Under what conditions are HR professionals more open to innovation adoption?
Master thesis, Msc Human Resource Management
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Under what conditions are HR professionals more open to innovation adoption?
Master thesis, Msc Human Resource Management
University of Groningen, Faculty of Economics & Business Economics
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An increase in use of technology and in literature could use some structure, research and understanding, because it has led to different innovative practices which are not always adopted by HR professionals. It is expected that the technological nature of a practice,
influences the willingness of an HR professional to adopt the innovative practice. My research has shown that the technology level within an HR innovative practice, regardless of the
cognitive style of the professional, does not seem to play a role in an HR professionals’ willingness to adopt the innovation. Analytical individuals are more likely to adopt a general HR innovative practice than intuitive individuals as measured through the REI-20. Multiple HR professionals have received a short survey in order to get insights into an HR
professionals view and to help organizations understand why their HR professionals are willing to adopt some practices, but not others.
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Introduction
Increasing global competition and rapid technological changes have made it possible to replace traditional practices with innovative ones. It shows that organizations’ have increasingly abandoned these traditional Human Resource (HR) practices and replaced them with innovative ones as well (Cozzarin & Jeffrey, 2014). These changes force organizations to better leverage their human capital (Wolfe, Wright and Smart, 2006), while strengthening their competitive advantage. Adoption, as related to research about innovation adoption, is defined as “the decision to make full use of an innovation as the best course of action available” (Vargas, Yurova, Ruppel, Tworoger & Greenwood, 2018, p. 3048). It is assumed that when an organization adopts a practice, it is because they see benefits for the
organization, such as efficiency or economic gains (Collings & Dick, 2011). Understanding these barriers to innovation can help an organization to overcome those barriers and it can help with clarifying the innovation adoption process within the organization (Hueske & Guenther, 2015). In general innovation adoption research, it becomes clear that a consumer’s resistance to innovation or technology is influenced not only by context factors but also by individual barriers (Mani & Inès, 2018; Heidenreich and Handrich, 2015). The following research question is derived from interests in these context and individual factors: Which factors are influencing (HR) innovation adoption?
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individuals that are more open to change (Armstrong & Hird, 2009). Whereas analytical individuals are in general expected to be more against innovation, due to being more inclined to keep working between the lines (Armstrong & Hird, 2009). However, being an analytical individual could indicate that this individual will be quicker in seeing the benefits and
opportunities of an analytical innovation, whereas an intuitive individual could be more open to gut feeling decision making that is more focused at intuition (Barkhi, 2002). It could be expected that the different types of HR innovations could be considered as more efficient by different HR professionals.
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In this research paper, I will try to add information to the existing questions in literature in order to get more knowledge about the conditions under which (HR)
professionals are open to innovation adoption. Through relating the theory about HR practices to the innovation adoption theory, I will aim at adding information to the existing literature about the presence and implementation of HR practices within an organization through the eyes of HR professionals. Knowing which environments and personal characteristics support innovation adoption within the HR department, will offer an organization more possibilities for an increasing interest in innovation adoption within an organization. Besides, knowing which characteristics within an innovation to focus on can decrease the endless possibilities of innovations to implement. Having this knowledge can simplify the steps an organization should take in reaching successful innovation adoptions in order to survive in their
competitive environment. There is still much to learn about the attributes that are most critical in innovation adopters or in the innovation itself (Yost et al., 2011; Guest, 2011). To study the proposed research questions, I used survey research to collect insights regarding the levels of technology within HR innovative practices and the willingness of HR professionals to adopt these (HR) innovations.
Theory
Innovative adoption
Innovation researchers have long realised the critical role of the individual in the innovation adoption process (Murphy and Southey, 2003). Before an organization can use an innovation, they, or often more specifically someone within the organization, has to adopt it. Within the HR department, the HR professionals are the individuals that determine which HR innovations to adopt. The adoption of an innovation results in the implementation of a
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& Aravind, 2012; Wischnevsky & Damanpour, 2006). An innovation itself can be characterized as “the full range of activities and processes involved in commercially
exploiting ideas “(Plewa, Troshani, Francis & Rampersad, 2012, p. 748). Innovations that are specific for the HR department could also be characterized as managerial innovations, since they can change how managers do what they do (Damanpour and Aravind, 2012). This indicates that within the HR department, this could for example lead to professionals changing the way in which they perform job interviews or performance evaluations.
Over the years many innovations have been created, which differ a lot in their characteristics, radicalness (Wischnevsky & Damanpour, 2006), or perceived usefulness (Plewa et al., 2012). The latest HR trends are mostly focused at increasingly using technology in HRM. This is probably due to the perception of technology adoption being inevitable in the current competitive contemporary business world (De Mauro, Greco, Grimaldi & Ritala, 2018). Technological advances have eroded physical, cultural, economic, and political
borders (Baldwin, Gray and Johnson, 1995) and require enhancing, renewing, and revitalising existing work systems, services, and products (Shipton, Sparrow, Budhwar & Brown, 2017). The advances in technology have changed the nature of work and workers within every level of an organization.
