Description
Research Proposal Attached – Same quality standard is a must. Poor grammar, poor context, misconstruction sentences, and poor referencing will not be tolerated. Referencing – Proper referencing, with multiple references when appropriate – same quality as research proposal Read the proposal and ask relevant questions so I know you understand what the task is. Lit Review – Follow the Research Proposal – Build off the previous research and authors stated in the proposal. Introduction – Read the Research Proposal and follow it’s lead – This should be the last thing written Every section must tie back to the research question – What is the relationship between organizational support and innovation culture leading to the adoption of Artificial Intelligence? Read the Research Proposal for additional info Subsection 1 – 1000-1500+ words on AI, what it is, types, benefits, etc. Follow subsection method 1a 1b 1c Subsection 2 – AI Adoption – READ THE Research Proposal 1500+ words 2a 2b 2c Subsection 3 – Organizational Support – READ THE Research Proposal 1500+ words 3a 3b 3c Subsection 4 – Innovation Culture – READ The Research Proposal 1500+ words 4a 4b 4c Methodology Section 4500-5500 words – Follow the Research Proposal Research Philosophy Research Design Methods and Analysis a. Interviews (Expectation is having 10 interview questions to use based on the Lit Review and researched articles) b.Code – Thematic Limitations Ethics (Reference Darcy, etc) These will be written at a later time after the Lit Review and Research Methodology is completed Analysis/Findings Discussion Conclusion
RESEARCH METHODS
Effects Of Organizational And Innovation Culture On Technology Adoption
Word Count: 3107
Table of Contents
Introduction ………………………………………………………………………………………………………………………….. 1
Literature Review …………………………………………………………………………………………………………………… 2
AI Adoption Pioneers vs Laggards …………………………………………………………………………………………. 2
Organizational Support ……………………………………………………………………………………………………….. 3
Innovation Organizational Culture ………………………………………………………………………………………… 4
Conclusion …………………………………………………………………………………………………………………………….. 5
Research Question …………………………………………………………………………………………………………………. 6
Proposed Research …………………………………………………………………………………………………………….. 6
Research Methodology …………………………………………………………………………………………………………… 6
Research Instrument …………………………………………………………………………………………………………… 7
Research Sample ………………………………………………………………………………………………………………… 7
Data Analysis ……………………………………………………………………………………………………………………… 7
Limitations ………………………………………………………………………………………………………………………… 8
Ethical considerations …………………………………………………………………………………………………………. 8
Ethics Form ……………………………………………………………………………………………………………………….. 8
Reference List ………………………………………………………………………………………………………………………… 9
Appendix A: …………………………………………………………………………………. Error! Bookmark not defined.
Interviewees: …………………………………………………………………………… Error! Bookmark not defined.
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Introduction
While Artificial Intelligence (AI) is seen as a game changer in driving business and gaining competitive advantage, there is a disparity between adopting AI and understanding the practical implications. The adoption of new innovation is influenced by a firms characteristics such as technological readiness, relative advantage, firm size, and top management support (Alsheibani et al., 2018). Additionally, other aspects such as leadership, data management, agility, and innovation influence adoption of technologies such as AI (Brock and von Wangenheim, 2019).
Businesses are turning towards AI to address the challenges of business decisions with high expectation of results (Khan et al., 2010). However, the expectations can be unrealistic as Brynjolfsson states, (Brynjolfsson and Mcafee, 2019) “we see business plans liberally sprinkled with references to machine learning, neural nets, and other forms of the technology, with little connection to its real capabilities.” This sentiment is echoed amongst AI academics and scientists in the field. Executives believe that AI will benefit their organizations by cost reductions, increased efficiencies, or new business development. Ransbotham et al. (2017) states firms believe Al will allow their organization to obtain or sustain a competitive advantage.”
With these factors considered and executive commitment to implement AI, only about one in five has limited implementations of AI in some offerings or processes. With only one in twenty having broadly incorporated AI in their organization (Ransbotham et al., 2017). There are four categories of organizational maturity to the understanding and adoption of AI. The largest group, 36% of organizations with no plans of AI adoption, are coined as “passives” or “laggards”(Brock and von Wangenheim, 2019). The second largest group are “investigators” who understand AI, currently in the piloting phase, but not deploying beyond it. The third largest group at are “experimenters” who are currently in the pilot phase and learning by doing. Lastly the smallest group are the “pioneers,” 19% of organizations who have understanding and incorporated AI in their business processes (Brock and Von Wangenheim, 2019). While there is extensive research on the adoption of AI in the organization and factors that influence it, there is a gap in leadership and organizational support driving the adoption of innovation, notably AI in an Irish context.
