Open Access

Understanding major dimensions and determinants that help in diffusion & adoption of product innovation: using AHP approach

Journal of Global Entrepreneurship Research20177:12

https://doi.org/10.1186/s40497-017-0072-4

Received: 2 August 2016

Accepted: 25 May 2017

Published: 16 June 2017

Abstract

In this global world, diffusion & adoption of new product innovation have become intricate and involves multifaceted process, as consumers demand a broad range of choices. So, this has generated enthusiasm among researchers and business practitioners while studying and implementing diffusion & adoption of product innovation.

Even though diffusion & adoption is known as one of the most important processes of an innovation management, but the literature available in this area is highly fragmented. Therefore, in this paper, we attempt to develop a framework for diffusion & adoption, which helps in learning and understanding its dimensions and determinants from different perspectives. In this study, AHP has been used to analyse the relative importance and provide a ranking to the dimensions and determinants of product innovation. The findings of this study can help the management, product developers and market professionals in strategic planning and in setting their priorities when there is resource constraint. This study also helps in deciding and prioritising the dimensions and determinants that should be fuse during product development phase.

Keywords

Analytical Hierarchy Process (AHP) Diffusion & adoption Factors Innovation management Knowledge management Technology transfer Product innovation

Background

Innovation has been referred as utilisation of thoughts, ideas, plans and concepts to make economic benefits (Akinboye, 2003). When connected to business administration, innovation is a process of being inventive, executing new strategies to sort out or run an organisation and produce enhanced results (Gates and Cooksey, 1998). In the innovation process, creativity and imagination prompt invention; the first presentation or usage of an invention will be innovation, which leads to adoption; adoption results from the diffusion process (Kumar, Kumar and Haleem, 2014).

Product innovation is a complex and multifaceted process which involves many influential factors not only at an individual level but also at organisational and institutional level. It starts with the design phase and ends with the successful adoption of product and having a high degree of consumer satisfaction. This study explains the dynamic relationship between the different process through the input-output model of innovation process shown in Fig. 1.
Fig. 1

Input and output based model on Innovation Management Processes (Adopted from Roger 1983 and Kumar et al. 2015a, b, c, d)

Rogers (2003) said that the process of diffusion is the spread of new thoughts from its source of creation to its ultimate consumers or adopters. The adoption process is in this way start with the mental procedure through which an individual goes from first finding out about an innovation to a particular selection. The innovation process is inadequate if the creation or the final product is restricted just to the innovator i.e. it is not embraced by others and does not bring change to the refer system (Mannan et al., 2015a, b). So many researchers take an interest in this interdisciplinary field of diffusion and adoption. Highly fragment literature is available which suggest that diffusion & adoption is one of the most complex processes of innovation management (Kumar, Singh and Haleem, 2014). It requires various inputs like Social system variables, Knowledge management, Human resource, Financial inputs, Technology related inputs, technology transfer, Technical know-how, Production capabilities, ICT Resources, Marketing/Sales inputs including customers’ needs/wants, R & D facilities. In return, it increases Competitiveness, Cost effectiveness, Profitability, Higher market share, Brand image, Revenues and Growth, Customer perception, Product/service performance, Credibility and Innovation success rate. The innovation management process has two type of flows; one is which require only information and the other one is which require material, funds, information, etc. (Kumar, Singh and Haleem, 2015). For efficient information flow, there is a requirement of an appropriate communication channel and a proper selection of the mode of communication. It is conceivable to distinguish several ways to deal with the diffusion of innovation, each one concentrating on the particular aspect of diffusion and having a different perspective. The fundamental contribution comes from economics, sociology, anthropology, and marketing. Business experts have created different econometric models to clarify the diffusion of new products in the light of the past conduct of the consumers. Geroski (2000) says that business studies have embraced an extensive variety of unique research tools to clarify the customer behaviour. Social studies have been engaged to inspect the socio-consistent element and mental factors that impact the diffusion of innovation, and most of the anthropological studies are base on case studies of the diffusion of new creations, regulations and information. Now, multi-disciplinary research has been developed looking at the diffusion of educational, medicinal and other approaches to innovation. Research on diffusion tries to identify the variables that impact the rate and course of the adoption of innovation.

Research objectives

This research paper helps to achieve the following objectives:
  • Identification of the main dimensions and related determinants helping in diffusion & adoption of product innovation.

  • Prioritisation of the dimensions to develop framework helping in diffusion & adoption of product innovation.

  • Prioritisation of the determinants to develop the framework supporting in diffusion & adoption of product innovation.

  • Global ranking of determinants for successful diffusion & adoption of product innovation.

  • Analysis of the global ranks and local ranks of dimensions and determinants.

This study is to identify and prioritise the determinants that help in diffusion and adoption of product innovation. In this study, Analytical Hierarchy Process (AHP) approach has recommended for prioritising the determinants helping in diffusion & adoption of product innovation (Saaty, 1980).

The remainder of this paper organised as follows. The state-of-the-art review of related literature covered as well as identified determinants are helping in diffusion and adoption of product innovation based on extensive literature review (Section 2). Section 3 discusses and explains the methodology of the research for the present work. The data analysis has done in Section 4. Discussion, finding and the managerial implication is provided in Section 5. Finally, Section 6 provides conclusions, unique contributions, limitations and the scope for future research.

Literature review

This section contains the literature on diffusion and adoption of product innovation. The explanation of four dimensions of diffusion & adoption which is given by Everett M Roger’s book titled “Diffusion of innovation” that has more than 77,533 citations is provided. The related determinants/ sub-factors of these dimensions, which helps in diffusion and adoption of product innovation identified and described using literature review approach. Research gaps for this study have been further discussed.