Recognizing that underutilization of IT continues to be a barrier in realizing the benefits, information science scholars have called for research to examine the factors that influence the use of IT so as to facilitate better access to, and sharing and processing of, information (Maruping, Bala, Venkatesh & Brown, 2017, p. 624). “The continuing quest to ensure user acceptance of technology is still an ongoing management challenge” (Williams, Rana & Dwivedi, 2015, p. 443).
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technology adoption depend on the nature of technology, suggesting that a one-size-fits-all approach is unsuitable and that factors driving the adoption of specific technologies require specific attention (Johnson, 2010; Plewa et al., 2012). Therefore, it could be concluded that it is still relevant to look at the differences in technology use between HR practices. To test this assumption within this research paper, I have formed the following proposition: “The
technological nature of the HR innovative practice influences the willingness to adopt the HR practice”. The aim of this proposition is to investigate if the technological nature of an innovation could be a resistance factor in the innovation adoption process.
Perceived Innovation Effectiveness
An HR professional's personal perceptions of innovations determines, partially, his or her openness to the (technological) innovation. Early adopters of a practice will be those that perceive the practice as meeting a particular technical need and are those that will be
motivated to adopt for reasons of efficiency, whereas later adopters will seek to obtain
legitimacy instead of looking at the impact on organizational performance (Collings and Dick, 2011). Based on this statement from Collings and Dick (2011) it can be assumed that before HR professionals will adopt an innovative HR practice, either as early or as later adopters, they first need to perceive the practice as being efficient or effective. Wood et al. (2014, p. 254) support this assumption with the statement that “for entrepreneurs to act on an
opportunity idea, they must believe that doing so will result in a desired end state”. Firms adopt innovations to gain first or early mover advantages that will lead to superior
performance (Wischnevsky & Damanpour, 2006).
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an HR professional needs to see some sort of benefit, which is translated into effectiveness, of an innovation, before he or she is willing to adopt the innovation. I have wondered if this is also the case for technological HR innovations. The presented arguments for the effectiveness of an innovation, as perceived by the HR professionals, show proof for its moderating role.
The role of a person’s Cognitive Style
One can argue that when diving deeper into the factors that determine an HR professional's openness to technological innovations, it becomes visible that one’s personal characteristics determine one’s personal perceptions of those innovations. It is proven that “practitioner variables (education, beliefs, assumptions) determine information search behaviours, information use and ultimately, the HR practice adopted” (Murphy and Southey, 2003, p. 75). An HR professional that possesses, for example, creativity relevant skills, intrinsic motivation, or the ability to gain support and credibility, will be more likely to consider adopting high performance work practices (Murphy and Southey, 2003). Multiple researchers (Batra & Vohra, 2016; Sadler-Smith and Badger, 1998) have investigated if the cognitive style, or the personality, of an individual, influences his or her likelihood of
showing innovation adoption. Before accepting that an HR professional will see a practice as being effective, it seems relevant to look at the individual characteristics of the HR
professionals that could influence their willingness to adopt an innovation.
The HR professional's cognitive style is such an individual characteristic that
influences an HR professional’s decision of (HR) innovation adoption. In this research paper, I focus on the cognitive style of HR professionals as a tool to represent the influence of their personal characteristics. The cognitive style of an individual shows the individual differences in the way people process information and arrive at decisions based on the gathered
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has proven that rationality is among other more strongly and directly associated with ego strength, openness, conscientiousness, open-minded thinking, and superior reasoning, whereas experientiality is most strongly and directly associated with for example extraversion,
agreeableness, emotional expressivity, and superstitious beliefs (Shirzadifard, Shahghasemi, Hejazi, Naghsh & Ranjbar, 2018). “An individual’s openness to experience is crucial for being innovative” (Batra and Vohra, 2015, p.771). However, individuals that prefer innovations, like entrepreneurs, tend to be more intuitive and less analytical than non-entrepreneurs (Armstrong & Hird, 2009). Besides, it is also assumed that analytical
individuals need more elaborate reasoning (Shirzadifard et al., 2018), leading to the possible conclusion that it may take analytical individuals more effort or time to see an innovation as effective. The differences between the two processing systems may lead to different feelings, thoughts, and behaviors (Shirzadifard et al, 2018). Marks, Hine, Blore & Phillips (2008, p. 50) support the use of one’s cognitive style as moderator in the innovation adoption process, since “individual differences in rational and experiential cognition can provide a valuable
moderator variable with which to investigate receptivity to different kinds of ideas”.
