THE INFLUENCE OF ADOPTERS’ PERCEPTIONS IN ADOPTION
INTENTION AND ADOPTION BEHAVIOR – A CASE STUDY OF A
BIOMEDICAL PRODUCT
Patrícia Tavares de Abreu
Dissertation Plan
Master in Economics and Management of Innovation
Supervised by
Prof. Doctor José Coelho Rodrigues
ACKNOWLEDGEMENTS
The end of this academic chapter would not have been possible without the support of certain people. Firstly, my deep thanks to Prof. Doctor José Coelho Rodrigues. The one who guided this work for a year, who believed and trusted me from the beginning. Thank you for being always available, for your support and all the motivation.
Secondly, I would like to thank all of those, that I could not mention, but who contributed to the conclusion of this dissertation. Without your information and cooperation none of this would have been possible.
The conclusion of this dissertation would not have been possible, of course, without the support of my family and friends, in particular from my parents, my biggest support in life. They always gave me the strength to continue and always made me believe that I would succeed. Thank you from the bottom of my heart. I would also like to acknowledge my sister for being my daily inspiration, Raquel for all the moments of joy and hours working together, and Francisco for always being by my side, no matter what. I can only add that I have the best friends in the world, which have always understood my absence during some important moments. You are truly the best.
Abstract
Similar to the biotechonology industry, the biomedicine industry has several challenges. To stand out for the competitors, every company in this industry needs to know how to get to the right market. According to the literature, individuals and organizations choose to adopt or reject an innovative product and will later confirm that decision or not. Such decision is of utmost importance to the success of the innovative products and, therefore, of the company that provides such products. The aim of this study is to verify and understand how the perceptions formed about a biomedical product can influence its adoption intention and behavior and, hereafter, influence potential adopters, through a multiple case study The results provide a clear definition of the adoption process of the specific product under study in this research, combining two existing adoption theories – the Diffusion of Innovations Theory (DOI) and the Technology of Acceptance Model (TAM) –, to help understand how to get to more customers or, at least, how to get to the market with a more structured way of looking to what influences potential adopters’ decisions.
JEL-Code: 030
Resumo
Tal como a indústria da biotecnologia, a indústria da biomedicina apresenta vários desafios. Para se destacar dos concorrentes, todas as empresas deste setor precisam saber como chegar ao mercado mais apropriado. De acordo com a literatura, indivíduos e organizações podem optar por adotar ou rejeitar um produto inovador, confirmando posteriormente essa decisão, ou não. Essa decisão é de extrema importância para o sucesso de produtos inovadores, assim como para a empresa que os desenvolve. O objetivo deste estudo consiste em verificar e entender como é que as percepções desenvolvidas acerca de um produto biomédico podem influenciar a intenção e o comportamento de adoção e, posteriormente, compreender como pode influenciar potenciais adotantes. Este objetivos é atingido utilizando um estudo de caso míltiplo. Os resultados mostram uma clara definição do processo de adoção deste produto específico, combinando duas teorias de adoção existentes - a Teoria da Difusão de Inovações (DOI) e o Modelo da Tecnologia de Aceitação (TAM) -, de forma a ajudar as empresas a entender como conquistar mais clientes ou, pelo menos, como chegar ao mercado com uma visão mais estruturada sobre aquilo poderá que influenciar as decisões de possíveis adotantes.
Código JEL: 030
Index of Contents
ACKNOWLEDGEMENTS ... ii
Abstract ... iii
Resumo ... iv
2. Literature review ... 3
2.1. Defining key concepts ... 3
2.1.1. Adoption and rejection of a technology ... 3
2.1.2. Innovation-decision process ... 3
2.1.3. Characteristics of the innovation ... 5
2.1.4. Characteristics of the adopters ... 8
2.2. Adoption of new technologies: models and frameworks ... 10
2.2.1. Proposed framework ... 12
3. Method ... 14
3.1. The method of analysis ... 14
3.2. Data collection ... 15
3.3. The unit of analysis ... 16
3.4. Data analysis ... 17
4. Analysis and Discussion of Results ... 19
4.1. The purpose for adopting Product P ... 19
4.2. The process of adoption of Product P ... 22
4.2.1. Knowledge ... 22
4.2.2. Persuasion ... 25
4.2.3. Decision ... 25
4.2.4.1. Characteristics of Device D and Software S ... 26
4.2.4.2. The relationship with Company C ... 30
4.2.4.3. The competing products of Product P ... 30
4.2.5. Confirmation ... 31
4.2.5.1. Confirmation of the adoption of Product P ... 32
4.2.5.2. Stop using Product P or just using it occasionally ... 32
4.2.5.3. The influence of perceptions in potential adopters ... 34
5. Conclusion, limitations and future research ... 35
5.1. Conclusion ... 35
5.2. Limitations ... 38
5.3. Future Research ... 38
References ... 40
Index of Figures Figure 1. Proposed Framework ... 12
Figure 2. The adoption of Product P ... 22
Index f Tables Table 1. The perceived characteristics of innovations – Rogers (2003) ... 5
Table 2. The effect of perceived characteristics of innovations on intention and behavior factors - Arts et al. (2011) ... 6
Table 3. The eight innovation characteristics studied by Kapoor et al. (2014) ... 7
Table 4. Adoption theories and their main features ... 10
Table 5. Unit of analysis characterization ... 16
Table 6. Purpose of adoption ... 19
Table 7. The main characteristics of Product P before its use ... 23
Table 8. The main characteristics of Device D after its use ... 27
1. Introduction
Individuals or organizations can choose to adopt or reject an innovation (Rogers, 2003). Each potential adopter adopts the innovation at different times when comparing to other potential adopters of the same community or social system, due to their different degrees of innovativeness, i.e., “the degree to which an individual (or other unit of adoption) is relatively earlier in adopting new ideas than other members of a system” (Rogers, 2003, p. 267). Regardless of the degree of innovativeness, the innovation-decision process comprehends five stages: knowledge, persuasion, decision, implementation and, confirmation (Rogers, 2003).
Researchers have been developing frameworks and models to systematize the process of adoption of new technologies and the factors that influence users’ choice (Taherdoost, 2018). There is an extensive list of theories and frameworks such as: Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975), Theory of Planned Behavior (TPB) (Fishbein & Ajzen, 1975), Technology of Acceptance Model (TAM) (Davis, 1985), Technology-Organization-Environment (TOE) Framework (Tornatzky & Fleischer, 1990), Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh, G Morris, B Davis, & Davis, 2003) and Diffusion of Innovations (DOI) Theory (Rogers, 2003). Furthermore, building on these theories, some researchers add or remove variables, according to their research environment (Barrane, Karuranga, & Poulin, 2018).
van Oorschot, Hofman, and Halman (2018) after their bibliometric review about innovation adoption encouraged “future studies to investigate the adoption and diffusion mechanisms related to specific innovations across different contexts” (p. 16). Therefore, the aim of this dissertation will focus on the adoption process of a specific biomedical technology that is being developed and commercialized by a Portuguese company and applied in different contexts mainly among research teams in universities, research institutes, and companies around the world. The product under study is a biomedical product that includes research kits, wearables, sensors, and a software.