Diffusion and adoption of product innovation

Rogers (2003, fifth edition) defines diffusion as “the process by which an innovation is communicated through certain channels over time among the members of a social system”.

It is being identified sort of communication that deals with the spread of messages which are view as new thoughts or ideas. Nowadays, information technology (IT) as an example, the Internet and mobile phones which become a part of mass communications and a tool of diffusion. It is the “novelty” of an idea in the message of communication that gives diffusion its unique character. There is a lot of work going on in the area of diffusion & adoption, and the research status of diffusion and adoption is impressive. In the 1960s and 1970s, the aftereffects of diffusion studies have incorporated in textbooks of public relations, marketing, advertising, social psychology, consumer behaviour and their related areas.

Researches/academicians and practitioners have come together to view the diffusion of product innovation as a significant field of sociology. Numerous U.S. government organisations have a division assigned for diffusing new technologies to local government as well as to the public; examples are the National Institutes of Health, U.S. Department of Transportation, The U.S. Department of Agriculture, and the U.S. Department of Education. Some federal agencies support research on diffusion, for example, the National Science Foundation and various private organisations. Further, most business corporations have a marketing division that is the researcher of diffusing new product and statistical research activities that lead to diffusion to help the organisation’s advertising endeavours. Since innovation is happening throughout the society and the application of diffusion & adoption theory is found in numerous spots. In this way, diffusion & adoption research has accomplished a prominent position today.

Identification of determinants helping in diffusion & adoption of product innovation

Literature review approach has used for identifying determinants helping in diffusion & adoption of product innovation. Initially, seventeen determinants helping in diffusion & adoption of product innovation. Further, these identified determinants have been categorised into four dimensions (Innovativeness, Communication channels, Time, and Social system). These identified four dimensions, and related determinants help in diffusion process and determine the success of product innovation have been discussing in detail in below section.

Innovativeness

Innovation is depicted as a thought, practice, or an object that is view as new by consumers. Numerous analysts concur that the primary success factor of diffusion of innovation is exclusiveness and uniqueness (Pham and Ho 2015; Montazemi and Qahri-Saremi 2015; Winer 2007; Kinnear and Bernhardt, 1990; Cooper 2001). Market analysts have frequently considered consumer acknowledgement of innovation in connection with product innovativeness (Garcia and Calantone, 2002).

Product innovativeness is often identified with the principal innovation qualities (i.e., relative advantage, trialability and compatibility) (Danneels and Kleinschmidtb, 2001). However, (Danneels and Kleinschmidtb, 2001) says that conceptualization of product innovativeness has yet to be establish empirically. There are different points of view in the contemporary customer conduct literature (e.g., Hoyer and MacInnis, 2008) typically see factor such as compatibility and trialability as customer learning necessities that impact the speed of diffusion, rather than measurements of innovativeness.

Such interrelated and complex relationships do not often empirically examine in connection to perceived innovativeness. Uniqueness or exclusiveness has been a critical analysis of product innovativeness and has offered as the primary dimension at times (Hoeffler & Keller, 2003; Rogers, 2003, Valente & Rogers, 1995; Sanni et al., 2013; Schiffman & Kanuk, 2004a; 2004b; Singh et al., 2012; Tidd, 2010; Tidd et al., 1997). So, this prompts a definition whereby the level of innovativeness is not a distinct development but rather equals to the degree of originality. The individual meetings with experts recommend that innovativeness is more than uniqueness.

Characteristics of innovativeness that determine success to product innovation and which increase its possibility of successful diffusion and adoption are identifies:

Uniqueness (UQ)

Innovation which is unique experience three to five times more success than the innovation which does not stand out (Cooper 2001). However, Hoffman (2005), Lamb et al. (2004), Baker, (1999) note that the changes in the product that already exist and not the unique and radical ones are less risky. Therefore, more chances of having a successful diffusion. In this regard, researchers such as Zikmund & D’Amico (1993); Kinnear et al. (1995); Winer (2007); Hoffman (2005); Cooper (2001) concur that product innovation achievement relies on upon the accomplishment of making a product that is unique when contrasted with competitors.

Relative advantage (RA)

Relative advantage is the extent to which an innovation is seen superior to the other ideas, and it supersedes by specific users, measured in terms that matter to those users, as comfort, economic point of preference, satisfaction or social prestige. The more noteworthy the apparent relative advantage of a product, the quicker its rate of adoption is likely to be. There are no set parameters for what constitutes as “relative advantage”. It relies on upon the specific observations and necessities of the consumers.

Compatibility (CB)

This is the extent to which a product innovation is seen as predictable and consistent with the qualities, past experiences, and needs of the consumers. An innovation that is inconsistent with the specified conditions, guidelines or practices won’t be adopted or received as fast as an innovation that is compatible

Customization (CU)

Pine and Gilmore (1999a, 1999b, p. 76) characterise customization as “…producing in light of a specific desire of analysing user.” The researchers like Duray (2000a); Pine and Gilmore (1999a, 1999b); Anuar et al. (2014); Chiyangwa and Alexander (2016) said that it is significant to make the refinement in the varieties and customization. While customization takes a stab at satisfying individual user’s needs, ranges include more choices from which the user can pick. “In most of the cases, the user doesn’t need choices; they exactly want what they specified” (Pine and Gilmore 1999a, 1999b, p. 76). Customization is planned to increase the value of an item since a customised product is well defined according to customer’s needs. The client association is a vital issue; since user dictates what the venture needs to create. In many cases, product customization can happen ex-post after assembling by the retailer or the users themselves. Also, to roll out procedures adaptable a few improvements on the product development level are necessary. Truth be told, “[t]he idea of modularity is an essential building block in the assembling circumstances customarily thought to be adaptable” (Duray et al. 2000a, p. 610).