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individuals favour face-to-face methods (Barkhi, 2002). Besides, it is argued that managers who prefer intuition-based hiring are likely to be feelings-based thinkers (Miles and Sadler-Smith, 2014). Analytical individuals will react better to analytical tasks, whereas intuitive individuals will react better towards intuitive tasks. The following hypotheses are formed to represent the expectations about the moderating influence of the cognitive style on the perceived effectiveness of a technological HR practice:
H1a: The more analytical an individual, the more likely he or she is to perceive a ‘technological’ innovative HR practice as being effective
H1b: The more intuitive an individual, the more likely he or she is to perceive a ‘non-technological’ innovative HR practice as being efficient.
The direct relationship between the level of technology and innovation adoption is thus, not only supported by existing theory, but it also includes an expected interaction effect. The direct and indirect effects are all expected to together determine the decisions about innovation adoption from an HR professional. All relationships are (partially) determined by the mediating influence of the perceived effectiveness of the innovation. The following hypothesis is formed to represent this expected mediation influence:
Hypothesis 2: The willingness to adopt an innovative HR practice is indirectly affected through the perceived effectiveness of the HR practice.
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FIGURE 1 Conceptual model
Method
Data collection
In order to test the proposed hypotheses, I have collected data for this research paper through two separate surveys. I created the surveys with help from the Qualtrics website and I have followed a quantitative research approach.
I have created the first survey in order to collect information about the level of
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trends and HR practices. When encountering an HR practice, I looked this HR practice up on their website and on google in order to find out how people write about these practices and if they can thus be considerate as innovative and relevant practices. The practices, which are visible in table 1, have different functions within the HR department, to ensure a diversity in the HR practices. The Dutch definitions as used in the survey and their average level of technology based on the responses of the HR students are visible in Appendix 1.
TABLE 1 HR practices
HR stage HR practices
Recruitment Niche social media ads, inclusive job ads & VR tours
Selection Realistic job previews, interview chatbots, situational judgment tests, low effort computerized judgment methods, AI video interviews, group interviews
Training Gamification, two-way mentoring/teaching program, interactive guidance methods
Evaluation & Rewards 360° feedback, upward feedback, high fives
Health & Satisfaction Biometric health screening, health risk assessment, menopause-friendly environment, creative job titles, sponsored volunteering, unlimited vacation days
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with people that work in an HR job via LinkedIn, the alumni network, personal connections, and company emails found online. These specific individuals are targeted, because these are the people that have to make decisions about the HR practices that are used within their organization. It was therefore expected that these individuals can answer the questions related to the subject of this research paper. The respondents were asked for each of the HR practices, if they see the practice as being effective and if they are willing to adopt the practice. After answering these questions, 20 questions, based on an existing cognitive style measure, remain to determine the cognitive style of the respondent. Finally, it is relevant to include some questions about demographics within the survey to collect extra information about the respondents and to check for underlying influences of demographic characteristics. I handled the gathered data in accordance with the existing privacy laws. Therefore, no names or other data that could be directly used for identification is collected. Most questions are based on a 5-point Likert scale that ranges from ‘completely agree’ to ‘completely disagree’. Using the same scale for all questions makes it easier for the respondents to go through the
questionnaire, which decreases the response time. The question about the age of the respondent is the only open question within the questionnaire.
Measures
Likelihood to adopt How likely it is that the respondent will adopt the HR innovative
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statement: “I am open to adopting the described HR practice within my organization”. The benefit of using a Likert scale over a binary answer is that the respondents can give a reaction about the extent to which they think that the statement about an HR practice is true or false.
Perceived effectiveness The mediator variable represents the extent to which a
respondent sees the HR practices as effective. The decision has been made to use only one question for the mediator variable and one for the dependent variable in order to create a survey that is not too long for respondents. Building forward on staying consistent, a 5-point Likert scale with the anchors 1 (totally agree) to 5 (totally disagree) is selected for the mediator variable as well. For every HR practice, the respondent will have to give an answer to a Dutch version of the following statement: “I perceive the described HR practice as effective”. I expect that respondents are more inclined to answer a questionnaire when they know that it is not too long.
Level of technology The independent variable is represented by the level of
technology within an HR innovative practice. I have used the average level of technology for every HR practice as measured through the responses from the first survey. The level of technology within a practice can range from 0 to 100, where 0 represents no technology used at all and 100 represents a practice that is completely technology driven. The respondent can move a slider between the two anchors. The number the respondents places the slide at represents the “level of technology” the respondent thinks fits best with the HR practice. The definition from Marler and Parry (2016) about the use of electronics in HR practices is used in the first survey to clarify what the level of technology within an HR innovative practice should indicate. The definition is translated to Dutch and simplified for a general
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actual HRM activities (e.g. policies, practices and services) through coordinating and controlling individual and group-level data capture and information creation and communication within and across organizational boundaries”.
Cognitive style scores The variable that moderates the relationship between the level
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individuals to ensure that I did not make any mistakes with the translation. The REI-20 consists of 20 questions (Cronbach’s α = .792), where 10 of them are used to calculate the rationality score (α = .843) and where the other 10 questions are used to determine the experientiality score (α = .777). Which questions count for which cognitive style and which questions I have reverse coded, is visible in Appendix 2 as well.