Adoption is a very broad topic of research and in this work a new approach, combining DOI Theory with TAM was created to “examine how individuals could have different adoption perceptions and behaviors based on their own technology readiness” (Min, So, & Jeong, 2018, p. 780). Such as the biotechnology industry that is recognized for having many challenges related to adoption of technologies, due to its “complexity, limited understanding of utility, high costs, and other factors” (Shimasaki, 2014, p. 241), the biomedical industry also faces similar challenges. In biomedical technologies, one of the most important modules is included in the growing market of wearables (Athavale & Krishnan, 2017). According to Athavale and Krishnan (2017), the growing market of wearables is divided by niches and human body areas, and play an important role in disease prevention and in healthcare systems in general across the globe. For this reason, a deep study about the adoption of this type of technology, which is included in a challenging industry, is fundamental. We expect to contribute for companies to understand how to get to more customers or, at least, how to get to the market with a more structured way of looking at potential adopters, focusing on better understanding how adopters’ perceptions influence adoption intention and behavior of such a technology.
A qualitative case study was the method used in this dissertation. Data was collected from adopters of the product, through semi-structured interviews and also through other documentation. Those were analyzed in order to develop a framework that can be used by any organization that develops and sells similar products.
This dissertation is divided in five chapters. In the following chapter, a literature review about innovation adoption concepts is presented and key concepts such as innovation-decision process, characteristics of innovations and adopters are defined. The third chapter includes the method of analysis selected and how data was collected and analyzed. In forth chapter, the analysis and discussion of results are presented, following the last chapter with the conclusions and limitations of this dissertation and directions for future research.
2. Literature review
The following literature review focuses, firstly, on defining the key concepts for the adoption process: the difference between adoption and rejection of a technology is introduced, and then the three factors that Rogers (2003) identifies as determinant to understand this process, i.e., the innovation-decision process, the characteristics of the innovation and, the characteristics of the adopters, are presented. Then, the most relevant models and frameworks that address adoption of innovations are briefly presented. Finally, a a framework proposed to study in depth the adoption of a biomedical product, based on the literature reviewed in the previous sub-sections, is described in detail. The topics of this review are essential to explain how the adopters’ perception influence the adoption intention and adoption behavior of the biomedical product under study.
2.1. Defining key concepts
2.1.1. Adoption and rejection of a technology
When a technology is developed and diffused among potential adopters, two different adoption decisions can be made: adopt or the reject it. According to Rogers (2003, p. 177), the adoption is the “decision to make full use of an innovation as the best course of action available” and the rejection is the “decision not to adopt an innovation” and arises at the decision stage of the innovation-decision process.
The following sub-sections will describe the three main factors of the DOI Theory suggested by Rogers (2003) that explain the adoption of innovations: the innovation-decision process, characteristics of the innovation unders adoption, and characteristics of the adopter.
2.1.2. Innovation-decision process
The innovation-decision process is described as “the process through which an individual (or other decision-making unit) passes from first knowledge of an innovation, to the formation of an attitude toward the innovation, to a decision to adopt or reject, to implementation and use of the new idea, and to confirmation of this decision” (Rogers, 2003, p. 20). Therefore, this process
consists of five stages - knowledge, persuasion, decision, implementation, and confirmation (Rogers, 2003).
During knowledge stage, the decision-making unit (potential adopter) learns about the innovation and its functions, but it is only at the persuasion stage that the decision-making unit forms an opinion about the innovation, either positive or negative (Rogers, 2003). In this latter stage, the decision-making unit creates perceptions about the innovation, that will influence the intention of acquiring it or not. In the decision stage, the adoption or rejection of the innovation is decided (Rogers, 2003) and, if adopted, its benefits will only emerge if the outcomes of the implementation stage are positive (Linton, 2002). According to Linton (2002, p. 66), “implementation involves all activities that occur between making an adoption commitment and the time that an innovation either becomes part of the organizational routine, ceases to be new, or is abandoned”. Finally, the last stage of this process is the confirmation. During this period, the decision-making unit can be surprised with some unexpected information about the innovation that triggers a reevaluation of the adoption decision made earlier (Rogers, 2003). Intrinsic to the innovation-decision process are two important factors: adoption intention and adoption behavior. The adoption intention is related to the perceptions that the adopter has about the innovation before purchasing it, when there is just a desire to obtain it (Arts, Frambach, & Bijmolt, 2011) The latter authors argued that the majority of adoption behavior studies analyzed the post innovation purchase, except Rogers (2003) who assumes the adoption behavior definition as the moment of the innovation purchase. In this dissertation, we analyze the adoption behavior as the post innovation purchase moment, where adopters’ perceptions are included, as most studies determine (Arts et al., 2011).
The decision of adopting an innovation requires to acquire it, whether it is purchased or not. For this reason, adoption intention occurs during the first three stages of the innovation-decision process - knowledge, persuasion and decision – and adoption behavior during the implementation stage. Depending on the outcomes of this behavior, the adopter will confirm or not the adoption.
2.1.3. Characteristics of the innovation
To better understand the adoption of an innovation, it is important to examine the characteristics of the innovation and their existent relation with the adoption decision (Kapoor, Dwivedi, & Williams, 2014). Innovation characteristics allow adopters to make an evaluation of the innovation (Arts et al., 2011), to develop a perception about it, and are one of the factors that can explain adoption (Rogers, 2003). According to Rogers (2003) characteristics of the innovation are also called perceived attributes of innovations and include: relative advantage, compatibility, complexity, trialability, and observability as Table 1 shows.
Table 1. The perceived characteristics of innovations – Rogers (2003)
Perceived characteristics of innovations Definition
Relative Advantage “degree to which an innovation is perceived as
better than the idea it supersedes” (p. 15).
Compatibility “degree to which an innovation is perceived as
being consistent with the existing values, past experiences, and needs of potential adopters” (p. 15).
Complexity “degree to which an innovation is perceived as
difficult to understand and use” (p. 16).
Trialability “degree to which an innovation may be
experimented with on a limited basis” (p. 16).