Trialability (TB)

According to Vasseur and Kemp (2015); El-Gohary (2012); Seyal and Rahman (2003); Stockdale and Standing (2006); Moore and Benbasat (1991); Chiyangwa and Alexander (2016); Sabi et al. (2016); Pham and Ho (2015); Hameed et al. (2012a) said that “Trial-ability is the degree to which an innovation can be tried in a restricted premises.” Innovations that can be attempted on the instalment arrangement are embraced quickly than innovations that are not tried. The individual trying-out of advancement is an approach to offer to intend to development, to discover how innovation works under one’s particular conditions. This trial is a way to dissipate instability about the new ideas.

Social system (SS)

The diffusion of the new product usually takes place in the social setting frequently referred as a social system. Sometimes in the context of consumer behavior, the term market segment and target market may be used instead of the social system. Rogers (2003) characterised the social framework as “an arrangement of interrelated units occupied with joint critical thinking to achieve a typical objective” (p. 23). Since diffusion of product innovation happens in the social framework or system, it is impacted by the social structure of the social framework. He guaranteed that the nature of the social framework influences people’s innovativeness, which is the primary standard for arranging adopters.

The studies on the social system of framework gave by Schiffman et al. (2004a, 2004b); Bakanauskas, (2004); Baker, (2001); Hoffman (2005) express that the related factor of the social system that helps in the accomplishment of diffusion of product innovation relies on:

Attitude towards the change in social system (TC)

The social system is dynamic is nature which changes continuously with respect to time. So, there should be a positive attitude towards the change in the technology and the market.

Rational relationships among members of the social system (RR)

A rational relationship is a logical relationship among the member of the social system which is based on the reasons and facts rather than on emotions. Many researchers who deal with the consumer behaviour advocate that the rationality among the consumer decide the successful adoption of a product but it’s hard to measure it. Sometime users of social system/ target market are emotionally attached or biased towards the certain product.

Level of homogeneity (HL)

Uniformity among the members of a social system is neglected by the researchers as a condition for diffusion but in fact, play a fundamental role for social system/ target market.

Nature and norms characteristic of the social system (NN)

Norms are established behaviour patterns for the members of a social system which defines the consumer’s compatibility with innovation.

Communication (CM)

Communication is neither transmission of message nor message itself. It is the mutually shared understanding, beginning with the receiver. Communication should be viable in an association. It is the essence of diffusion and adoption. Indeed, even the basic operation of on organisation can’t be performed well without effective communication. It includes the steady stream of information and feedback is an essential part of it. Communication between individuals from the innovation execution group likewise expands the likelihood of fruitful selection of innovation, when colleagues always discuss and communicate about the course of the creation process, conceivable issues and methods for solving those problems. Outside the association, it is between the organisation, its accomplices and shareholders, its delegates and user. Determinants of communication that helps in diffusion and adoption are discussed below:

Mode of communication (MC)

Communication has advanced significantly from the times of manual typewriters, telegrams and window placards. According to the researchers, such as; Baker, (1995), Cooper, (1999), Cooper, (2005) Dahnil et al. (2014), Hameed, et al. (2012a), Gopalakrishnan and Bierly (2001) and so forth the newest trend in communication that aids in a diffusion of innovation is social media advertising. That is, the cautious utilisation of social media sites like Facebook, MySpace, YouTube and Twitter to draw the consumer in their trusted network. Buyers make online profiles and organisations make “fan pages” or Twitter accounts that user might “like” or associate with it. The second alternative is email; Internal organisation communication depends on email; in only an era, email has supplanted paper as the mode of communication. The website is additionally one of the options for Critical communication. A few organisations go beyond and utilise their website to host user discussion forums, reports, videos and to post blog

Understandable communication (UC)

The interesting point of understandable communication is that individuals will adequately comprehend the message. Consumers need to know how marketers can help them, yet they would not prefer to invest hours making sense of what marketers are attempting to tell them. Clear communication is additionally critical inside the organisation and with the suppliers as well. Only if staff and vendors comprehend messages, then only they will take care of the job effectively. Dahnil et al. (2014), Gopalakrishnan and Damanpour (1994), Hameed et al. (2012a) said that understandable communication is necessary to the success of any organisation. However, it is often left to care for itself in numerous organisations. Understandable and clear communication built trust, reputation, relationship with customers and credibility; and all of these build organisation.

Communication channel (CC)

In an association, information flows in every direction i.e. backwards, forward, and sideways. This information alludes as communication. Communication channels mean the way this information flows inside the association and with different organisations. Gopalakrishnan and Damanpour (1994), Dahnil et al. (2014), Anuar et al. (2014), Abed et al. (2015), Hameed et al. (2012a) said that if communication channel breakdown, it will prompt a wasteful and insufficient information flow. Employees are unconscious of what the organisation expects of them. They are clueless about what is happening in the organisation. This will make them suspicious of thought processes and any adjustments in the organization. Likewise, without compelling communication channel, employees become division oriented instead of organisation oriented, and this influence decision making and profitability in the working environment.

Communication network (CN)

According to Roger (2003), communication network consists of individuals that are interconnected by information flow that follows a pattern which means communication networks helps to create a link in a social system. Communication networking is critical for diffusion because, in an efficient network, the flow of information is smooth and right which take less time.

Time (TM)

In research, the ruling conclusion is that time is the premise of the entire procedure of diffusion and adoption of product innovation. Schiffman et al., 2004a, 2004b research consumer behaviour for a division of adopters. Hoffman et al., 2005; Antonides et al., 1998; Zikmund et al., 1993; Solomon et al., 2002; Schiffman et al. 2004a, 2004b and Lamb et al., 2004 assets of purchasing recurrence. Bakanauskas, 2004 measures adoption rate and all the researchers advocate that time is one of the major factors for diffusion and adoption.

