Exploring the Impact of Artificial Intelligence

1. Introduction

The purpose of this report is to identify the research methods, which could be used to conduct effective research on the impact of artificial intelligence on SMEs in the UK. In this regard, this report will provide a brief justification about the choice of topic as well as aims and objective of the report. Thereafter, it will identify data methodology and data collection methods including its advantages and disadvantage soffering the most valuable insights for those who are seeking business dissertation help. This will be followed by sampling, ethical considerations and data analysis. Finally, this report will include brief discussion about limitation of the approaches.

2. Justification of Topic Choice

Artificial intelligent (AI) is widely used by businesses to enhance its productivity and to lower its operational costs. Artificial intelligence helps to boost efficiency in some enterprises (Borth, 2018). It can create new jobs and reduce mistakes and human errors. However, the major impact of AI on SMEs is to gain competitive advantages as AI lowers the operational costs of the company, increases productivity, enhances flexibility and responsiveness which will lead to an increase in profitability volume by maximising sales (Otar, 2019). therefore, AI is important for all the SMEs.

3. Aims and Objectives

The aim of the report is to identify the research methods that could be used to conduct the research on the impact of artificial intelligence on the SMEs in the UK. The objective of this report is to research the different research methodology that could be used in this particular analysis including its advantages and disadvantages, identify and understand how the data can be collected that will be useful for effective analysis, identify the type and size of sample, acknowledge the ethical considerations, refer to similar research done on the topic and identify any limitations.

4. Academic Literature

A research which was done on ‘impact factors on agricultural SMEs technological innovation capacity’ using 114 samples showed that the main factors influencing the technological innovation of agricultural SMEs are the level of technological development, incentive measures to reach the staff, capital source, government policy support and enterprise strategy (Li and Zeng, 2010). Another research on ‘the impact of technological and non-technological innovation on export intensity in the SMEs’ with 11169 sample size showed that technological innovation has a positive impact on export intensity in SMEs (Radicic and Djalilov, 2019). Both researches used survey as the research method. On the other hand, a research on ‘the impact of research and technology organizations on firm competitiveness’ using surveys and interviews and 2357 samples showed that technology had a positive impact on the rate of investment (Barge-Gil and Modrego, 2009). This particular research used mixed methods research, which showed that the proposed research methodology can be effective to complete the analysis successfully by collecting and analysing valid data and information. Moreover, all 3 researches showed that, technology have an impact on SMEs and as artificial intelligence is a type of technology suggesting that the research can be done.

5 Data Methodology
5.1 Mixed Methods

Mixed methods research will be used to analyse the impact of artificial intelligence on the SMEs in the UK. Mixed methods research is the research that is used to combine qualitative research and quantitative research (Bryman and Bell, 2015). Qualitative research is research, which uses and focus on words instead of the quantification of data (Bryman and Bell, 2015). Quantitative research is research, which uses numerical data or data that can be transformed into usable statistics (Myers, 2009). Mixed methods research will be used as quantitative research facilitating the qualitative research through facilitating the selection of people or organizations that could be interviewed or observed (Bryman and Bell, 2015). In addition, triangulation can be used for the ‘validation’ of the research results by comparing the findings (Hair et al., 2007; Eriksson and Kovalainen, 2008).

The major advantage is that, it allows the researcher to answer both ‘confirmatory and exploratory questions’ simultaneously and therefore verifying and generating theory in the same study (Malina, Nørreklit and Selto, 2011). Mixed methods research could enhance the results to be more accurate and valid (Golafshani and Salehi, 2010). In addition to this, the exploration of more complex aspects and different concepts of the research leading to new empirical insights, as mixed methods can facilitate the interpretation of results and create a stronger outcome by combining data (Malina, Nørreklit and Selto, 2011; Bryman and Bell, 2015).

The major disadvantage is that, it is difficult to combine both the quantitative and qualitative research in one study (Golafshani and Salehi, 2010). There might be inconsistencies between both types of research methods (Malina, Nørreklit and Selto, 2011). In addition to this, the research must be competently designed and conducted to avoid suspicious findings (Bryman and Bell, 2015).