There is opportunity for organizations to leverage their technology, which would include AI, within their business, and to harness their data to deliver a better client experience and gain competitive advantage (Ransbotham et al., 2017). Studies have identified that successful implementation of innovation including AI require business and technology strategy (Alsheibani et al., 2018; Brock and von Wangenheim, 2019; Ransbotham et al., 2017), and leadership supporting an innovation culture (Anandhi Bharadwaj et al., 2013; Gobble, 2019; Khan et al., 2010). The aim of this research is to further build upon previous studies to further establish the impact of these factors driving the successful implementation of innovative technologies, including AI.
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Literature Review
The proposed research will attempt to further explore the relationships between innovation adoption, organizational support, and innovation culture. Artificial Intelligence (AI) is positioned to have a transformational impact on business in all sectors. Current adoption of AI technology or cognitive technologies can be utilized to enhance essentially any service or product offering. AI which is defined as, “a systems ability to interpret external data correctly to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation” (Kaplan and Haenlein, 2019), is a progressive extension of current analytic strategies currently being employed by organizations. Davenport argues that, (Davenport, 2018) AI takes analytics to the next level by introducing automation for building models that allow the development of new products, and enhance features of existing products and services while accelerating the product development cycle, thus providing competitive advantage to business.
The literature review will consist of three sections in the realm of Innovation adoption. Starting with AI Adopters. This subject is important, as senior leadership commitment has a noteworthy positive influence on new technology adoption such as AI, in terms of defining a vision for the business (Yang et al., 2015). The second topic to be discussed is organizational support, and the factors which drive and hinder technology adoption at the leadership level. Finally, organizational culture and the dimensions which drive innovation in the firm.
AI Adoption Pioneers vs Laggards
A global survey conducted by the Boston Consulting Group concluded that AI adoption in business has a gap between ambition and execution. According to Ransbotham et al. (2017) less than 40 percent of interviewed companies reported to having an ongoing AI strategy. However, over 80 percent responded they believe that AI provides a real competitive advantage. A follow up survey by Ramsbotham et al. (2018) shows that the Pioneers of AI adoption were still increasing AI investment, further widening the adoption gap with other firms. The early adopters of AI, also called Pioneers, are positioning their firms to reap the benefits of AI at scale. These early adopters are focusing on revenue generating applications over cost saving activities as part of their strategy (Ransbotham et al., 2018). The Pioneer companies, whom have adopted, implemented, and understand AI accounted for less than 20 percent of the most recent survey.
The Investigators and Experimenters groups both have an interest in AI adoption, but differ in terms of engagement with AI technology. The Investigators group of firms are researching and educating their employees in regard to the technological possibilities of AI, however they are not currently testing or running experiments in regard to which applications should be scaled up. Barriers for investigators tends to be lack of resources and skill set to lead and implement initial experiments. Additionally, there is often a lack of data or strategy that
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would allow the firm to obtain complete and relevant data regarding their customers (Chui et al., 2018; Ransbotham et al., 2017). Experimenters, the smallest group are learning by doing. These firms are in a continuous state of performing tests in a controlled environment, but with limited understanding of the results. Lack of understanding tends to fall under two categories. The first being not matching an appropriate business problem to a suitable AI solution, or the incorrect assumptions being applied to the tests themselves. The final group of adopters of AI is the Passives. These firms have no AI strategy, and are neither investigating or experimenting with AI, as they believe that their firm doesn’t require AI, or that competition will not differentiate with AI prior to them.
Digital transformation leadership in the organization plays a critical role in supporting the adoption of new technologies. It is important to understand how support from the organization drives adoption and will be covered in the next section.
Organizational Support
Kahn et al. (2010), argues that drivers dictate the desired business outcomes of the organization which also imply pressure and incentive to adopt AI. Organizational support is critical and a key driving factor for a technology to be adopted and then utilized. Quinton et al. (2018) emphasises the main factors that influence technology adoption is leadership attitudes and behaviour that provides downward influence on the firm. The owner-managers level of IT knowledge, and belief that technology adoption delivers value is a driving factor in technology adoption (Bharadwaj et al., 2013; Awa et al., 2017). This delivered value is defined by Quinton et al. (2018) as relative value “the use of technology to create an outcome valued by the organization.” In more pragmatic terms this involves the AI systems leveraged to solve a business problem, enhance services, or to increase operational efficiency. Meinert et al. (2018) suggests that there is a fundamental aspect of value derived from more efficient systems and broad adoption of technology. This is observed in the transport industry with AI aimed at overcoming the challenges of an increasing travel demand. For transport authorities it is important to determine the way to use these technologies to create an improvement in operational improvements such as relieving congestion and delivering improved passenger satisfaction with reliable travel times (Abduljabbar, 2019). In Ireland, to deliver this relative value utilizing AI, The Real Time Passenger Information (RTPI) was a recent innovation and introduced to the public. The RTPI system takes in two key pieces of data into an AI data lake, which includes the ticketing data and vehicle location data. The location of busses around the city can actually then filter a prediction generator and give the arrival time of the next bus stop (Sheehan, 2019). Prior to RTPI passengers would wait for a bus to arrive at an approximate time, and sometimes not knowing if they had missed their bus. This system provides visual signage with up to the minute updates of bus arrival, a clear enhancement of service and operational efficiency delivering relative value by leveraging AI technology.
