passing the first iteration, we selected 7 articles out of 3280 as the collection of the second iteration. In this iteration we are more focused on the specific approaches implemented in the strategy, such as what the combination of products should be when the strategy is bundled. In this iteration, Only P27 is conference article while others are journal articles.
ID Articles Relevant to the Field
P22 [35]
Cui, A.S. & Wu, F. (2017). The Impact of Customer Involvement on New Product Development: Contingent and Substitutive Effects, The Journal of product innovation
management, vol. 34, no. 1, pp. 60-80.
P23 [36]
Henard, D.H. & Dacin, P.A. (2010). Reputation for Product Innovation: Its Impact on Consumers, Journal of Product Innovation Management, vol. 27, no. 3, pp. 321-335.
P24 [37]
Yunus, E.N. (2018). Leveraging supply chain collaboration in pursuing radical innovation, International Journal of Innovation Science, vol. 10, no. 3, pp. 350-370.
P25 [38]
Ackermann, C., Teichert, T. & Truong, Y. (2018). 'So, what is it? And do I like it?' New product categorisation and the formation of consumer implicit attitude, Journal
of Marketing Management, vol. 34, no. 9-10, pp. 796-818
P26 [39]
Heidenreich, S. & Kraemer, T. (2015). Passive innovation resistance: The curse of innovation? Investigating consequences for innovative consumer behavior, Journal of
Economic Psychology, vol. 51, pp. 134-151.
P27 [40]
Tacer, B., Ruzzier, M. & Nagy, T. (2018). User-driven innovation: scale development and validation, Economic Research-Ekonomska Istraživanja, vol. 31, no. 1, pp. 1472-1487.
P28 [41]
Gruner, R.L., Vomberg, A., Homburg, C. & Lukas, B.A. (2019). Supporting New Product Launches With Social Media Communication and Online Advertising: Sales Volume and Profit Implications, The Journal of product innovation management, vol. 36, no. 2, pp. 172-195.
Table 4.14 Result of Iteration 2
Iteration 3
After the last round of iterations, this article selected 3 articles from 989 articles to form a collection of the third round of iteration. Similar to the second iteration, the selected articles in this iteration detailed the strategies of the previous iterations. The articles selected in this iteration are all journal articles.
ID Articles Relevant to the Field
P29 [42]
P30 [43]
Pappu, R. & Quester, P.G. (2016). How does brand innovativeness affect brand loyalty?, European journal of marketing, vol. 50, no. 1/2, pp. 2-28.
P31 [44]
Heiskanen, E., Hyvönen, K., Niva, M., Pantzar, M., Timonen, P. & Varjonen, J. (2007). User involvement in radical innovation: are consumers conservative?, European
Journal of Innovation Management, vol. 10, no. 4, pp. 489-509.
Table 4.15 Result of Iteration 3
Iteration 4
In this part of iteration, there are 1 article which are related to this research was selected from 482 articles. As we can see, P32 is a journal article.
ID Articles Relevant to the Field
P32 [45]
Heiskanen, E., Hyvönen, K., Niva, M., Pantzar, M., Timonen, P. & Varjonen, J. (2007). User involvement in radical innovation: are consumers conservative?,
European Journal of Innovation Management, vol. 10, no. 4, pp. 489-509.
Table 4.16 Result of Iteration 4
Iteration5
In the final iteration, we review the abstract of 348 articles. Only 2 articles, which is already selected in the previous iteration, are involved. What’s more, other literature has been irrelevant to our study, which means that we cannot continue the snowballing to the next iteration. Therefore, we decided to close the iteration of SLR, with five forward iterations and backward iterations.
Distribution of relevant articles
After 5 snowballing iterations, articles published after 2000 were selected according to the screening strategy. Figure 4.1 shows the distribution according to the publication time of the article. As shown in the figure, the publication year of the selected literature samples covers almost the entire time period, and the number of literature samples published between 2014 and 2020 is the largest proportion, which shows that this literature review is novel and not outdated from time.