Data analysis
I have analysed the gathered data with the help from the software packages ‘IBM SPSS Statistics 25’ and ‘R Studio’.
Before starting the analysis, the average levels of technology, as calculated from the first survey, have to be merged with the dataset from the second survey. The dataset is restructured into a multilevel dataset (long format), where every respondent has 21 sublevels that represent the HR innovations (a rating for every HR innovation). The new dataset consists of 1050 nested observations and offers the opportunity to perform a multilevel regression analysis (observations nested in rater). After doing a regression analysis, I performed an exploratory descriptive analysis that provides me with deeper insights in the influences of the cognitive styles at the level of technology. One can get a deeper
understanding of the relationship between the two variables through looking more closely at reactions towards the practices, by for example looking at the respondent’s favourite
practices. Clusters are formed to divide the respondents based on how high or low their rationality and experientiality scores are.
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of effectivity were correlated with increases in the likelihood to adopt the innovation. Therefore, it is not possible to examine the mediation effect of effectiveness for the
(technological) innovation adoption. A possible explanation for the high correlation between the two variables is that they are measured at the same time for every HR practice. The data supports this correlation by the fact that when looking at the data it becomes clear that the scores for the effectiveness of an HR practice and likelihood to adopt that specific practice are often quite similar.
TABLE 2
Descriptive statistics and intercorrelations
Notes. N=1050. † P<.10, *p<.05, **p<.01, ***p<.001.
In order to investigate the separate effects of effectiveness and innovation adoption, effectiveness can be considered as a dependent variable in a second model in order to measure the relationship between effectiveness, level of technology, and one’s cognitive style. This results in a revised conceptual model in which effectivity is removed from the model. The revised model, which is a moderation model, is visible in figure 2 and represents the model that is used in the mixed effects regression analysis. The new hypotheses are:
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H1b: The more intuitive an individual, the less likely they are to adopt a ‘technological’ innovative HR practice.
H2a: The more analytical an individual, the less likely they are to adopt an innovative HR practice.
H2b: The more intuitive an individual, the more likely they are to adopt an innovative HR practice.
FIGURE 2 Revised model
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Results
Descriptive & Data preparation
In total, 50 respondents, who have a job in which they can make decisions about HR
practices or work intensively with those practices, have (almost) completed the survey. The respondents show a high level of diversity in their demographic characteristics. There are 17 men and 33 women, whose ages range from 21 to 59 years old. The mean and standard deviations that represent the descriptive information for the respondents are represented in table 2. All individuals that have almost completed the questionnaire and that provided some demographic information, have been included in the analyses. The results below are based on the revised model as depicted in the method section.
Regression analysis
I performed a mixed effects regression analysis. Within this analysis, the likelihood to
adopt an innovation is the dependent variable, the level of technology is the independent variable and the moderator is represented by the experientiality and rationality scores. The two interaction effects that are included represent the moderation effect of the cognitive style variables. The results of the regression analysis are visible in table 3. Table 4 shows the results from a mixed effects regression analysis in which the perceived effectiveness is the dependent variable instead of the innovation adoption variable.
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The effect of the rationality (B = -.01, t = -1.11, p = .27) and experientiality scores (B = -.00, t = -.19, p = .85) show no moderation effect on the innovation adoption decision in relation to the technological nature of an innovation. The effect of the rationality (B = -.01, t = -.97, p = .34) and experientiality scores (B = .01, t = .93, p = .36) also show no moderation effect on the relationship between the technological nature of an innovation and its perceived effectiveness. In both models, there is no significant moderation effect. Therefore, it could be concluded that hypotheses 1a and 1b are not supported.
The effect between rationality and the likelihood to adopt an innovation in general is significant and positive (B = .21, t = 2.04, p = .05), which is not as expected. This indicates that when an individual has a higher rationality score, he or she is more likely to adopt an (HR) innovation. The effect between experientiality and the likelihood to adopt an innovation in general shows against expectations, a negative significant effect (B = -.27, t = -2.61, p = .01), indicating that when an individual has a higher experientiality score, he or she is less likely to adopt an (HR) innovation. It is noticeable that the effect of rationality on perceived effectiveness is not significant in the regression analysis for the perceived effectiveness as a dependent analysis (B = .07, t = .81, p = .42). This result indicates that even though an increase in rationality score does increase the likelihood to adopt the innovation it does not necessarily increase the perceived effectiveness of that innovation. The direction of the influences are against expectations as well, indicating that experientiality has a negative significant influence (B = -.19, t = -2.24, p = .03). Rationality has a positive influence, where experientiality has a negative one. These results indicate that both hypotheses 2a and 2b from the revised model are not supported by the results, since the directions work in opposite ways as would be expected from theory.