6.1 Qualitative Research

Qualitative data can be collected through different methods such as observations, interviews and analysis of texts and documents (Hair et al., 2007; Bryman and Bell, 2015). However, for this research semi structured interviews will be used to conduct the interview directors of different SMEs in the UK as it will allow proper collection of any essential information needed and it will allow for following up questions to clarifying specific points (Hair et al., 2007). The companies will be interviewed in a group as this will facilitate the collection of data.

The major advantage is the gathering of more depth information which can result more accurate results (Saunders, Lewis and Thornhill, 2012). Moreover, that the material is ‘systematic and comprehensive’ and it allows for preparation of questions which can enhance the findings (Hair et al., 2007; Eriksson and Kovalainen, 2008).

The main disadvantages are that interviewing skills are required (Eriksson and Kovalainen, 2008), questions must be carefully planned to prevent leading questions (Maylor and Blackmon, 2005). Additionally, it is difficult to compare materials as different enterprises could be affected differently (Eriksson and Kovalainen, 2008).

Survey is a method of quantitative research, which will be utilised. Surveys will be distributed to a different SMEs in the UK. Electronic surveys are going to be used by emailing the enterprises about the survey questions and asking them to take part in the research.

The main advantages are that, electronic surveys are not expensive and can be sent to large number of companies at the same time (Waters, 2011). Data collection and analysis will be fast (Hair et al., 2007). In addition to this, eliminating bias as no personal opinions are required (Hair et al., 2007).

The major disadvantages are that the survey might be filtered as spam emails (Waters, 2011). it will be limited to computer users (Hair et al., 2007). The person filling the survey will be anonymous, which can affect the quality of research (Hair et al., 2007). Limited or low number of respondents might lead to low quality research (Saunders, Lewis and Thornhill, 2012).

Stratified sampling will be utilised as the companies will be divided into groups with similar characteristics (Hair et al., 2007). Two companies from each group will be randomly selected to be interviewed and 20 companies, to which the survey questionnaire will be sent that can be used in this research and these will be provided in the Appendix. For example, agriculture SMEs will be grouped together and 2 companies will be randomly selected to be interviewed. This will give a wide range of reliable data which can be compared.

When conducting a research different ethics must be considered including informed consent where the researcher must provide sufficient information about the research to the participants, voluntary participation, where participants are not forced to take part in the research , confidentiality to keep personal information confidential, freedom from harm where there is no physical or psychological harm and anonymity as the identity of the participants must remain unknown (Parry and Mauthner, 2004; Saunders, Lewis and Thornhill, 2012; Parry and Mauthner, 2004; Eriksson and Kovalainen, 2008; Parry and Mauthner, 2004). In addition to respect and copy right. Ethical considerations are important because they support values required for collaborative work such as respect, trust and accountability (Hair et al., 2007) and prevent the fabrication of data (Saunders, Lewis and Thornhill, 2012).

Qualitative data could be analysed using software or theories such as Kolb’s cycle. Data gathered from interviews will be analysed using software programmes referred to as CAQDAS (Maylor and Blackmon, 2005). Software such as Ethnograph, QSR NVivo and ATLAS. It can be used to analyse the data gathered from the interviews (Hair et al., 2007). 9.1.1 Advantages The main advantages of using software are that similar concepts can be traced, the research can be managed easily, and large amount of data could be analysed in less time (Maylor and Blackmon, 2005). The software also allows for ease in coding change, adding notes, deleting and moving data (Cope, 2014).

The analysis of quantitative data, gathered from the surveys can be performed using software programme such as SNAP and SPSS (Maylor and Blackmon, 2005). SPSS can analyse data and produce frequency tables, bar charts, pie charts and histograms (Bryman and Cramer, 2011).

The main advantage of using statistical programs is that large numbers of data can be analysed and condensed into statistics (Saunders, Lewis and Thornhill, 2012). In addition to this, transforming the data in to tables, charts and graphs and setting the variables and labelling automatically therefore, improves the output (Bryman and Cramer, 2011).

Doing the research with different limitations might be faced such as getting access to enterprises as some enterprises will refuse to take part in the survey. Analysing data might be hard as familiarization with the software is needed to be able to use it effectively. A lot of time is required to interview the directors of the companies as a large number of data is going to be used for more accurate and reliable results and getting all the companies to fill the survey might be a limitation as some survey emails might be filtered as a spam email.