Introduction timing (IT)

Introduction timing is frequently a represent the moment of Make-or-Break for small and medium enterprises. Whether, it’s a producer working through wholesale and retail outlets, or a retailer attempting to draw in the consumer to innovation. Compelling timing is necessary to pull in new customer and to optimise profit and revenue on innovations. (Klastorin and Tsai, 2004; Regibeau and Rockett, 1996; Srivastava and Lee, 2005; Tony Ke, Max Shen and Li, 2013). In a competitive business sector, the timing and planning of introduction are also influenced by the introduction timing of similar product which is launched by other competitive organisations.

First-mover attempts to pull eager purchasers. Other organisations incline toward a second-mover and wait until the business sector gets comfortable with the item and interest starts to develop. This technique secures against a negative response that may come from the item offered by the first-mover.

Product development cycle (PD)

Johnson and Kirchain, 2011 said the article dealing with the assessment of product development is somewhat substantial and concentrates principally on the lead-time metric. Surprisingly, only a few researchers like Chiyangwa and Alexander (2016) and Hameed et al. (2012a) have additionally demonstrated the significance of product development lead-time or cycle as it identifies with product or project achievement.

Progressive Technology (PT)

As the time changes, innovation makes obsolete to the existing ones. For example, a smartphone replaces many gadgets which can’t even count like watches, telephone, camera, personal computers, banking, etc. Adoption rate (AR)

Rogers and Shoemaker (1971) define the rate of adoption as “the relative speed with which an innovation adopted by members of a social system. This rate of adoption is usually measured by the length of time required for a certain percentage of the members of a system to adopt an innovation” (p.28). Anuar et al. (2014), Montazemi and Qahri-Saremi (2015), Gopalakrishnan and Damanpour (1994). Rogers and Shoemaker agree with Toffler (1970) and others that the rate of adoption of innovations is rapidly increasing over a specified period. For instance, they justify the need for their book with the opening statement that “the phenomenal rate at which innovations are invented, developed, and spread makes it important to look at how these new ideas affect (or fail to change) the existing social order (Rogers and Shoemaker 1971, p.1).” But, no evidence is presented to show that the rate of adoption is increasing. All these variables have complied in Table 1 with references.
Table 1

Dimensions & determinants that influence product innovation management with support references

Dimensions

References

Determinants

References

Innovativeness (IN)

Zikmund & D’ Amico (1993), Gopalakrishnan and Damanpour (1994), Kinnear et al. (1995), Lambkin (1998), Baker(1999), Cooper(2001), Tidd (2001), Rogers (2003), Lamb et al. (2004), Hoffman (2005), Winer (2007), Solomon (2009), Hameed et al. (2012a), Rampersad et al. (2012), Lee (2014), Montazemi and Qahri-Saremi (2015), Pham and Ho (2015), Porter and Graham (2015), Wu and Chiu, (2015), Mannan et al. (2016) Rogers (2004), Zikmund et al. (2003), Cooper (2010)

Uniqueness (UQ)

exclusiveness of innovation

Kinnear and Bernhardt (1990); Moore and Benbasat (1991); Cooper (2001); Winer (2007); El-Gohary (2012); Hameed et al. (2012a); Lee (2014); Gebrezgabher et al. (2015); Montazemi and Qahri-Saremi (2015); Vasseur and Kemp (2015); Chiyangwa and Alexander (2016); Wang et al. (2016),

Relative Advantage (RA)

the clearly seen benefit provided by innovation

Poon and Swatman (1999), Beatty et al. (2001), Kuan and Chau (2001), Mirchandani and Motwani (2001), Mehrtens et al. (2001), Chwelos, et al. (2001), Doolin et al. (2003), El-Gohary (2012), Vasseur and Kemp (2015), El-Gohary et al. (2009), Grandon and Pearson (2004), Al-Qirim (2006), Stockdale and Standing (2006), Kumar (2015a, 2015b, 2015c, 2015d), Moore and Benbasat(1991), Chiyangwa and Alexander (2016), Sabi et al. (2016), Wang et al. (2016), Lee (2014), Trang et al. (2016), Lawson-Body and O’Keefe (2006), Hameed et al. (2012a), Al-Qirim (2007), Wu and Chiu (2015)

Compatibility(CB)

compatible with a lifestyle norms, values, and skills of consumers

Vasseur and Kemp (2015), El-Gohary (2012). El-Gohary et al. (2009), Beatty et al., (2001), Mirchandani and Motwani (2001), Kumar et al.(2015a, 2015b, 2015c, 2015d), Moore and Benbasat (1991), Chiyangwa and Alexander (2016), Sabi et al. (2016), Pham and Ho (2015), Arifin and Frmanzah (2015), Wang et al. (2016), Trang et al. (2016), Damanpour (1996), Gopalakrishnan and Damanpour (1994), Hameed et al. (2012a), Rampersad et al.(2012), Wu and Chiu (2015)

Customization(CU) Alterable to individual or personal specifications

Pine and Gilmore (1999a, 1999b), Duray et al. (2000a), Hameed et al. (2012a), Anuar et al. (2014), Dedehayir et al. (2014), Noppers et al. (2015),Chiyangwa and Alexander(2016), Sabi et al. (2016), Wang et al. (2016). Chang et al. (2015), Mannan & Khurana (2012), Khurana et al. (2013), Khurana et al. (2014).

Trial ability (TB) Ability to try out innovation

Moore and Benbasat (1991),Seyal and Rahman (2003), El-Gohary (2012), Pham and Ho (2015), Vasseur and Kemp (2015), Chiyangwa and Alexander (2016), Sabi et al. (2016), Stockdale and Standing (2006).