Observability “degree to which the results of an innovation
are visible to others” (p. 16).
Tornatzky and Klein (1982) focused their work on studying innovation characteristics. Their review and meta-analysis research allowed to identify thirty innovation characteristics. However, only ten of them were deeply scrutinized by the authors, as those were the characteristics that emerged more often in the literature. Apart from the perceived characteristics of innovations identified by Rogers (2003), Tornatzky and Klein (1982) also studied in detail five additional characteristics: cost, communicability, divisibility, profitability, and social approval.
In addition, Arts et al. (2011) studied how the characteristics of innovations identified by Rogers (2003) affected intention and behavior adoption factors introduced before. Table 2 illustrates their main findings.
Table 2. The effect of perceived characteristics of innovations on intention and behavior factors - Arts et al. (2011)
Perceived characteristics of innovations
Findings Authors’ discussion of results
Relative Advantage
Positively affects both, but it has a stronger effect on behavior
Relative advantage can be seen as a guarantee of quality made by experience which can be evaluated in more detail after the innovation is used.
Compatibility Positively
affects both, but it has a stronger effect on intention
The authors identified compatibility as being a benefit of innovation, so it is expected to be a characteristic that will affect the intention to adopt.
Complexity Positive effect on intention and negative effect on behavior
Complexity positively affects intention because “consumers underestimate the (potentially negative) role of complexity” (p. 142) while developping their intention to adopt.
Trialability Negative effect
on behavior The authors argue that an explanation for this negative effect could be related to the study of Karahanna, Straub, and Chervany (1999) that stands out that when the innovation starts being used, the relevance of trialability disapears.
Observability Negative effect
on behavior Observability shows a negative effect on behavior “because of personal experience with the innovation” (p. 141).
Despite both studies having different aims and being done at different times, there is a similar pattern in their results. Despite Tornatzky and Klein (1982) did not distinguish between adoption intention and adoption behavior, they argued that relative advantage and compatibility were positively related to adoption, a conclusion also achieved by Arts et al. (2011). In addition, Tornatzky and Klein (1982) mentioned that complexity was negatively related to adoption and Arts et al. (2011) cited a negative relation between complexity and adoption behavior.
In addition, Kapoor, Dwivedi, and Williams (2014) evaluated how twenty-eight characteristics influenced the adoption process, using a meta-analysis method. Firstly, they did a systematic
review of specific innovation characteristics and their evolution over the last fifteen years. They identified that innovation characteristics apart from the ones identified by Rogers (2003) needed more attention. For this reason, they studied the twenty-five characteristics identified by Tornatzky and Klein (1982) and the three identified by Moore and Benbasat (1991) – image, voluntariness and, result demonstrability. A total of twenty-eight innovation characteristics were analyzed. From the initial twenty-eight innovation characteristics, the authors chose to focus on those that appeared in more than ten of the studies they reviewed. Only eight of them met this requirement: ease of operation, image, cost, riskiness, visibility, voluntariness, result demonstrability, and social approval. Furthermore, they studied the importance of those eight characteristics on innovation adoption, which are characterized in Table 3.
Table 3. The eight innovation characteristics studied by Kapoor et al. (2014)
Innovation
characteristics Description
Ease of operation If an innovation has this characteristic it means that it is easy to be used.
Image As “image is a social element” (p. 333), a change in the social image (to improve it) is positive from the user’s point of view, and consequently for the adoption of the innovation.
Cost The authors cited that the lower the cost of the innovation, the
higher the odds to adopt it.
Riskiness The authors concluded that when the innovation is not much risky, it is more likely to be adopted.
Visibility The authors compared visibility with observability, i.e., how visible is the innovation to others. The authors found out that visibility has a positive effect in adoption, among some studies.
Voluntariness An innovation can be easily adopted if it is influenced by the free will.
Result
Demonstrability The authors concluded that the more visible and catching the innovation is, the higher are the odds of adopting it.
Social Approval Social approval is associated to the status of the adopter in a reference group, which will positively contribute to the decision of adoption.
Kapoor et al. (2014) also analyzed the antecedents and descendants of the previously mentioned characteristics. Considering the characteristics identified by Rogers (2003), the authors only identified complexity as an antecedent of ease of operation.
2.1.4. Characteristics of the adopters
For organizations that provide innovations, it is imperative to know their adopters’ characteristics and respective intentions to adop the innovation (Arts et al., 2011).
The target market of a company, ideally, should include the population of individuals who will easily purchase and use its products (Shimasaki, 2014). According to Arts et al. (2011), the target market should aggregate “consumers showing high involvement in the product category related to the innovation” (p. 142) so after its adoption, they can disseminate information about the new product or service to others. This can be achieved through segmentation by reducing that population into a homogenous and smaller group, enabling the company to target the initial adopters of a product (Shimasaki, 2014).
The results of the meta-analysis of Arts et al. (2011) also concluded that sociodemographic variables, such as age, education, and income, did not have any particular effect on adoption intention or behavior. However, the authors found that psychographic variables (such as innovativeness, opinion leadership, media proneness, and involvement) are “powerful drivers of innovation adoption, with respect to both intention and behavior” (p. 135).
Depending on their innovativeness, adopters can be divided into five categories: innovators, early adopters, early majority, late majority and laggards (Rogers, 2003).
According to Jeong, Kim, Park, and Choi (2017) organizations must know innovative consumers’ (or innovators) needs so the diffusion of a new product can be successful. The main goal of the diffusion process is to spread the word about any innovation within the members of a social system (Rogers, 2003). The author defines the social system as the group of members or units, such as individuals or organizations, focused on achieving a final goal, together.
There are two types of consumers especially important in the diffusion process: the innovators and the early adopters. They have particular characteristics that stimulate them to be the first to adopt a new product or service before any other member of the social system (Dedehayir, Ortt, Riverola, & Miralles, 2017). Although they only represent less than 20% of the population of
potential adopters, they play a vital role in the diffusion process, and their characteristics can differ according to product categories (Dedehayir et al., 2017).
Rogers (2003) defines the innovator as the one that “plays a gatekeeping role in the flow of new ideas into a system” (p. 283) since innovation is outside that social system and the innovator is the first to adopt, i.e., to include it into the system. On the other hand, the author describes early adopters as those whose advice about the innovation is the most reliable and recognized among the rest of the social system. In addition, Bernstein and Singh (2008) assign informal, creative and entrepreneurial characteristics to the innovators. Those same authors also argue that despite early adopters have analogous characteristics to the previous group, they have “the insight or the vision to match the innovative scientific findings with the business goals of the organization” (pp. 375-376). The third group of adopters is the early majority. The individuals or organizations included in this group need a longer time to adopt an innovation, when comparing with the innovators and the early adopters, and their location in the diffusion process is as an important connection to the rest of the social sytem (Rogers, 2003). Such as the early majority, the late majority is a considerable group of individuals or organizations. They are more sketical than the previous group and the pressure of peers is an important factor to motivate individuals or organizations to adopt an innovation (Rogers, 2003). The last group, the laggards, is the most isolated in the system and more resistant to adopt innovations (Rogers, 2003).