Categorization of Research method and Study type
Among the 32 selected articles, 9 articles used 2 research methods. According to the classification of research methods, the results are shown in Figure 4.2. The largest proportion of selected research method is Survey, which is chosen by almost half of the articles. The second proportion of selected research method is literature review, and 8 articles have used this research method. Followed closely by experiment and data analysis, which both used by 5 articles. The smallest proportions are case study, interview and focus group, which have 4 articles, 3 articles and 2 articles separately.
Fig 4.2 Categorization method and study type
Quality assessment based Rigor and Relevance
In order to evaluate the selected articles at a high-quality level, we referred to Martin's paper [55] and chose the rigor and relevance framework. The rigor part is divided into three parts: Context, Study design, and Validity. The relevance part is divided into four parts: User/Subject, Scale, RM (Research method), and Context. After scoring each part, we sum up all score. In order to present the comparison of values more intuitively, the bubble diagram is used. The higher the abscissa score, the stronger the rigor is; The higher the ordinate score is, the higher relevance is. The score table will be shown in Appendix and bubble diagram will be present as following:
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Case Study Survey Literature
Review
Experiment Interview Data Analysis Focus Group
Fig 4.3: Rigor and Relevance scores
The table of all identified strategy from the result of snowballing
ID Identified Strategy
P1 Integrate new products with existing habits to promote adoption.
P3 Analogy; Benefit comparison; Classification; Horizontal cooperation; Induction; Mental simulation; Visualization; Product display; Binding; Quality assurance.
P5 Product bundling
P6 Customer Involvement in Design
P7 Reduce the number of new features added in product iterations P8 Celebrity endorsements and contextual marketing communication
P9 Customer behavior intervention: Situational cues; Add labels; The location where the restriction occurs, etc.
P10 Distribution synergy P11 Using brand loyalty
P12 Assess resistance, CI (customer involvement), benefit comparison P13 Media planning and brand loyalty
P18 UOD (UserǦoriented design) P19 User interface in aesthetic design P21 Self-visualization—advertising
P22 CI (customer involvement) for development process P23 Combine CI (customer involvement) and brand loyalty P24 Customer involvement for II, Positive influence P25 Categorization cue
P26 Categorization cue, using advertisements, assess resistance, product bundling P27 Social media like YouTube or Facebook Online Advertising like website
P28 Customer participation. Integrate product appearance and brand development after receiving comments.
P29 IUI (Important user involvement)
P31 CI (customer involvement) and brand loyalty P32 Advertising and communicate with customer
Table 4.16: Identified strategy from selected articles
It can be seen from this table that some of these strategies are repeated. Owing to these strategies are solved in different directions or are not the only one strategy mentioned in the selected article, we still repeatedly filter into the above table so as not to miss. In order to make the table more to be clear, we have further screened the strategy, and the screening criteria are as follows:
1. Repeated multiple times; 2. It is practical and feasible.
After screening, we organized the strategy into the following table.
The table of selected strategy
To make the strategy more clear and concise, we combined all the strategies and formulated them into the following table:
ID Strategy Article num.
S1 Integrate new products with existing habits P1
S7 Reduce the number of new features added in product iterations
P7
S8 Customer behavior intervention P9
S9 Distribution synergy P10
S10 Brand loyalty P11, P13, P30
S11 Media planning P13, P27
S12 UOD (UserǦoriented design) P18, P19
Table 4.17: Selected strategies from identified strategies
The relationship with NFR (Non-functional requirement)
Requirements engineering (RE) is the process of defining, documenting, and maintaining requirements in the engineering design process [51]. It contains requirement elicitation, requirement analysis, requirement specification and requirement validation. Requirements are classified into business requirement, functional requirement, non-functional requirement, etc. Non-functional requirements (NFR) can be defined as quality attributes (for example, ease of use, transparency, safety) or general system constraints [52]. The 12 strategies sorted out in this article can play a role in the process of requirements engineering and can also provide powerful help for NFR.
Each strategy has helped the development of requirements engineering to varying degrees, and they have helped innovative products improve the overall quality. To better know how these strategies work, we match them with NFR to see what kind of NFR that each strategy can help to improve. Non-functional requirements are often called "quality attributes" of a system. And it marks the difference between whether the product has succeeded or failed [53].