Social system (SS)

Montazemi and Qahri-Saremi (2015), Rogers (2003), Vasseur and Kemp (2015), Venkatesh et al., (2003), Chiyangwa and Alexander (2016), Sarpong et al. (2016), Locke and Rissman (2015), Long et al. (2016), Montalvo (2008), Shen et al. (2015), Rai and Robinson (2015), Dahnil et al. (2014), Brook and Pagnanelli (2014), Hottenstein et al. (1999), Hameed et al. (2012a)

Attitude towards change (TC)

Customer reaction towards change

Venkatesh et al. (2003), Montalvo (2008), Rampersad et al.(2012), Anuar et al. (2014), Lee (2014), Knudsen and Roman (2015), Long et al. (2016),Abed et al.(2015), Porter and Graham (2015), Shen et al. (2015), Chiyangwa and Alexander (2016).

Rational relationships (RR)

Reasonable relationship based on the facts

Dahnil et al. (2014), Abed et al. (2015), Locke and Rissman (2015), Montazemi and Qahri-Saremi (2015), Knudsen and Roman (2015), Long et al. (2016), Rai and Robinson, (2015). Kumar, (2014), Kumar & Haleem, (2016), Grandon & Pearson, (2003), Henry & Kato, (2011), Kapur et al., (2012)

Homogeneity level (HL)

Uniformity of social system

Venkatesh et al. (2003), Montalvo (2008), Abed et al. (2015), Locke and Rissman (2015), Kusumaningtyas and Suwarto (2015), Porter and Graham (2015), Shen et al. (2015), Rai and Robinson (2015), Sabi et al. (2016).

Nature & norms (NN)

Social and cultural policies of the society

Chang and Cheung (2001), Kuan and Chau (2001), Grandon and Pearson (2004), El-Gohary et al. (2009), El-Gohary(2012), Crann et al. (2015), Gebrezgabher et al. (2015), Long et al. (2016), Rai and Robinson (2015), Shen et al. (2015).

Communication (CM)

Rogers (2003), Dahnil et al. (2014), Gopalakrishnan and Damanpour (1994), Hameed et al. (2012a), Mannan, Khan, and Khurana (2012), Mannan, Jameel, and Haleem (2013a),

Mode of communication (MC)

Medium of communication

Gopalakrishnan and Bierly (2001), Hameed et al. (2012a), Dahnil et al. (2014).

Understandable communication (UD)

Easy to understand

Gopalakrishnan and Damanpour (1994), Hameed et al. (2012a), Dahnil et al. (2014), Kumar, Luthra and Haleem (2015b).

Communication channels (CC)

Link of information

Gopalakrishnan and Damanpour (1994), Hameed et al. (2012a), Mannan, Khurana, and Haleem (2012), Mannan, Khan, and Khurana (2013b), Anuar et al.(2014), Dahnil et al. (2014), Abed et al.(2015).

Communication network (CN)

Interact and exchange information in social system

Gopalakrishnan and Damanpour (1994), Hameed et al. (2012a), Dahnil et al. (2014), Trang et al. (2016), Wang et al. (2016). Khurana et al., (2012), Sambasivan & Fei, (2008), Noorul Haq & Kannan, (2006), Pardeshi, (2014)

Time (TM)

Rogers (2003), Derwisch et al. (2015), Sarpong et al. (2016), Gopalakrishnan and Damanpour (1994), Tsai and Hung (2014)

Introduction timing (IT)

Proper timing of introduction of innovation into the market

Regibeau and Rockett, (1996), Klastorin and Tsai (2004), Srivastava and Lee (2005), Tony Ke, Max Shen and Li (2013), Lee (2014).

Product development cycle (PD)

Time require to develop an innovation

Hameed et al. (2012a), Kaushik et al. (2014), Chiyangwa and Alexander (2016). Venkatesh et al., (2002), Walitzer et al., (2015), Winer, (2000), Zhu et al., (2012)

Progressive Technology (PT)

Innovation making obsolete to the existing one with time

Anuar et al. (2014), Lee (2014), Tsai and Hung (2014), Abed et al.(2015), Pham and Ho (2015), Porter and Graham (2015), Chiyangwa and Alexander (2016).

Adoption rate (AR)

Time require to adopt innovation

Gopalakrishnan and Damanpour (1994), Anuar et al. (2014), Montazemi and Qahri-Saremi (2015). Lamb et al., (2013), Lucia-Palacios et al., (2014), Udoh, (2012).

Research gaps

Many past research hypotheses advocate that setbacks experienced during diffusion are more prominent than the delays experienced during creation or product development phase. Although it is already established that diffusion and adoption are one of the costliest phases of the innovation process, there is practically no exertion made to develop a framework for diffusion and adoption or learning and understanding its dimensions and determinants from different perspectives. There is no study which deals with the relative importance of diffusion & adoption dimensions and their correlation with the determinants i.e. in how much weightage these dimension and determinants influences diffusion and adoption of product innovation. Therefore, in this study, AHP is used to analyse the relative importance and provide the ranking to the dimensions and determinants of product innovation. This study helps management, product developers and market professionals in strategic planning and set their priorities when there is resource constraint. It also helps in deciding and prioritising the dimensions and determinants that should be incorporated during product development phase.

Methods

In this paper, we have taken the four dimensions given by Everett M Roger’s book diffusion of innovation which has more than 77,533 citations and which is the second most cited book in social science. Researcher believes that the name of Everett Rogers is virtually synonymous with the study of the diffusion of innovations. The determinants/ sub-factors of dimensions are identified with the help of extensive literature review of most cited paper in the area of diffusion and adoption of innovation. After determining dimension and determinant, we have conducted a workshop in which we sought the opinion of experts on categorising determinants under four well-established dimensions. There are four experts from the management of MSMEs and three from academia. After establishing dimension and determinants, we have used AHP methodology with the help of expert suggestions. Utilisation of AHP methodology helps in accomplishing efficient diffusion and adoption of product innovation and uncovering their significance for the competitiveness of organisations.