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innovation, is not significant (B = -.01, t = -1.54, p = .13). The influence of the level of technology on the perceived effectiveness of that innovation is not significant either (B = -.01, t = -1.48, p = .15). The lack of significance shows that there is no clear relationship between the two variables. It can be concluded that the proposition about the role of the level of technology within the innovation adoption process cannot be confirmed. The lack of
interaction effect between one’s cognitive style and the level of technology supports this lack of influence from the level of technology within the innovation adoption process.
The covariate that represents the sector in which the organization the respondents works at is active has a significant effect as well (B = .05, t = 2.63, p = .01). This indicates that when a respondent works in a certain sector, he or she is more likely to adopt the innovation.
Table 3
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Table 4
Mixed effects regression analysis likelihood to adoption
Exploratory descriptive analyses
The same data as has been used within the regression analysis is used within the exploratory descriptive analyses. This indicates that the observations that show an already implemented HR practice are filtered out of the dataset. A total of 838 observations is left to work with in the following analyses.
The role of technology The level of technology within an HR innovative practice does
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Adding percentages to the figure can support the visible representation of figure 3, but this does result in a more chaotic graph and that is why I placed the graph with percentages in it in Appendix 3. Within the upcoming figures, the x-axis represent the level of technology within a practice. The more to left a practice is located, the higher its level of technology, whereas the more to right a practice is located, the lower its level of technology.
In figure 4 a lower average adoption level represents a higher willingness to adopt. The figure supports the conclusion that no clear conclusions can be formed about the level of technology. Taking no personal characteristics into account, it seems that most attractive innovations are the realistic job preview (54), the 2-way mentoring program (33) and 360-degree feedback methods (32). The chatbot interviews (65), biometric health screening (48) and niche job advertisements (60) are the least likely to be adopted by an HR professional. The top 3 most likely to get adopted innovations do have a somewhat lower average level of technology than the top 3 least likely to get adopted innovations. However, these differences are not that large, since all 6 levels of technology are quite close to the middle of the spectrum (50).
From figure 3 it becomes clear that the practice with the highest level of technology (VR) does get a lot of enthusiasm, represented by around 70% of the reactions being a positive reaction and a lower average response value in figure 4 in comparison to the other average responses within the figure. The second highest technological innovation gets a lot less enthusiasm, since only around 40% of the reactions are positive, and gets a somewhat high average of willingness to adopt in figure 4.
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technological ones. This again supports the results from the mixed effects analysis that the level of technology plays no clear role in the willingness to adopt an innovation.
FIGURE 3
Responses likelihood to adopt scaled to 100%
FIGURE 4
Average responses likelihood to adopt
Forming clusters The revised conceptual model investigates personal characteristics
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independently of each other. However, it may be interesting to look at the relationship
between the two scores as well. To dive deeper into the influences of the cognitive styles of a respondent, two clusters have been made. A small selection of the respondents have been selected for a certain cluster. Highly analytical and the highly intuitive individuals are the subject of the clustering method. A person is analytical when he or she has a high rationality score and a somewhat lower experientiality score. A person is intuitive when he or she has a high experientiality score, but a somewhat lower rationality score. The boundaries are set at 3.6, because this is right in the middle of the number of scores and at 4, because this is about ⅔ of scores. The group of intuitive individuals is still quite small, with only 11 individuals in it, but it does represent 22% of the responses. The second cluster contains 34 (68%) of the respondents that are considered to be analytical individuals. In order to do some more
exploring, I have also created an extra cluster in which both scores are high and compared the information about it to a cluster in which both cognitive style scores are low. However, there are no clear differences and the results from the clusters are not adding any new information next to information one can get from the rationality and intuitive clusters. The figures from these two clusters are therefore not included in the text. I have placed some graphs that represent the enthusiasm of the respondents within those clusters in Appendix 4 in order to make it possible for the reader to form conclusions for themselves.
Analytical individuals I created multiple figures that have helped to form conclusions
about the analytical individuals. From figure 5 it can be concluded that the analytical people do see some positive and some negative things in every practice. This could imply that analytical individuals are open to technological innovations as well.
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the least likely to get adopted by an analytical individual, are unlimited vacation days (24), biometric health screening (48) and performing job interviews with a chatbot (65), which are also not including any technological innovations. Moving forward with figure 6, one can see that the 3 most technological innovations do get more positivity than the least technological innovations, which is represented with lower average responses in figure 6. Therefore, it could be concluded that it is possible that analytic individuals are, maybe even more, into technological innovations and that they are rather willing to adopt them than the
non-technological innovations. It should be noted that these are not strong differences but based on the exploratory descriptive analysis it can be concluded that there is a possible relationship between being an analytical individual and adopting technological innovations.