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Technology adoption delivering value is also considered a key barrier, but also indicates the requirement of organizational support and senior management buy in. Innovation projects suffering barriers, particularly in the public sector are mainly due to failure of the innovation champion to prove its relative value. Often this was due to lack of support by senior management due to either not involving them during vision creation or decision making process (Khan et al., 2010; Schedler et al., 2019). Adoption of innovation is shown to be more successful when two senior management stakeholders are on board with the innovation project from the onset, thus building alliances and partnerships (Anandhi Bharadwaj et al., 2013; Khan et al., 2010).
Other barriers such as security and data privacy and costs and costs are considered, however, studies show that lack of organizational support of an innovative culture are critical factors in the failure of technology adoption (Khan et al., 2010; Schedler et al., 2019; Shafie et al., 2014). Schedler et al. (2019) further states that in public sector the low incentive for innovation stifled the implementation of innovative initiatives. Additionally, little experimentation is often carried out, generically placing public sector in the investigators group (Schedler et al., 2019). Thus, placing the significance of organizational support as a key determinant of adoption of new technology and a topic worthy of further research.
Innovation Organizational Culture
Academics state that to remain relevant in a rapidly changing and competitive world, businesses must innovate to maintain competitive advantage, (Dobni C. Brooke, 2008; Dodge et al., 2017; Sadegh Sharifirad Mohammad and Ataei Vahid, 2012). Organizations must keep pace with intrinsic and extrinsic pressures to create new products and services, hence innovation is critical for an organizations achievability of vison, and long term growth, while also critical in a firms ability to adjust to changes in the market (Sadegh Sharifirad Mohammad and Ataei Vahid, 2012). However, research to date is scarce in how organizational culture affects the adoption of innovation. For example, Dodge et al. (2017) ‘conducted an informal survey to identify factors that participants perceived as providing obstacles or drivers of innovation at their companies’. Their core finding produced was that there are three leadership dimensions that result in a firm to be believed as an innovator by its staff: providing organizational encouragement, ensuring challenging work, and fostering support within the work group (Dodge et al., 2017). Shafie et al. (2014) focuses on the aspect of leadership encouraging knowledge sharing and learning as a key determinant of innovation. Shafie et al. (2014) state that a knowledge sharing culture had positive effects on innovation, leading to enhance operational performance and competitive advantage. Although a critical factor in developing an innovative organizational culture, this aspect is limited in scope as it doesn’t consider a wholistic view of the organization. Drawing from the innovation and organizational culture theories developed by Dennison, Sadegh Sharifirad Mohammad and Ataei Vahid. (2012) they state that there are four dimensions that influence innovative culture at the organizational level; consistency, adaptability, mission, and involvement. Consistency and
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innovativeness refer to the internal governance mechanisms to develop a mindset of innovation through consensual organizational support. They claim that “this encompasses core values, agreements, coordination, and integrations, sharing a mindset of values which creates a sense of identity and clear set of expectations ability to reach agreement on critical issues, and reconciliation with differences (Sadegh Sharifirad Mohammad and Ataei Vahid, 2012). Adaptability and innovativeness refer to the organizations ability to adapt to extrinsic market pressures and changes and turn it into action. Similar to Pioneers previously mentioned, these organizations are open to risk and learning from experimentation and mistakes allowing them to embrace and create change. The third dimension is mission and innovativeness where mission as described by Sharifirad and Vahid, “is the firms sense of purpose and direction that shapes and forms the goals and strategic objectives and expresses a vision of the organization in the future” (Sadegh Sharifirad Mohammad and Ataei Vahid, 2012). Lastly, Involvement and innovativeness describes the importance of empowering the individual and fostering a team orientation. Empowering an individual and allowing the person to have flexibility in decision making leads to additional participation amongst cross-functional teams, and motivation to innovate. By creating this sense of ownership though empowerment helps enable collaboration and knowledge sharing required for innovation and subsequently it’s implementation. This fourth dimension encapsulates the knowledge sharing culture theory by Shafie, and findings by Dodge, while Denison’s theory introduces additional aspects of consistency, adaptability, and mission. There appears to be a gap in which of the four dimension affects the innovativeness of the organizational culture and would be worthy of further research.