AHP has been used for this study because of its benefits over other multi-criteria basic making (MCDM) tools, for example, ISM, DEMETEL, ELECTRE, TOPSIS, and ANP. ISM can give the following structure to the reliant variables required in the study yet neglects to examine the relative significance of the variables w.r.t. The objective. Henceforth, it distinguishes the course of the relationship of the variables yet at the same time neglects to perceive their weightage. Other MCDM tools like ELECTRE and TOPSIS has restricted acknowledgement among practitioners and researchers (Mathiyazhagan et al., 2013; Kumar et al., 2015a, 2015b, 2015c, 2015d). Another MCDM tool is ANP which requires a few comparison pair-wise matrices that add complexity to the study for expert members (Harputlugil et al., 2011) as compared with AHP (Mathiyazhagan et al., 2013).

Solving problem utilising AHP can be sorted into the following three broad steps:

Step I: Conversion of decision problem into a hierarchical structure.

In this step, a systematic hierarchical structure was first developed (as shown in Fig. 2) to facilitate the study.
Fig. 2

A hierarchical AHP model of dimensions and determinants for the successful diffusion and adoption of product innovation

A list of dimensions and determinants for efficient diffusion and adoption are selected through a literature review and a structured hierarchical framework was developed with the help of expert consultation.

Step II: Collection of information and construct a judgmental matrix

This step includes the gathering of information from experts. The experts have examined the strength of dimensions in connection with another dimension positioned on the relative scales in a pair-wise comparison (Saaty, 2000; Saaty, 2008; Lewis et al., 2006). With the assistance of the expert’s judgment, an arrangement of correlation matrices was built for all components in a level and then change individual correlation judgments into proportion scale estimations (Kannan et al., 2008).

Step III: Determination of needs by calculation of standardised weights.

After the judgmental matrices are developed, normalised weights of all criteria are figured. The sets of data are not consistent with this situation; subsequently, the researcher of the standardised eigenvector method is recommended for computing relative weights (Saaty, 1996, 2000; Lewis et al., 2006). At that point, the global weight and most extreme Eigen value (λmax) for every matrix are figured. The global priority weights of every hierarchical level can be figured by multiplying a standardized vector to approve the pair-wise correlation matrix gives an entirely reliable assessment (Kannan et al., 2008).

A nine-point scale, as proposed by Saaty (2000) proposed, and is utilised to measure the inclinations. The CR can be figured out by utilising the following formula:
$$ \mathrm{CR}=\left(\uplambda \max \hbox{-} \mathrm{n}\right)/\left(\mathrm{n}\hbox{-} 1\right)\left(\mathrm{RI}\right) $$

Where the order of matrix is “n” and “RI” is known as random consistency index.

RI values for matrices given in Table 2
Table 2

Average random consistency index (RI) based on critical size

N

1

2

3

4

5

6

7

8

9

10

RI

0

0

0.52

0.89

1.11

1.25

1.35

1.40

1.45

1.49

Source: Adapted from Saaty (2000)

Data analysis and results

There are four main dimensions of diffusion and adoption of product innovation i.e. innovativeness, social system, communication and time. In this study, we have identified various determinants of each dimension. With the help of AHP approach, we have calculated local and global weight and then rank them accordingly

Tables 3, 4, 5, 6 and 7 shows the local priority weights of different factors as generated to determine the relative importance of these dimensions and their determinants with respect to successful diffusion and adoption of product innovation. After that, rankings of the level- 3and level-4 has been done from their global weights (priority percent with respect to the objective) and are represented in Table 8.
Table 3

Pairwise comparison matrix of diffusion and adoption dimensions that influences innovation management

Diffusion and adoption dimensions

Innovativeness (IN)

Social system (SS)

Communication (CM)

Time (TM)

Priority weight

Rank

Innovativeness (IN)

1

2

1/3

1/4

0.124793

3

Social system (SS)

1

1/4

1/5

0.0777981

4

Communication (CM)

1

1/2

0.305571

2

Time (TM)

1

0.491839

1

Maximum Eigen Value = 4.04836; C.I. = 0.0161203

Table 4

Pairwise comparison matrix of determinants of Innovativeness of diffusion and adoption that influences innovation management

Determinants under “Innovativeness”

Uniqueness (UQ)

Relative Advantage (RA)

Compatibility (CB)

Customization (CU)

Trial-ability (TB)

Priority weight

Rank

Uniqueness (UQ)

1

¼

1/3

1/5

2

0.0799396

5

Relative Advantage (RA)

1

1/5

2

5

0.266364

2

Compatibility(CB)

1

4

6

0.418962

1

Customization(CU)

1

3

0.18301

3

Trialability (TB)

1

0.0517252

4

Maximum Eigen Value = 5.33199; C.I. = 0.0829966

Table 5

Pairwise comparison matrix of determinants of Social system of diffusion and adoption that influences innovation management

Determinants under “Social system”

Attitude Towards Change (TC)

Rational Relationships (RR)

Homogeneity level (HL)

Nature &Norms (NN)

Priority weight

Rank

Attitude Towards Change (TC)

1

5

3

1/3

0.282984

2

Rational Relationships (RR)

1

1/3

1/6

0.0599547

4

Homogeneity level (HL)

1

1/3

0.141077

3

Nature & Norms (NN)

1

0.515984

1

Maximum Eigen Value = 4.16272; C.I. = 0.0542394

Table 6

Pairwise comparison matrix of determinants of Communication of diffusion and adoption that influences innovation management