S12 Extensibility
Table 4.18: The relationship with NFR
The specific meaning of strategy
To better understand the meaning of each strategy, the meaning of each decision is given below, and how it helps the development of requirements engineering.
S1: Integrate new products with existing habits
The purpose of integrating new products with existing habits is to enhance compatibility with habits, enabling users to develop new habits and increasing adoption rates. This strategy can be used in the elicitation phase of requirements. The requirement personnel should summarize the existing habits of stakeholders, and develop functions that are highly compatible with these habits as a requirement. This strategy can help to increase usability and extensibility.
S2: Classification
The classification of new products into a certain category can help users better understand the functions of new products, facilitate users to compare the comparative advantages of new products, and reduce the complexity of new products through classification. It can satisfy customers' observability requirements, for example: customers want to learn about this product in a short time. This strategy can help to increase usability and understandability.
S3: Visualization
Visualization in this strategy refers to image visualization, which simulates specific usage patterns through advertising and other methods to help users align new products with existing product usage patterns. This strategy enhances compatibility and visibility. This can satisfy customers' needs of observability and transparency, for example: customers want to understand the appearance and design of this product.
S4: Product Bundling
Product bundling refers to bundling new products with existing complementary products, thus enhancing users' perceived benefits and assessment of new products. Compared with S1, bundling with existing habits, this strategy can help users get familiar with new products, thus reducing the burden brought by habits. This strategy can increase customers' understanding of the product to achieve the goal of interoperability.
Demonstrations and exhibitions of new products help familiarize users with the features of new products, to reducing complexity and giving users the opportunity to try out new products. This can satisfy customers' requirements in the aspect of transparency. For example: customers want to understand how the product's functions are used.
S6: Customer Involvement
The strategy is based on the concept of the primary users is an important source of innovation, research and understand the needs of users, allows users to participate in new product development process, allows the user to the new product has a certain familiarity, help the user to evaluate new product and better understand the function of the new product. This strategy not only strengthened the compatibility and observability, but also reduced complexity. Customer involvement can be used throughout the requirement engineering. Customers can put forward their own requirements, and requirement personnel can get more requirements from communication with customers. Customers can also provide effective suggestions on the priority of requirements. What’s more, this strategy can help to increase the quality and extensibility of the innovation products.
S7: Reduce the number of new features added in product iterations
As the number of new features added increases, the more complex users face, which will reduce product adoption. Satisfaction is highest when the number of new features added is close to the number acceptable to customers. So in some cases, reducing the number of new features added in a product iteration will increase product adoption. Companies can prioritize the new features they want to add, and then add them based on user conditions. This strategy should be used in requirement prioritization, prioritizing all requirements based on important, cost, time, risk and other factors, and selecting a part of them for implementation. In one iteration, some but not all of the high-priority new functions are selected for implementation, which reduces the complexity of the product and improves the usability.
S8: Customer behavior intervention
Consumers usually do not act according to their own consciousness, because the situational prompts in the surrounding environment will automatically cause the habitual behavior of learning. Based on the principle of situational cognition, customer behavior intervention can change consumer habits through situational prompts in the consumer environment. For example, removing or reducing exposure to situational cues that activate stored situated conceptualizations or adding situational cues to prompt other behavioral responses. This strategy can change the habits of customers or reduce the influence of customers from previous habits through behavioral intervention which can increase the usability of products.
Using a common distribution structure between products can maintain the continuity of relationships with customers. Therefore, distribution synergies may increase customer familiarity. Distribution channels are a key means to increase the adoption rate of new products, and are a valuable tool to overcome customer risk thresholds, especially when the products are too innovative. Even after the product is released, existing sales staff can promote product adoption by providing reliable information. Through the introduction of sales staff, customers can feel that their requirements are met, increasing the transparency and usability of products.
S10: Brand loyalty
Loyalty has been defined as “a deeply held commitment to rebuy or re-patronize a preferred product/service consistently in the future” (Oliver, 1999 p30). Brand loyalty can increase product adoption and can be consolidated by measures such as increasing communication with customers. Establishing brand loyalty can magnify the matching degree between customer requirements and product functions and reduce the influence of customer requirements that are not met by the product. In addition, it also takes the emotional factors of the product into account.