Conclusion

This report shows the advantages and disadvantages of the research methods that could be used to find the impact of artificial intelligence on SMEs in the UK. Therefore, selecting the right research method is important to be able to reach a reliable and accurate research outcome. Choosing the right sample size and type, preparing ethical considerations, data analysis and data collection methods are vital in achieving a valid and reliable results.

References

• Barge-Gil, A. and Modrego, A. (2009) 'The impact of research and technology organizations on firm competitiveness. Measurement and determinants’, The Journal of Technology Transfer, 36(1), pp.61-83.
• Borth, K. (2018) ‘Automation, AI and CRM’, Smart Business Columbus, 25(5), p.8.
• Bryman, A. and Bell, E. (2015) Business Research Methods. 4th edition. Oxford (UK): Oxford University Press, pp.174-179, 404-414 and 647-659.
• Bryman, A. and Cramer, D. (2011) Quantitative Data Analysis with SPSS 17, 18 and 19. London: Routledge, pp.86-100.
• Byrne, M. (2001) ‘Sampling for qualitative research’, AORN Journal, 73(2), pp.494-498.
• Carter, D. (2018) ‘How real is the impact of artificial intelligence? The business information survey 2018’, Business Information Review, 35(3), pp.99-115.
• Cope, D. (2014) ‘Computer-Assisted Qualitative Data Analysis Software’, Oncology Nursing Forum, 41(3), pp.322-323.
• Eriksson, P. and Kovalainen, A. (2008) Qualitative Methods in Business Research. Sage, pp.62-75, 82-86, 103-110 and 292-295.
• Golafshani, N. and Salehi, K. (2010) ‘Using mixed methods in research studies: An opportunity with its challenges’, International Journal of Multiple Research Approaches, 4(3), pp.186-191.
• Hair, J., Money, A., Samouel, P. and Page, M. (2007) Research Methods for Business. Chichester: Wiley, pp.60-74, 175-182, 193-210, 295 and 422.
• Howe, K. (2012) ‘Mixed Methods, Triangulation, and Causal Explanation’, Journal of Mixed Methods Research, 6(2), pp.89-96.
• Krishnaswamy, O. and Satyaprasad, B. (2010) Business Research Methods. Mumbai India: Himalaya Publication House, pp.1-19.
• Li, Y. and Zeng, W. (2010) ‘Analysis of Impact Factors on Agricultural SMEs’ Technological Innovation Capacity’, Asian Agricultural Research, 41(3), pp.10-16.
• Macfarlane, M., Kisely, S., Loi, S., Macfarlane, S., Merry, S., Parker, S., Power, B., Siskind, D., Smith, G. and Looi, J. (2014) ‘Getting started in research: designing and preparing to conduct a research study’, Australasian Psychiatry, 23(1), pp.12-15.
• Malina, M., Nørreklit, H. and Selto, F. (2011) ‘Lessons learned: advantages and disadvantages of mixed method research’, Qualitative Research in Accounting & Management, 8(1), pp.59-71.
• Maylor, H. and Blackmon, K. (2005) Researching Business and Management. Basingstoke: Palgrave Macmillan, pp.222-234, 280-288, 306-320 and 347-362. Myers, M. (2009) Qualitative Research in Business & Management. Los Angeles, California: Sage, pp.8-15 and 260. Otar, C. (2019) Four Ways Artificial Intelligence Can Help Your Small Business. Available at:
• Parry, O. and Mauthner, N. (2004) ‘Whose Data are They Anyway?’, Sociology, 38(1), pp.139-152. Radicic, D. and Djalilov, K. (2019) ‘The impact of technological and non-technological innovations on export intensity in SMEs’, Journal of Small Business and Enterprise Development.
• Saunders, M., Lewis, P. and Thornhill, A. (2012) Research Methods for Business Students. 6th edition. Pearson, pp.230-235, 310-330, 374-406 and 487-527. Waters, C. (2011) Quantitative Methods for Business. 5th edition. Harlow: Financial Times Prentice Hall, pp.86-94. Zyphur, M. and Pierides, D. (2017) ‘Is Quantitative Research Ethical? Tools for Ethically Practicing, Evaluating, and Using Quantitative Research’, Journal of Business Ethics, 143(1), pp.1-16.

Take a deeper dive into the Foundations and Applications of Ethics in Business with our additional resources.