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FIGURE 5
Responses likelihood to adopt analytics
FIGURE 6
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FIGURE 7
Percentages likelihood to adopt analytics
Intuitive individuals I created multiple figures that have helped to form conclusions
about the intuitive individuals as well. From figure 8 it can be concluded that intuitive people do see some positive and some negative things in every practice as well. This could imply that intuitive individuals are open to technological innovations as well.
The top 3 innovations that are most likely to get adopted by intuitives are the inclusive advertising (42), realistic job previews (54), and 360-degree feedback (32), which includes no high technology driven innovations (Figure 9). The top 3 innovations that are the least likely to get adopted by an intuitive individual, are creative job titles (16), health risk assessment (41) and AI video analysis (77), which does include a high technology driven innovation (Figure 9). Moving forward with figure 8, one can see that the 3 most technological
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quite small and that a cluster from a larger sample size could show different results. Based on the exploratory descriptive analysis it can be concluded that there is no clear relationship between being an intuitive individual and adopting technological innovations.
Having the respondents see some positivity in every practice regardless of the level of technology shows that intuitives are open to change. Figure 10 shows that the ‘totally agree’ together with the ‘somewhat agree’ responses show that over half (57.15%) of the responses are positive towards being willing to adopt an innovation in general. Only 18.29% of the responses can be categorized as negative and the rest of the responses are neutral. Based on the graphs, it could be concluded that there could be a positive relationship between being an intuitive individual and being open to adopting innovations in general.
FIGURE 8
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FIGURE 9
Average response values intuitives
FIGURE 10
Percentages likelihood to adopt intuitives
Discussion
Technological nature of innovation
The results show that no clear conclusions can be formed about the level of
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practices and low technology driven HR practices. Every practice can expect some
willingness to get adopted by some individuals, regardless of the personal characteristics of that individual. Based on the result I have concluded that the level of technology within an innovation does not play a role in the innovation adoption process.
Having the willingness to adopt responses change for every HR practice could indicate that there are other relevant differences between the practices that are the reason for the differences in the HR professional’s choice to adopt the innovation or not. The results support the assumption from Johnson (2010) and Plewa et al. (2012) that there is no one-size-fits-all approach and that specific attention is needed for technology adoption. It can still be expected that the adoption of the innovation still depends on the nature of the innovation, and more specific on the nature of technology. An example is visible at comparing the results of the two innovations with the highest level of technology, VR tours and AI video analysis. Both tools can be used in the selection procedure in addition to other personal or impersonal selection tools. However, one could think that the second innovation may be more radical and invasive since a technological tool makes decisions about the future of an individual, whereas the first tool could be considered at making it easier for an applicant to know what to expect. When building forward on the argument of Wischnevsky and Damanpour (2006), it could be
concluded that AI possibly gets more negative responses due to being more radical. AI would therefore need a different approach to reach a successful implementation.
Technology and big data has gotten a large role within the functioning of
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technology and that reasons for resistance have shifted. Adding new knowledge about barriers to innovation adoption in general is useful in improving the knowledge about the innovation adoption process. Measuring the influence of the level of technology has added knowledge about the differences in technology over the last few years. However, to form reliable conclusions about the innovations, more research about the innovations, their nature, their radicalness, and their role within the organization is needed.
HR professional’s cognitive styles
The effect of the rationality and experientiality scores show no moderation effect due to the lack of significance of the relationship. These results suggest that there is no influence of being an analytical or intuitive individual on adopting a technological innovation. The results do show a direct relationship between an HR professionals’ rational or experiential score and their receptivity to innovations regardless of their technological nature. The results show that when you are an analytical person, you are more willing to consider adopting an innovation than an intuitive person. The regression analysis argues an individual with a higher
experientiality score will be less likely to adopt a practice than someone with a lower experientiality score, but the exploratory descriptive analysis does show that intuitive individuals in general are open to innovations.
The argumentation from Marks et al. (2007) that differences in rational and
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place for this as well, like HR professionals being socially influenced by their environment or the media richness of the communication tools (Barkhi, 2002). It was proven that managers may base selection choices more on intuitive judgment, rather than using objective evidence-based tools, since intuitive judgment methods can take context into consideration (Miles and Sadler-Smith, 2014). When having a closer look at some of these more objective evidence-based tools for hiring, like an AI program that analyses video interviews and getting interviewed by chatbots, one can conclude that this seems to be true, since these are on average not likely to get adopted.
Batra and Vohra (2016) argue that missing the personality characteristic ‘openness to experience’, which is often a characteristic of analytics, could be a reason for resistance to innovation adoption. However, since rational individuals are open to adopting innovation in general and only show no significant effect for technological innovations, this is not expected to be an issue. The fact that analytics are open to innovation adoption is probably because they do possess this characteristic (Batra and Vohra, 2016), which can overshadow their normal aversion towards innovation adoption as suggested by Armstrong and Hird (2009).