2.2. Adoption of new technologies: models and frameworks
Several authors studied the adoption of innovations, contributing for a wide list of theoretical models and frameworks that explain the adoption of innovations and the factors that influence potential users to adopt them (Taherdoost, 2018).
Table 4 summarizes some of the most used adoption theories and their main features. Table 4. Adoption theories and their main features
Theories and Authors Main features
Theory of Reasoned Action (TRA) developed by Fishbein and Ajzen (1975)
In this theory, human behavior is related to behavioral intentions (Angeles, 2008). The behavior intention is explained and predicted through users’ attitudes (positive or negative) and subjective norms (Angeles, 2008; Taylor & Todd, 1995).
Theory of Planned Behavior (TPB) developed by Ajzen (1985)
This model is an extension of TRA, where a new variable was added - the perceived behavioral control – to explain the behavior intention, i.e., user’s behavior (Taherdoost, 2018; Taylor & Todd, 1995). This variable arose “to account for situations where an individual has less than complete control over the behavior” (Taylor & Todd, 1995, p. 139).
Technology of Acceptance Model
(TAM) developed by Davis (1985) In TAM, the attitude toward the use of technology is explained in function of two factors: perceived usefulness and perceived ease of use (Davis, 1985). Moreover, despite TAM being an evolution of TRA, subjective norms and behavior intention components were not included (Davis, 1985).
Technology-Organization-Environment (TOE) Framework developed by Tornatzky and Fleischer (1990)
TOE framework explains the innovation adoption taking into account three sets of factors that influence it positively or negatively: technological, organizational and environmental factors (Cao, Jones, & Sheng, 2014).
Table 4. Adoption theories and their main features (continuation)
Theories and Authors Main features
Unified Theory of
Acceptance and Use of
Technology (UTAUT)
developed by Venkatesh et al. (2003)
Venkatesh et al. (2003) unified the theory and research present in eight models “of the determinants of intention and usage of information technology” (p. 467) and discovered that:
• Performance expectancy, effort expectancy, and social influence were directly associated with the intention to use;
• Intention and facilitating conditions were directly associated with user behavior.
Diffusion of Innovations (DOI) Theory developed by Rogers (2003)
There are three major factors that explain the adoption at both individual and organizational levels (Rogers, 2003):
• The characteristics of the adopters, who are divided into five categories: innovators, early adopters, early majority, late majority, and laggards;
• The innovation characteristics: relative advantage, compatibility, complexity, trialability, and observability;
• The innovation-decision process, divided into five steps: knowledge, persuasion, decision, implementation, and confirmation
Some of the previous frameworks are applied in more generic contexts of research, such as the Technology-Organization-Environment (TOE) Framework and the Diffusion of Innovations (DOI) Theory. Others, such as the Technology of Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) are most commonly used to study the adoption of information technologies.
Many researchers use the traditional frameworks as they are, others combine them (Taherdoost, 2018), and others add or remove variables from those frameworks (Barrane et al., 2018). The following sub-section presents a framework that was created by integrating two traditional adoption’s models, in order to contribute to the proposed aim of the present research.
2.2.1. Proposed framework
Two of the previously presented theories were integrated - the Diffusion of (DOI) Theory and the Technology Acceptance Model (TAM) – and a framework (Figure 1) was built to guide this work. Such integration was crucial to understand the influence of adopters’ perceptions in the adoption intention and the adoption behavior of the product under research. According to Kapoor et al. (2014), most of the adoption studies mainly focus on the decision of adoption itself, and not in the implementation. Since we wanted to go further and investigate how the confirmation of the adoption could influence other potential adopters, this framework was firstly divided in the five innovation-decision process stages: knowledge, persuasion, decision, implementation, confirmation. Then, these stages were grouped according to the two factors identified by Arts et al. (2011): the adoption intention and the adoption behavior. Kapoor et al. (2014) mentioned that while studying innovation adoption, it is crucial to study both adoption decision and implementation, so it is a way to understand more deeply the adoption behavior. Adoption intention included the knowledge, persuasion and decision stages and adoption
behavior included the implementation stage. The confirmation stage represented the outcomes of this behavior.
Despite TAM is not focused in confirming the decision of adoption as is DOI Theory, its integration with DOI Theory allows to better understand the stage that precedes the decision to adopt or reject an innovation: the persuasion stage. According to Davis (1985), the design features of the technology determine the perceived ease of use and the perceived usefulness of it. The perceived ease of use is “the degree to which an individual believes that using a particular system would be free of physical and mental effort” (Davis, 1985, p. 26) and the perceived usefulness is “the degree to which an individual believes that using a particular system would be free of physical and mental effort” (Davis, 1985, p. 26).
With the combination of these two theories, we expect to comprehend how adoption confirmation influences other potential adopters, where there might exist some kind of feedback that will emerge from the confirmation stage and, consequently, influence the factors identified in TAM, i.e., one of the design features mentioned by Davis (1985).
3. Method
For the purpose of this research, a case study was the method used to study the adoption of a biomedical product in different contexts. This product is being developed and commercialized by a Portuguese company. It is one of the three that the company provides, and includes: research kits, wearables, sensors and a specific software that can analyze the vital signs of an individual.
It was decided to keep the company’s name and everything that could lead to its identification anonymous. For this reason, and henceforward, the following designations will be used:
• Company under research - Company C;
• Biomedical product under research - Product P;
• Product P (device) - Device D - It includes all its features, e.g. sensor; whenever necessary, the sensor will be mentioned;
• Product P (software) - Software S; • Main competitor - Competitor N.
In addition:
• The adopters of general technological products – Adopters; • The adopters of Product P – Adopters, interviewees, contacts;
• General technological products - Product, technology or innovation - when not clearly identified.
3.1. The method of analysis
According to Yin (2003), a case study is a linear-iterative method of research that is divided into six stages: plan, design, prepare, collect, analyze and share. The author defined case study as “an empirical inquiry that investigates a contemporary phenomenon in depth and within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident” (p. 18). In addition, when the researcher looks to answer questions of contemporary events in the form of “how” or “why”, without having any control over them, a case study should be the method preferred, either through interviews or direct observation (Yin, 2003).