Determinants under “Communication”

Mode of Communication (MC)

Understandable communication (UD)

Communication channels (CC)

Communication Network (CN)

Priority weight

Rank

Mode of Communication (MC)

1

1/2

3

5

0.310242

2

Understandable communication (UD)

1

4

6

0.494763

1

Communication channels (CC)

1

3

0.133594

3

Communication Network (CN)

1

0.061401

4

Maximum Eigen Value = 4.07878; C.I. = 0.0262612

Table 7

Pairwise comparison matrix of determinants of Time of diffusion and adoption that influences innovation management

Determinants under “Time”

Introduction timing (IT)

Product Development cycle (PD)

Progressive Technology (PT)

Adoption rate (AR)

Priority weight

Rank

Introduction timing (IT)

1

4

1/3

1/2

0.179611

3

Product Development cycle (PD)

1

1/6

1/4

0.0632412

4

Progressive Technology (PT)

1

2

0.479702

1

Adoption rate (AR)

1

0.277445

2

Maximum Eigen Value = 4.07101; C.I. = 0.0236709

Table 8

Global ranking of determinants for successful diffusion and adoption of product innovation

Dimensions

Relative weight

Determinants

Local weight

Local rank

Global weight

Global rank

Innovativeness (IN)

0.124793

Uniqueness (UQ)

0.079940

4

0.0099759

15

Relative Advantage (RA)

0.266364

2

0.0332403

9

Compatibility (CB)

0.418962

1

0.052283

6

Customization(CU)

0.18301

3

0.022838

11

Trialability (TB)

0.051725

5

0.0064549

16

Social system (SS)

0.077798

Attitude towards change (TC)

0.282984

2

0.0220156

12

Rational Relationships (RR)

0.059955

4

0.004664

17

Homogeneity level (HL)

0.141077

3

0.010975

14

Nature & Norms (NN)

0.515984

1

0.040142

8

Communication (CM)

0.305571

Mode of Communication (MC)

0.310242

2

0.09480

4

Understandable communication(UC)

0.494763

1

0.151185

2

Communication channels (CC)

0.133594

3

0.04082

7

Networking (NW)

0.061401

4

0.01876

13

Time (TM)

0.491839

Introduction timing (IT)

0.179611

3

0.088339

5

Development time (DT)

0.063241

4

0.031104

10

Progressive Technology (PT)

0.479702

1

0.23593

1

Adoption rate (AR)

0.277445

2

0.13645

3

The results have been compiled as follows:

From the AHP analysis, it is found out that time is the most critical dimension followed by communication, innovativeness and social system.
  • If considering innovativeness as a primary dimension, then the highest local weight is of compatibility followed by relative advantage.

  • If considering a social system as the main dimension, then the highest local weight is of rational relationships followed by nature and norms.

  • If considering communication as a main dimension than the highest local weight is of understandable communication followed by mode of communication.

  • If considering time as a main dimension than the highest local weight is of progressive technology followed by adoption rate.

  • When considering a global weight then progressive technology, Understandable communication, Adoption rate, Mode of Communication, Introduction timing, Compatibility, Communication channels, Nature & norms have relative higher weight than other determinants.

Result and discussion

Due to globalisation, there is a rapid technological change, shorter product life cycles, environment and social issues. Thus, organisation are under tremendous pressure to incorporate changes in their business practices. Confronted with a progressively evolving environment, organisation are now dependably concerned about new product development. This could bring a change ranging from slight to moderate to extensive or the continuum, or even result in absolutely new product offerings. In-spite, new product development gives a competitive advantage to the organisation, but it is also sensible to consider the future longevity of technologies. There two problems that faced by any product innovation organisation are, i) whether the altered/new product would be acknowledged by the consumer(s), and ii) how rapidly would the product be recognised by the consumers(s). While the first relates to what is alluded to as diffusion, the second relates to what is known as adoption. Dissemination and adoption were studied by many researchers like Kotler et al. (2003), Zikmund & D’Amico (1993), Baker (1999), Kinnear et al. (1995), Winer (2007), Tidd (2001), Cooper (2001) and Hoffman (2005) but the literature is highly fragmented and deals with only a few aspects of it. Although Roger’s studies on diffusion and adoption have introduced us with four dimensions of diffusion and adoption. But Roger’s studies do not give the information on (i) how these dimensions can be achieved; (ii) what are the determinants that help in making these dimensions; and (iii) what is the relative significance of these dimensions and determinant. So that decision makers of organisation can set their priority. This study provides an understanding of dimension and determinants for diffusion and adoption of product innovation with the help of AHP.

In this study, we have developed an input and output model of diffusion and adoption using literature review that can be helpful for organisations in identifying the gaps, flow of information and resources between the desired and the current conditions of diffusion and adoption as well as in identifying the areas of improvements. Next, we have taken 4 dimensions of diffusion and adoption given by Rogers (2003) i.e. innovativeness, social system, communication and time which include all the broad aspect of diffusion and adoption. Then with the help of literature review, we have identified 17 determinants of these four dimensions that influence diffusion and adoption of product innovation, and they are compiled in tabular form. After that hierarchical model is developed and this hierarchical model of diffusion and adoption of product innovation can permit managers/decision-makers to effectively watch the impact of the progressions in the upper levels of the criteria with respect to the lower levels changes. Then we are using AHP approach in which we have done a pair wise comparison of each dimension and their determinants and then calculate their local and global weights. According to calculated weight, local as well as global ranking has been done that shows the relative importance of each dimension and determinants. It will help decision-makers/managers to structure their issue by concentrating on its contrasting aspect instead of concentrating on only one or two aspects. Results will be useful especially for SMEs that are experiencing different sorts of asset limitations, as it will help in figuring appropriate strategies for optimum utilisation of resources.