S11: Media planning
Media planning includes social media communication and online advertising. In these ways, customers' familiarity with the product can be increased and product advantages can be enlarged. This strategy can improve the understandability of the product. As the degree of understanding deepens, the perceived value of the product will increase. In addition, as the functional operation of the product is easy to see, the transparency of the product is improved.
S12: UOD (UserǦoriented design)
5
D
ISCUSSION
5.1
RQ1: What is the impact of different types of innovation on user
experience and satisfaction in terms of user inertia?
In previous studies, many authors have studied how RI and II affect product adoption and how barriers to RI and II differ. However, strategies that can increase product adoption/overcome obstacles may not necessarily solve stakeholder inertia, and existing research does not have a deeper understanding of the attributes of strategies. Since there is no relevant research on the influence of RI and II on user inertia, RQ1 aims to study the different effects of basic and incremental innovations on user experience and satisfaction in iOS system updates. We proposed H1: RI products’ HPIR is higher than II products, leading to a decline in user experience and satisfaction, and tends to reject innovation. Through a survey of 60 people, the results of RQ1 were obtained: the HPIR of RI products is higher than II products, which will lead to a reduced user experience, but this does not affect user satisfaction. For user experience, the same as H1, the user experience of RI products is lower than that of II products due to user inertia. For satisfaction, unlike H1, there is no significant difference in user satisfaction between RI products and II products.
Radical innovation is an innovation with a high degree of novelty. It breaks through previous innovations and is the result of non-obvious paths or ideas [54]. When users use new radical functionalities (RI), such as a full screen, compared with the previous mobile phone screen, the users will feel that the change is great, resulting in a large degree of habit change, so the influence of user inertia is more obvious, and the user may have more resistance, which reduces the user experience. But when users use RI products for a period of time, after adapting to RI, the influence of user inertia gradually decreases, so there is no obvious impact on satisfaction. In contrast, incremental innovation is a low-innovative continuous innovation. When the user uses II, the degree of change is small, and the influence of user inertia is poor. As the survey data shows, a lot of users consider that II's influence on habits is neutral.
5.2
RQ2: What requirements engineering techniques should be used to
create the guideline that helps to handle potential user inertia?
The S-A model
As can be seen from the results of SLR, we have sorted out 12 strategies that frequently appear in the literature to deal with the adoption of innovative products. In the part of related work, we mentioned that the adoption from user inertia can basically be divided into 6 attributes: Complexity, Trial-ability, Relative advantage, Familiarity, Observability and compatibility.
- Complexity refers to the ease of use of a new product.
- Trial-ability refers to the number of opportunities to try the product before buying it, such as samples or uses the exhibition machine.
- Relative advantage refers to the user can perceive the benefits of the product and adopt it after comparing with other products.
- Familiarity refers to how familiar the user is with the innovative product.
Fig 5.1: S-A model
It can be clearly seen from the figure that there are not many attributes corresponding to each strategy, which means that these single strategies cannot comprehensively solve the problem of innovative product adoption caused by user inertia. Thus, confirming the establishment of H2.
The GAS model
At the same time, after we have a one-to-one correspondence between user inertia, attributes and strategies (as shown in the figure below), we can see more clearly that S5 (product display) corresponds to four attributes, which means that this strategy can be used to a great extent to effectively alleviate the problem of user inertia. As for S7 (Reduce the number of new features added in product iterations) and S8 (Customer behavior intervention), they can only improve user inertia based on one attribute due to little effect or difficulty. On this basis, since there is no strategy that can comprehensively solve the problem of adopting innovative products due to user inertia, we recommend combining multiple strategies to correspond to multiple attributes.
Fig 5.2: GAS model (Goal-Attribute-Strategy)
The process of combination strategy
1. By involving the main users in the development process, effectively extract the user's intrinsic requirements and then obtain an innovative product that meets the user's needs.
2. Use image visualization such as advertising to promote the innovative product. 3. Display the sample of the innovative product,
4. Use the product bundling strategy to make the product more familiar and understood by the user. The specific process is shown in the figure below:
Fig 5.3: The process of combining the strategies