Moreover, this method of research can allow the researchers to evaluate “multiple variables and their interaction” (Häggman, 2009, p. 393), and study contemporary phenomena when it is not suitable or possible to use quantitative research methodologies (Cao et al., 2014).
There are two different options of designing a case study - applying a single case study or multiple case studies - which “can be unitary or multiple units of analysis” (Yin, 2003, p. 46). Since the technology under study is included in different context scenarios (van Oorschot et al., 2018), a multiple case study, with multiple unit of analysis (adoption of the technology by an organization), was the design chosen.
To select the cases it was used a theoretical sampling. According to Eisenhardt (1989), theoretical sampling is used to select cases that can “replicate or extend theory by filling conceptual categories” (p. 533). In addition, once the cases selected were fairly similar, it was possible to do a cross-case analysis. This type of analysis allowed us to look into the data in divergent ways (Eisenhardt, 1989) and led us to build a stronger and more complete descriptive analysis. 3.2. Data collection
The data was mainly collected through interviews with different individual units, adopters of Product P. In addition, some data was collected from an article that one interviewee shared. Company C provided a total of fourteen contacts, which were all contacted via email. From those contacts, only eight were available to contribute for this research. At the end of each of first eight interviews, it was asked if it was possible to provide further references that were using Product P. Only five provided further contacts, a total of nine contacts that were also contacted via email. From those nine contacts, only five were available to contribute. In total, thirteen interviews were performed, with a success rate of 56.52% considering all the contacts made.
3.3. The unit of analysis
The first analysis performed was the characterization of the units of analysis, represented in the Table 5.
Table 5. Unit of analysis characterization
Interview ID Nº of
interviews Interview duration
time (minutes)
Type of
organization Country Adoption Year of Nº of team members
using Product P I1 1 17.26 Company USA 2014/2015 3 I2 1 24.14 University Portugal Norway 2000 (pilot project) - I3 1 10.51 University Norway - - I4 1 38.01 University Portugal 2011 - I5 1 31.44 University Portugal 2007 - I6 1 23.18 University UK 2013/2014 - I7 1 18.22 University UK - 3 to 4 I8 1 23.50 University USA 2013 6 to 10 I9 1 14.32 University USA 2013 6 to 10 I10 (participation of 3 people) 1 46.42 Company Portugal - -
I11 1 17.10 University Portugal - -
I12 1 22.59 University Denmark 2015 -
I13 1 34.11 University Norway 2009/2010 -
Average 23.22
Interviews were identified with numbers to guarantee interviewees anonymity. All of them belong to research teams in two different types of organizations - universities and companies – and adopted Product P.
The interviewees were mainly the decision-makers or the individuals that took part, directly or indirectly in the decision-making process of each research team.
3.4. Data analysis
The data analysis followed four stages: 1. Interviewing process 2. Construction of a database
3. Identification of emergent categories 4. Cross-case analysis
After contacting the first fourteen contacts that Company C provided, a script for the interview was elaborated. It was developed focusing on the research objective and in the characteristics of Product P, which were available on the website of Company C. The script consisted mainly of open questions and, after performing the first interviews, was slightly adapted. At the same time that the interviewing process started, some of the questions were rewritten or removed based on the information received on previous interviews, to improve the following ones. During some interviews, some new questions emerged, once we noticed that it was possible to collect some information than initially expected, which would be crucial for this research.
The interviewees allowed audio recording of their interviews, which were carefully transcribed. After transcribing and briefly analyze each interview, we decided to build an Excel database with all the questions performed, and their respective answers.
After a more careful analysis, some categories emerged, and that data was organized by the type of information retrieved. Beyond the initial information of the first Excel database, new matrices were built with the information that we found out to be more relevant to study the adoption intention and behavior of Product P, namely:
1. Product P, which includes both Device D and Software S perceptions, before and after adopting it;
2. Competitors’ information;
3. Relationship between interviewees and Company C and other information that could influence it.
Right before beginning to study the relation between the previous different categories, some were divided and/or updated to allow a better descriptive analysis of the adoption intention and behavior of Product P. Thereafter, the cross-case analysis was performed. The following chapter “4. Analysis and Discussion of Results” presents the results of such analysis.
4. Analysis and Discussion of Results
4.1. The purpose for adopting Product PProduct P is a biomedical product and is composed by Device D and Software S. Device D is a wireless device that can collect different biosignals. Due to its diversity of sensors, it can be applied in different research contexts, such as the interviewees are using in sports science, ergonomics and human activity recognition, among others. Software S is a free open source platform that collects, and analyses data received from those sensors.
The thirteen interviewees adopted Product P to measure different types of signals crucial for their research. Table 6 shows the purpose of adoption of Product P for each interviewee and the main sensors acquired to meet their requirements.
Table 6. Purpose of adoption
Interviewee Adoption purpose Research
Context Type of Sensor(s) used
I1 To build and troubleshoot a custom sensor to study human state assessment, in particular to measure the workload in realistic environments.
Human Activity
Recognition fNIRS
I2 To be used by researchers and students in both clinical and sports fields of research for physiotherapy and swimming.
Biomechanics Electromyography
I3 To complete a Ph.D., with the particular purpose of studying muscle activation, mainly during swimming and cross-country ski.
Sports Science Electromyography
I4 To complete a Ph.D., with the particular purpose of collecting data that could detect injuries and muscle activity in golf.
Biomechanics Electromyography
I5 To complete a Ph.D., with the particular purpose of studying the neuromuscular activity in swimmers - the biomechanics of swimming.
Table 6. Purpose of adoption (continuation)
Interviewee Adoption purpose Research Context
Type of Sensor(s) used
I6 To study the emotional development of infants and young children.
Psychophysiology Electroencephalography Electrocardiogram
I7 To expand the signals
record possibilities to study pain perception in the real world and its brain responses.
Human Activity
Recognition Electrocardiogram Electrodermal activity Electroencephalography
I8 To measure people's
physiology in highly emotional activities. such as paranormal activities. Psychophysiology Electrocardiogram Electrodermal activity RFID I9 To study people's physiology in highly emotional activities, such as inside a haunted house.
Psychophysiology Electrocardiogram Electrodermal activity RFID
I10 To use in ergonomic
studies which could detect where to intervene, to provide a better quality of work.
Ergonomics Electromyography
I11 To study surface
electromyography. Sports Science Electromyography
I12 To complete a Ph.D.,
with the particular purpose of studying varied daily human activities through data collected from a knee bandage.
Human Activity
Recognition Accelerometer Electromyography
I13 To complete a Master
thesis related to cross country ski.