Conclusion
Innovation, by definition, is the process of turning ideas into reality and capturing value from them (Tidd and Bessant, 2013). The adoption of AI, or any technology requires more than technological skill sets, data lakes, cloud computing, and data scientists. Pioneers in the AI adoption arena have matured capabilities such as vision leadership, commitment and an openness to transformational change, effective collaboration, and alignment of the business and technological strategies of the firm (Ransbotham et al., 2017). This top-level management support and maturity are considered to be key organizational factors that drive successful innovation adoption, and innovation culture. Building upon this sentiment it is stated that innovation leadership is critical in encouraging positive attitudes towards technological change. This is accomplished though the innovation culture by empowering the individual to think outside the box, facilitating research, tests, and evaluation, and finally acceptance of new technological platforms (Dong et al., 2007). These aspects separate the pioneers from the laggards, providing the pioneers competitive advantage.
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Research Question
What is the relationship between organizational support and innovation culture leading to the adoption of Artificial Inteligence?
Proposed Research
The aim of this research is to build upon the studies of (Brock and von Wangenheim, 2019; Sadegh Sharifirad Mohammad and Ataei Vahid, 2012) in relation to the following:
• Which leadership and organizational support behaviours differentiate the Pioneers from the laggards?
• How does organizational support and which aspects influence the innovation culture and drive the adoption of technology, specifically AI?
• Which aspect of Denison’s model has the most profound effect on the innovation culture?
Research Methodology
This study proposes to investigate the relationship between the adoption of technology and innovation culture in the organization. Essentially it will attempt to answer what factors in the organization drive innovation and its subsequent adoption in Ireland.
As exemplified in the literature review the organization consists of, and decisions are made by people. This research will attempt to understand the decision-making role that people in the organization have in the defining the innovation culture and the adoption of those innovations. Thus, a qualitative approach with interprevist philosophy would be deemed appropriate. This approach is suitable as highlighted by Saunders et al. (2015) who states that the ‘researcher would need to make sense of subjective and socially constructed meanings in regards to the subject being studied’. Based on the literature review and analysis of research methodologies used by subject matter experts such as Schedler et al. (2019) who conducted thirty-two semi-structured interviews on the drivers and barriers to innovation adoption in the government sector. This study identified drivers and barriers at the organizational level in the adoption of technology efforts with a focus on the innovativeness of the culture and organization. Additionally, the study by Dobni C. Brooke. (2008) investigated innovation culture in the organization as a means of delivering competitive advantage and differentiation to deliver value.
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Research Instrument
This research will use a single case study approach with the National Transport Authority (NTA), an Irish government agency providing governance to Irelands transportation network. With government agencies traditionally behind the innovation curve, the NTA is seen as a digital transformation leader in an Irish context.
An inductive research approach in this study allows for the “theory to be developed through the observation of empirical reality; thus general inferences are induced from particular instances” (Collis and Hussey p.342, 2014). Semi-structured questions will be employed with a thematic approach, driving upon the literature to inform the themes. Innovation culture and the decisions to adopt technology are made by people. People make decisions derived from their own experiences, culture and influences, hence the inductive approach would be deemed fitting to understand why people make specific decisions and the motivators and variables drive them.
Research Sample
The research has secured access to senior leadership, and head of technical function at the NTA. A sample of 5-10 in depth interviews will be conducted with these senior executives and technology leaders at the NTA who have strategic and technological influence in the firm. These leaders will consist of the CIO, CEO, CFO, Head of Technology Architecture, Head of AI, Head of BI, Head of ICT, and Head of IT Security.
Data Analysis
Interview research and data will be in note written form in conjunction with using “Otter” a voice recording and transcribing application. As illustrated, analysis will be conducted employing the five-step model developed by Yin (2015).
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Limitations
Limitations of this research are considered due to the use of a single case study. Additionally, the researcher works for the NTA and in that regard may be subject to certain bias.
Ethical considerations
The researcher will carry out this research project in an independent and impartial manner void of bias based on gender, race, religion or creed. Participants will be treated with respect, and professionalism. The researcher will be ensuring subject respondents’ confidentiality and anonymity. All participants will participate on a voluntary basis and be void of harm based on this research project. All data will be maintained on a secured, encrypted mass storage device. All data will subsequently be destroyed after the appropriate time frame mandated by the National College of Ireland.
Ethics Form
The ethics form provided by the National College of Ireland (NCI) will be completed with honesty and transparency.
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Reference List
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