My research paper supports the existing theory in the idea that personal characteristics of individuals, like HR professionals, determine which innovation they are most likely to adopt. My research paper does not show any clear understanding about the role of technology within this adoption process. For now I would have to conclude that there is in this moment of time no influence of technology, but a larger sample size and in a different time period, where not most individuals work at home with technology driven sources as main communication tools, could lead to different results.
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When implementing an innovation within your organization, the HR professional should consider the factors that influence that specific innovation and not generalize it to the factors that influence technological innovations in general. When an organization wants to become more innovative it could be more attractive to hire an analytical individual, because it can expect more resistance from intuitive individuals. However, the resistance and openness from the two types of cognitive styles do differ for different kinds of innovation. This indicates that, if multiple options are available, it could also be an option to choose innovations that show a fit to the cognitive style of the professional.
Limitations & future research
There are different factors that can determine the nature of technology that is used within an innovation. Based on the results one can focus more on the type of innovation and on the type of technology used within that innovation, like focussing more on the radicalness (Wischnevsky & Damanpour, 2006) than on just the level of technology. For one to look into the nature of the technology, more specific knowledge about technology is needed. More research about the nature of the different technology is required, because the question still rises why do the same people like certain (technological) HR practices and not others, even technological ones.
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The HR practices that have been used within the questionnaire represent different functions within the HR department. However, there is no further information gathered in the analysis about these roles and their influences on the willingness to adopt the innovation. The specific HR practices and their characteristics were not a point of interest for this research paper. However, it is expected that they can deliver some interesting insights, especially when relating it to the level, radicalness or other characteristics of the technology used within the innovations. The role of the innovation within the HR department could therefore be researched more in the future.
Conclusion
In this research paper, I have attempted to gain knowledge about which factors influence the innovation adoption process. Some interesting insights have been gathered and some ideas for future research have been proposed. The small sample size has led to less reliability within the results, but some conclusions can still be formed. I have concluded that the adoption of an HR practice does depend on the perceived effectiveness of an HR
innovative practice, but that the level of technology does not play a role in the innovation adoption process. The cognitive style of an HR manager/professional does not moderate the influence between the technology level and perceived effectiveness or innovation adoption. However, there is a direct relationship between the cognitive style of an individual and the likelihood of adopting an innovation. Being an analytical individual will increase the
likelihood of adopting an innovation, whereas being an intuitive individual will decrease the likelihood of adopting an innovation. However, from the exploratory analysis it becomes clear that this does differ between different innovative practices. The factors that show these
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innovation from different areas within HR departments. However, new innovations are discovered every day. My research paper has set a good example for focussing more on technology within the HR field, but it is in its infant stage. Technology will be used more and more and to use this effectively within an organization, more, more reliable, and bigger research about this subject is needed for many more successful innovation adoptions that will be needed in the future.
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Appendices
Appendix 1. HR innovative practices
Name Definition Level of
technology
1. Niche social media advertenties
De verspreiding van vacatures op niche kanalen die frequent bezocht worden door de doelgroep zoals Tinder en SnapChat.
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2. Inclusieve job advertenties
Het gebruik van 'inclusief taalgebruik' betekent, dat u een bewuste keuze maakt om woorden te gebruiken die geen groepen mensen marginaliseren die bewust of onbewust worden gediscrimineerd vanwege hun cultuur, ras, etniciteit, geslacht, seksuele geaardheid, leeftijd, handicap, sociaaleconomische status, uiterlijk, of een andere factor die gewoonlijk geen rol zou moeten spelen.
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3. Virtual Reality tour VR kan kandidaten een realistische, virtuele tour door het kantoor geven of de cultuur van het bedrijf verduidelijken.
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4. Realistic job previews RJP's tonen accurate informatie over de positieve kenmerken en potentiële uitdagingen die deel uitmaken van een baan, maar ook duidelijke info over de prestatieverwachtingen en de prestatiemanagement processessen van het bedrijf.
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5. Interview Chatbots Deze HR-praktijk houdt in dat een kandidaat geïnterviewd wordt door een robot die de kandidaat zal beoordelen voordat deze wordt uitgenodigd voor een persoonlijk interview. De robot stelt open vragen, neemt reacties op, bedankt de kandidaat, adviseert over de volgende stappen en beoordeelt het interview.
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6. Situational judgment tests
Deze testvorm presenteert kandidaten met meerdere situaties die ze ook kunnen tegenkomen in hun baan, met behulp van teksten, audio en video en geven opties weer hoe om te gaan met deze situaties. De kandidaten selecteren vervolgens de meest of minst effectieve optie.
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7. 'Low effort' computergestuurde beoordelingsmethoden
Het gebruik van nieuwe technologie en machine learning om door grote hoeveelheden ongestructureerde informatie te gaan, zoals cv's. Op basis van voorgaande onderzoeken kan op basis van reeds beschikbare resultaten van werknemers via datavalidatie kennis en vaardigheden van de beschikbare kandidaten gefilterd worden.