Sports Science Accelerometer
Despite that Company C offers other sensors to record different types of signals, the sensors were acquired for different purposes and applied in different research contexts, mainly to study the electrical activity of skeletal muscles - electromyography.
The remain sensors presented in the Table 6 are described below:
• Accelerometer: it can measure motion, acceleration and vibration, from another device. • Electrocardiogram: measures the heart rate.
• Electrodermal activity: measure the skin conductance, i.e., the electrical activity of skin. • Electroencephalography: measures the brain electrical activity (Nunez, 2006).
• fNIRS: “a neuroimaging technology for mapping the functioning human cortex” (Ferrari & Quaresima, 2012, p. 922).
• RFID: used in a lot of different contexts, this sensor can transmit data trough radio waves, using a reader and an antenna.
4.2. The process of adoption of Product P
Figure 2 shows the adoption process of Product P, based on the framework previously defined in 2.2.1. Proposed framework, resulting from the analysis of the results of this study.
According to Rogers (2003), the innovation-decision process is divided into the five previous stages: knowledge, persuasion, decision, implementation, confirmation. It is important to understand thoroughly this process, especially how it occurs and the results of the confirmation stage, which can influence other adopters to decide whether to adopt or not the product. The five stages of the innovation-decision process for the case study are deeply analyzed in the following subsections.
4.2.1. Knowledge
The first moment of the innovation-decision process is the knowledge stage. At this stage, adopters get to know the innovation and its functions (Rogers, 2003). In this study, the main
factors that enabled interviewees to learn about Product P were the proximity to Company C, the characteristics of the innovation and the opinion from other adopters. Interviewees were asked about what led them to acquire Product P, and the decision was related with the technical characteristics of the product and also with a considerable number of characteristics that they were expecting to retrieve from the product after it started being used (in the implementation stage), such as: wireless capabilities, possibility of recording different types of signals, difficulty in locating the electrode while doing surface electromyography, not possible to record video, difficulty in processing/treating data, among others. These characteristics are all, described in sub-section
4.2.4.1. Characteristics of Device D and Software S. Considering the findings of Rogers
(2003) – Table 2 – and the characteristics studied by Kapoor et al. (2014) – Table 3 – interviewees mentioned the characteristics in Table 7.
Table 7. The main characteristics of Product P before its use
Innovation characteristics Main characteristics of Product P mentioned by the interviewees
Relative Advantage
(Rogers, 2003) Smaller device - “Smaller device in terms of the electrodes and the sensors (I3)”.
Portable device - “...equipment that can also be used
outside the laboratory (I2)”.
Waterproof when using a specific pouch - “…we were
looking for a technology that we could use in the water, which was waterproof and that’s kind of how Product P came into the picture (I3)”.
Complexity (Rogers, 2003) and Ease of Operation (Kapoor et al., 2014)
Easy to get and transmit signals via Bluetooth - “… and
then, in a simple way to collect the signals, in a simple way to transmit them, as I told you, via Bluetooth… (I11)”
Cost
(Kapoor et al., 2014)
Competitive price - “… the price was very interesting for
those who want to start a biomechanics lab (I2)".
The previous characteristics were not mentioned by every interviewee. However, each interviewee only highlighted the most important for them. In addition, interviewees at this stage, did not mention characteristics that match, compatibility, observability and trialability (Rogers,
2003) and visibility, image, riskiness, voluntariness and result demonstrability (Kapoor et al., 2014).
The knowledge stage was also influenced by the proximity with the company: “We got a good relation with Plux and they helped us out and that's why we started using it... The personal relation was important... (I13)”. Even though the relationship with Company C was not that strong in the beginning for some interviewees, others mentioned the existence of a good one with its administrators. In addition, according to Pittaway, Robertson, Munir, Denyer, and Neely (2004), when a company develops a good relationship with its customers, it can benefit with a lot of knowledge to improve an innovation, and, therefore, such relationship “needs to be treated with some caution” (p. 152). The interviewees that have a good relationship with Company C, shared that they had the opportunity to contribute for the development of some features of Product P. For example, I10 highlighted that “…they have seen us as interesting partners, who can also give them some added value…(I10)”. Moreover, I10 also shared that Company C redesigned Product P, with specific characteristics and also gave a specific name to it: “… We use this tool that was developed at the request of colleagues, specifically to our reality… (I10)”. This type of relationship, besides being important for Company C to improve its products, shows how malleable and close to the customers can be its operations, to provide solutions to customers’ needs.
In addition, the opinion from others were also identified as a factor that contributed for the knowledge built about Product P. Even though interviewees acquired Product P because they met someone from Company C, for some of them, the first time they heard about Product P was from someone outside Company C.
The proximity with the company, the characteristics of the innovation, and the opinions (from others), were the three crucial factors to form a perception about Product P. The design features of the technology proposed by Davis (1985) were substituted by this factors, once they were the ones that best fit into this research context.
4.2.2. Persuasion
Before the decision stage there is another very important stage - the persuasion - where a positive or a negative opinion about the product is shaped by the potential adopter (Rogers, 2003). After forming an opinion, the adopter is capable of deciding to adopt or reject the product.
At this stage of the innovation-decision process, it is not yet possible to determine if the information collected in the knowledge stage is valuable and absolutely valid, even with different information obtained about Product P. However, the information should be sufficient to be possible to build an individual perspective about the technology, particularly its perceived ease of use and perceived usefulness. Similarly to TAM, the three previously mentioned factors influenced the perceived ease of use and the perceived usefulness of the product under research, as well as, its perceived ease of use directly influenced its perceived usefulness (Davis, 1985). Despite these are no more than mere beliefs about a product, every interviewee recognized more positive perceptions than negative about Product P, i.e. more advantages than disadvantages in using it, making it the most appropriate product for their work, even without comparing it with other similar products. With data from the interviews, we are about to confirm the perceived ease of use of Product P and the direct effect in its perceived usefulness.
4.2.3. Decision
The decision stage can result in the adoption or non-adoption (rejection) of a product (Rogers, 2003). Notwithstanding that the acquisition of a product is the factor that determines the adoption or rejection of a product, in this research it was not deeply analyzed. Although most of the interviewees were the decision-makers, only their superiors had the capacity to approve the purchase, because it exceeded the limits of the interviewees’ functions in the organization. For this reason, interviewees did not share much information about the purchase process. Even though the number of characteristics mentioned by the interviewees was reduced at the knowledge stage, each of them developed positive perceptions, what led them to acquire Product P. Furthermore, the results of the interviews only show that most purchase orders arrived as
expected, where the availability of Company C was essential to provide a simple purchasing process. However, a few of these purchases were delayed and, sometimes, the equipment arrived with missing pieces: “…the last sensors we were working on were terribly delayed… (I1)”.