Managerial implications

Diffusion and adoption of an innovative product not only increases profit for an organization but also emphasise on consumer’s demand. It’s a unification of the economic as well as social objectives. Following are the managerial insights that emerge from this study and are given in Table 9.
Table 9

Managerial implications of the proposed research

S. No.

Finding/ learning

Task/s

Next course of action

Next operational action

Benefits/Outcomes

Beneficiary/Beneficiaries

1.

Input and output model of innovation management processes

-Review of article & past literature

-Understanding experiences through case studies

-Expert opinion

Comprehension of goals and objectives

- Identify gaps in organisational innovation processes

-Identify areas of improvements.

-Improve flow of resources and information

-Helpful for organisations in identifying the gaps, flow of information and flow of resources between the current and the desired conditions of diffusion and adoption

-Helps in identifying the areas which require improvements

Research team, top management, operation management team, product developers & designers,

Marketing professionals, advertising experts, science and psychology experts, consumers, stakeholders etc.

2.

Considering innovativeness, social system, communication and time as main factors/ dimensions given by Rogers (2003)

-Review of article & past literature

-Expert opinion

Learning of diffusion and adoption dimensions from different perspectives

-Recognition was given to the main factors that need to be tackle for efficient diffusion and adoption

All the four dimensions cover a broad spectrum of diffusion and adoption

Research team, top management, operation management team

3.

Identifying sub-factors/ determinants related to four dimensions of diffusion and adoption of product innovation

-Literature review

-filling of questionnaire

-group and expert discussion

In-depth learning about the relevance of dimensions and their correlation

-Identify sub-factors which directly or indirectly influences diffusion & adoption

- Categorise and tackle them as per the requirement

-Clarity on diffusion and

adoption process

- Clarity about the relation between dimensions and their determinants

-Clarity about how to set up a hierarchy of different dimensions and determinants

Research team, top management, operation management team, product developers & designers

4.

Establishing hierarchy of goal, objectives, dimensions and determinants

-Taking research team and expert suggestions/opinions

-Developing different framework

-Discussion with focus group and experts

-selection of best framework

Learning about the different levels of dimensions that influence diffusion and adoption

- Develop an effective plan considering factors and sub-factors

-A hierarchical model that depicts diffusion and adoption

-Clarity about the levels of hierarchy

-Clarity in the complex relationship between the dimension

Research team, top management, operation management team, product developers & designers, Marketing professionals

5.

Using AHP approach

-Taking expert opinion using AHP questionnaire

-Data collection and analysis

Finding of the different weight value of each dimension and determinant

-Judge relative importance of each factor and subfactor

-Assign priority according to the global rank

-Ranking of each dimension and determinants.

-The relative importance of each dimension and determinant w.r.t. Diffusion and adoption of product innovation

Research team, top management, operation management team, product developers & designers, Marketing professionals, advertising experts, science and psychology experts

6.

Planning and implementation of AHP results

-Correct depiction of results

-Reframe results according to organisation

Learning about the result interpretation and the relative importance of each dimension and determinant

- Develop

strategic and tactical plans

- Improving the product innovation as per requirements

- Maintain relative

advantages towards achieving a competitive edge over competitors

-Helpful for the MSMEs that has various kinds of resource constraints.

- Provide benefit in strategic planning

- Help in deciding the dimensions that should incorporate in product while designing

-Help in deciding the level of customization in the product.

Research team, top management, operation management team, product developers & designers, Marketing professionals, advertising experts, science and psychology experts, consumers, stakeholders,

Conclusions

It’s already established that diffusion and adoption is one of the most critical stages of product innovation. So, this study proposed a comprehensive framework to identify dynamics of diffusion and adoption issues from four different perspectives, namely, innovativeness, social system, communication and time. In this study, we have identified 17 sub-factors of these four dimensions/ issues which are justified from the references of different studies as given in Table 1.

An AHP (MCDM technique) has been proposed in this study to recognise the relative significance of different dimensions and determinants that are important for the diffusion and adoption of product innovation. It is found that compatibility and relative advantage have higher weights when considering innovativeness, rational relationship and nature and norms have higher weight when considering the social system, understandable communication and mode of communication when considering communication, progressive technology and adoption rate have higher weight when considering time. When considering global weights then progressive technology, Understandable communication, Adoption rate, Mode of Communication, Introduction timing, Compatibility, Communication channels, Nature & Norms have relative higher weights than other determinants. Therefore, an organization should give priority to these determinants and aligned their objectives while considering these dimensions and determinants which help in overall diffusion and adoption of product innovation.

Limitations and scope for future research

Seventeen major determinants Critical in this study under four major dimensions; hence, more determinants can be considered in future research. These determinants will have regional biases. The experts were from northern India, and we assume that their opinion is valid for the industry. For further validation, statistical techniques could be used for these determinants. Also, AHP has the constraint of capturing vagueness which is associated with the judgments of experts. So, fuzzy AHP which can give an opportunity to the experts to express their experiences through natural language can also be considered in future studies.

Declarations

Acknowledgement

The authors extend thanks to Dr. Sanjay Kumar, Dr. Sunil Luthra and anonymous experts for sparing their precious time and giving useful comments and suggestions that helped in improving the quality of research. The authors would also like to acknowledge Jamia Millia Islamia and MoMA, Govt. of India for funding this research through MANF (Maulana Azad National Fellowship).

Authors’ contributions

BM is pursuing her doctoral research under the guidance of AH. AH develops a framework, design a questionnaire and finalize the dimensions and determinants with the help of experts. Under his guidance BM collects, analyze and interpret the data. Both authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
Department of Mechanical Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia

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