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8. AI ondersteunde video interviews
Artificial Intelligence software biedt de mogelijkheid om videobeelden van een kandidaat te analyseren op basis van bijvoorbeeld de stem en andere verbale reacties, maar ook gezichtsuitdrukkingen, met als doel het meten van (softe) skills van kandidaten.
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9. Groepsinterviews Met groepsinterviews kunnen recruiters een aantal sollicitanten met vergelijkbare skills bij elkaar brengen en hun vaardigheden en competenties "in actie" beoordelen aan de hand van tests, teamopdrachten en functiespecifieke taken.
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10. Gamification Het gebruik van game design op alledaagse activiteiten, zoals het bevorderen van leren en ontwikkelen in een veilige digitale omgeving. Het doel is het verhogen van werkplezier en betrokkenheid door mensen te inspireren om te blijven leren.
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11. Two-way
mentoring/teaching program
Het delen van de kennis en ervaring van de oudere werknemer en de innovatieve kijk van de jongere werknemers, waardoor de ouderen hun pensioen uitstellen en het aantrekkelijk wordt voor jongeren om bij de organisatie te blijven.
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12. Interactive guidance methods
Na het voltooien van de interactieve training hebben medewerkers de mogelijkheid om specifieke onderdelen op te roepen of aan te vragen om bewerkingen opnieuw uit te voeren die ze nog niet voldoende beheersen binnen de beschikbare software. Deze software programma's maken gebruik van virtuele assistenten die de
medewerkers begeleiden.
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13. 360° feedback Binnen deze methodes geven collega's elkaar feedback, met als doel inzage te krijgen in hoe jouw collega's tegen jouw functioneren aankijken.
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14. Upward feedback Upward Feedback biedt de optie om anoniem en vertrouwelijk feedback te geven aan de directe leidinggevende.
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15. High fives High fives kunnen gebruikt worden om waardering voor je collega's te laten zien en om de sfeer op de werkvloer te bevorderen.
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16. Biometrische
gezondheidsscreening
Het meten van bijvoorbeeld de bloeddruk, cholesterol, suikerspiegel, slijm en urine van de huidige werknemers met als doel het identificeren van hoge risico
werknemers en het creëren van preventieve strategieën.
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17. Gezondheidsrisico beoordelingsonderzoek
Het in kaart brengen van onder andere de demografische profielen,
gezondheidsstatus, dagelijkse gewoontes en interesses voor het verbeteren van de gezondheid van de werknemers via online of offline vragenlijsten.
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18. Menopauze-vriendelijke werkomgeving
Een voorbeeld van een werkomgeving die vrouwen in de menopauze op hun gemak stelt, is het gebruik van een 'cafetaria strategie'. De werkgever biedt hen in dit geval secundaire arbeidsvoorwaarden aan die het werken in de menopauze aangenamer kunnen maken.
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19. Creatieve functietitels Het geven of laten kiezen van creatieve functietitels in plaats van de bestaande traditionele titels om onder andere het delen van informatie en unieke ideeën te bevorderen.
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20. Faciliteren van vrijwilligerswerk
Het bieden van de mogelijkheid aan werknemers om een bepaald aantal uren vrij te krijgen om vrijwilligerswerk te doen.
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21. Ongelimiteerde vakantiedagen
Werknemers mogen zelf kiezen hoeveel dagen ze vrij nemen, niet alleen om op vakantie te gaan, maar ook voor ziekte of kort verlet.
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Appendix 2. REI-20/REI-A
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3. I don’t enjoy having to think.
4. I don’t have very strong gut instincts.
5. I enjoy solving hard problems that require lots of thinking. 6. I often go by my instincts when deciding on a course of action. 7. I have no problem thinking things through carefully.
8. I don't like to have to do a lot of thinking. 9. I tend to use my feelings to guide my actions.
10. I think it is foolish to make important decisions based on feelings. 11. I enjoy a challenge that makes me think hard.
12. I'm not that good at figuring out complicated problems. 13. I believe in trusting my instincts.
14. Reasoning things out carefully is not one of my strong points.
15. I try to avoid situations that require thinking in depth about something. 16. I don’t trust my initial feelings about people.
17. Using my gut feelings usually works well for me in figuring out problems in my life. 18. I prefer complex problems to simple problems.
19. I don't like situations in which I have to rely on my gut instincts. 20. I am not very good at solving problems that require careful thinking. SCORING: (-) = reversed scored
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Appendix 3 Extra graphs with percentages general
FIGURE 11
Responses with percentages general responses
Appendix 4 Graphs extra clusters
FIGURE 12
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FIGURE 13
Average response values both low clusters
FIGURE 14
Percentage responses both high cluster
FIGURE 15
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FIGURE 16
Responses both high cluster
FIGURE 17
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Appendix 5 Extra graphs with percentages analytics
FIGURE 18
Responses with percentages general responses
Appendix 6 Extra graphs with percentages intuitives
FIGURE 19