4.2.4. Implementation
Once the product is purchased, the adopters start using it and, due to this reason, new characteristics of the technology emerge. Once it is purchased, the perceptions of the product previously created can change (Arts et al., 2011), and positively or negatively influence the usage behavior. Only when the implementation is successful, technology benefits emerge (Linton, 2002), which will directly influence the confirmation stage results.
The main factors that influenced the adoption behavior of Product P were the characteristics of Device D and Software S, the relationship with Company C, and the competing products of Product P.
Each of the following sub-sections describes the interviewees’ feedback about each of the three mentioned factors. After that, in each subsection, feedbacks about the factor under analysis are discussed in order to explain how they influence the different scenarios obtained in the confirmation stage, as Figure 2 shows. Such as the characteristics represented in Table 7, the following feedbacks were all mentioned in the interviews, but each interviewee only highlighted the most important characteristics for his/her case.
4.2.4.1. Characteristics of Device D and Software S
When the interviewees started using Product P, specific characteristics of Device D and Software S emerged, and their perceptions about Product P changed.
Table 8 and Table 9 were built on the same basis as Table 7 and present the collected data about the characteristics of Device D and Software S, after the interviewees start using them.
Table 8. The main characteristics of Device D after its use
Innovation characteristics Main characteristics of Device D mentioned by the interviewees
Relative Advantage
(Rogers, 2003) Wireless capabilities - “… data was synchronized and [wirelessly] transmitted (I13)”.
Possibility of recording different types of signals -
“…sensors diversity… was very important to experiment and to present them to my master students (I3)”.
Cable and wires can be customized but not everyone knows about it - “I guess that you can order like the default
sensors settings, like you can also pick [or] you can specify different things like if you wanted a longer cable … I haven’t tried that [although] that looks like a rather complex process. And there was no obvious indication on the website exactly how you specify this (I7)”.
Cable and wires can limit the movement, which can compromise the synchronization of the signal - “… It
should work with children as well... There is much more movement, I think, than with adults (I6)”.
Electromyography frequency is slightly low - “… The
measurement frequency for electromyography is a little low, but it fits perfectly, it is 1000 Hertz… (I2)".
Difficulty to locate the electrode while doing surface electromyography - “… it is very easy for us to fail to
locate the electrode placement (I10)”.
Compatibility
(Rogers, 2003) Help and objectivity in assessments - “… so we ended up having the need here because we had a complaint from one employee or several employees, and the lack of an evaluation tool that would give us some more objective indicator that could give us the way to intervene and understand what was going on in the quality of work… then presented us this tool that could give us here another help in our evaluations and be a little more objective in our assessments (I10)”.
Adaptability with other systems - “We can always adapt
the… system to work with what we need (I7)”.
For some interviewees, the last version of Device D is worse than the previous one - “…since then I am not
using it, I ended up using the old equipment that I already know how does it work (I5)”.
Table 8. The main characteristics of Device D after its use (continuation)
Innovation characteristics Main characteristics of Device D mentioned by the interviewees
Complexity (Rogers, 2003) and Ease of Operation (Kapoor et al., 2014)
Ease to use - “… it’s relatively simple… to know
how to put electrodes and acquire data without being a biomechanics expert, which is a big advantage (I3)”.
Observability (Rogers, 2003) and
Visibility (Kapoor et al., 2014) Shows good results - “… we are [trying] the system for several recording and the results are very good based on this (I12)”.
Reduced data collection time - “… this tool
reduced data collection time by 20 minutes, which is the minimum we have for EMG (I10)”.
When using a device such as Device D, it is vital to have a software capable of acquiring, visualizing, recording and analyzing the data collected from the sensors. Without it, the use of Device D becomes meaningless. For this reason, it was important to analyze users’ feedback about the solution that Company C developed, Software S. Table 9 presents the main feedback of the interviewees.
Table 9. The main characteristics of Software S after its use
Innovation
characteristics Main characteristics of Software S mentioned by the interviewees
Relative Advantage
(Rogers, 2003) Sometimes, the signals stopped being collected - “… we did have the difficulties in which it was crushing but I think it doesn’t have to do with the software per se, maybe it's the connection, the Bluetooth connection (I6)”.
Does not record video - “…what would be good was a solution
that would allow us to identify on a time tape the correspondence of the electromyographic signal with the various processes. A time tape that ideally would also be reproduced in terms of video image (I10)”.
Compatibility
(Rogers, 2003) Difficult to interface with other software - “… Software limitations… to be able to interface… system with other software, other than theirs [(i.e., software from Company C)] … (I7)".
Complexity (Rogers, 2003) and Ease of Operation (Kapoor et al., 2014)
Hard to see and transmit the signals in real time - “…if I
could see the signal in real time, even though it was on time to record, that would be awesome… (I8)”.
Difficult to process/treat data - “When I took the file and tried
to treat it or observe the signals in Software S… it gave me lots of errors: I couldn't see it; it was very slow… horrible… (I5)”.
Almost all interviewees shared that the weakest part of Product P is Software S, even with engineers from Company C being always available to help. Software S is mainly used as a data collector and tester tool. With it, interviewees can check if sensors are collecting data or not. Despite Software S is a free open source platform and it provides some extra and paid options to analyze data, some interviewees have preferred to build their own algorithm to study a specific and more complex reality: “"…we are moving more and more toward using our own software (I1)".
On the other hand, some interviewees that do not find themselves as coding experts, find Software S hard to be used: “... I try to do a much harder job than it is, I have to adapt and ask the people who know how to program, the results... to program the treatment of the entire signal. It a tremendous hard work... It has to be intuitive; it has to be easy (I5)”. Even so, someone who knows how to program, said that “…for somebody like me they are ideal. For somebody who has very little technical ability… they might want something that is even more industry standard… even easy to work with, without calling somebody for support (I8)”. Similarly to the knowledge stage, interviewees did not mention some characteristics found in the literature: trialability (Rogers, 2003) and cost, visibility, image, riskiness, voluntariness and result demonstrability (Kapoor et al., 2014). This does not mean that these characteristics are not suitable for this case, but only that the information shared by the interviewee did not match any of these characteristics.
Coincidentally, both Device D and Software S feedbacks were mainly negative at this stage. When interviewees were asked about how was the experience with Product P, they mainly emphasized negative perceptions about it. The interviewees focused on the problems that emerged while using Product P because it negatively influenced their daily operations, even though most of them have confirmed its adoption, as explained in sub-section 4.2.5.1.
4.2.4.2. The relationship with Company C
Although it is not directly related to Product P, the relationship established with Company C also influenced the implementation stage and, consequently, the confirmation stage, as well as has influenced before the knowledge of Product P.
When interviewees had some problem, even if in a different time zone, the support team and/or the administrators of Company C were available to help them: “…we design prototypes and then they send us a prototype, we test it, we give them feedback, they go back and sort of adapt it (I1)”.
Company C was always available to help interviewees with Product P, especially until the beginning of its implementation. However, while some interviewees were used to a weekly, sometimes daily contact with certain administrators of Company C, once the organizational structure of the company changed, a manager was substituted, the frequency of contact decreased. I2 was used to ask for specific improvements of Product P before this change: “I have repeatedly shared my concerns about various usage options… Since he came in, I never got in touch with these concerns because I did not have many exchanges of ideas (I2)”.
Despite the relationship with Company C have changed over time, and new perceptions about Company C arose during the implementation stage, most interviewees did not find that this was an important factor to not the confirm the adoption of Product P. However, a few of them considered it to be significant to the confirmation stage. Together with the feedbacks previously mentioned, two different scenarios emerged during the confirmation stage, which are described in sub-section 4.2.5. Confirmation.
4.2.4.3. The competing products of Product P
There are many similar devices to Product P in the market and a lot of interviewees use, at least an extra product, similar to Product P. I7 emphasized that “There is always an alternative system (I7)” that can solve their problems. In fact, every user showed complete loyalty to Product P.
However, only few of them have highlighted the advantages and disadvantages of competitors that provide a similar device and software to Device D and Software S, respectively.
For example, Competitor N has a very similar product to Product P. Competitor N has devices and software that can be used in different research contexts, just like Product P. The interviewees that mentioned this competitor, are using Product P to collect signals of electromyography: “[Competitor N] reproduces immediate reports of muscle activity ...with [Company C] it takes me three months to get data off and I have to run MATLAB every day…(I5)" Despite, Competitor N provides a much more complete product, especially when it comes to its software, interviewees that confirmed the adoption of Product P – 4.2.5.1. Confirmation of the
adoption of Product P – are still using Product P because while doing research it is common
to use complementary products that can reduce the limitations of an existing product. This is the reason why the competitors’ topic only emerged during the implementation stage, since when the interviewees started to notice the limitations of Product P, only then did they also started to compare it with similar products that already existed or started looking for new ones.
4.2.5. Confirmation
At this stage, adopters analyze if decision of adopting a product was successful (Rogers, 2003). Interviewees have adopted Product P because of the positive perceptions they have formed about it. Nevertheless, after using it, these perceptions have changed. Despite some negative feedbacks, some interviewees did not find the disadvantages of Product P or a slightly different relationship with Company C to be relevant enough to abandon the use of Product P. Contrarily, other interviewees particularly took into consideration the new perceptions that emerged after using it. These two scenarios, based on the outcomes of the adoption behavior of each interviewee, allowed us to divide them in two groups:
• The interviewee confirms the adoption of Product P;
• The interviewee stops using Product P or just uses it occasionally, i.e. it is open to use it sporadically.
As is presented in Figure 2, the decision stage resulted in the adoption or the non-adoption of Product P, which also happened in this stage. The following sub-sections will explain what led to adoption’s confirmation or near rejection, considering the perceptions about Product P formed during the implementation stage.
4.2.5.1. Confirmation of the adoption of Product P
Most interviewees confirmed their decision of adopting Product P: I1, I2, I3, I6, I7, I8, I9, I10, I11, I12, I13,Most of them are still using Product P, however, I3 and I13 stopped using it because they are working in a new organization or because they changed their research purposes: “I am not using it right now… because I changed job (I13)”. As the relationship with Company C was an important factor to decide to adopt Product P, it was also a key factor to confirm the adoption of it. As I1 said, Company C was always available to help them out with some technical problems, which also was “…the main reason we work with them because they are so good with that (I1)”. Similarly to the persuasion stage, these interviewees confirmed that the perceived ease of use and perceived usefulness of Product P have contributed for a positive adoption behavior, i.e., to confirm the adoption.
4.2.5.2. Stop using Product P or just using it occasionally
Contrarily to the previous group, a few interviewees had a negative adoption behavior of Product P, i.e., they stopped using it: I4 and I5.
As the performance of Product P depends on the performance of both Software S and Device D, these interviewees were expecting a product that could immediately collect and analyze data to improve their daily operations. This did not happen when I4 and I5 started using it, and the positive perceptions they had about Product P turned became negative. For these interviewees, the perceived ease of use that they found during the persuasion stage did not confirm during its implementation and negatively influenced the performance of their researches. Even though the type of research that made them acquire Product P were different (I4 – to detect injuries and muscle activity in golf; I5 – to study the neuromuscular activity in swimmers) these interviewees
used the same type of sensors and shared similar experiences that led them to stop using Product P (I4) just to use it sporadically (I5), specially to do research in swimming biomechanics. The main factor that contributed for their negative perceptions was the amount of time spent in analyzing data collected with the sensors. This mainly happened due to the negative performance of Software S, and not the performance of Device D itself. However, these interviewees admitted using an older version of Device D: “I remember once, I was collecting… and I chose to collect with the older device and not with the new one (I4)”; “So from now on I do not even use, I am using the first version… which is the most basic… is the one I trust (I5)”.
A factor that also contributed for the negative perception of Product P was the relationship with Company C. Even though Company C was recognized by every interviewee as a company that cares about its customers and answers to their problems, I5 had a little struggle with Company C that created a huge impact during the implementation of Product P. I5 helped Company C to develop a waterproof pouch to protect Product P from water. After being developed the first version of the pouch, I5 contacted Company C to improve some features and, after contributing with more knowledge to the development of a second version of the pouch, Company C said that I5 needed to pay for it to have it: “…the company already had another structure, which must be the structure it has now… at the time we did it all based on a friendship… I gave my knowledge, they lent me the product. At that time I gave my whole know-how, I tested the product, the bag had nothing to do ... even the swimmers found it completely different. But then, I would have to pay… to have the product that I have contributed for. From there on, I said I did not have that money, neither could buy an equipment that I gave knowledge. So, the partnership ended (I5)”.
These factors led interviewees to start looking for other devices that could be more useful for their researches, and I4 also had a particular situation that led to acquire a competing product. I4 asked Company C for help to solve a problem related with Software S, but it took a lot of time to do it. I4 needed to solve it quickly and looked for a new solution: “We ended up finding another solution… we are users, we do not have to be looking for solutions properly (I4)”.
