Hypothesis Testing through SPSS (Statistics Project Sample)

Data Analysis and Interpretation Introduction The section discusses and interprets the information collected from respondents in a meaningful way using SPSS. The purpose of the section is to analyze the responses of respondents toward AI acceptance in the accounting field in UAE. The tests conducted are the reliability test, demographic information, correlation and regression to test the hypothesis and analyze the research variables. Instrument Reliability and Validity To test the instrument reliability and validity, Cronbach’s Alpha test is used. It is a statistical measure used to assess the internal consistency or reliability of a set of items in a questionnaire or scale (Hajjar, 2018). The concept of internal consistency refers to how well the items within a scale or questionnaire correlate with each other. It ranges from 0 to 1. A higher value indicates better internal consistency. Ideally, the value should be above 0.6 to make a scale reliability acceptable (Singh, 2017). Table 1: Reliability of Scale Scale α Perceived Support and Influence (PSI) 0.670 Perceived Benefits and Opportunity (PBO) 0.888 Perceived Threats and Risks (PTR) 0.865 Perceived Personal Competence (PPC) 0.851 Attitude (ATT) 0.924 Behavioral Intentions to Use 0.866 Table 1 presents the reliability analysis of the scale used in the study. The α values range from 0.670 to 0.924 which indicates a varying degrees of internal consistency. The ATT dimension demonstrates the highest reliability α= 0.924, followed by PBO α= 0.888 and PTR α= 0.865. While the PSI dimension exhibits lower reliability α= 0.670, the PPC and Behavioral Intentions to Use dimensions both show moderate reliability α= 0.851 and α= 0.866, respectively). These reliability coefficients are the measurement scale in capturing the intended constructs (Hajjar, 2018). Actual Use behavior is not used for reliability tests, because, it is a categorical variable (yes, no) and reliability cannot be measured through Cronbach’s test. Demographic Information The table 2 shows the demographic information of the variables where respondents selected only one option. The total number of respondents is 96. Table 2: Demographic Information (n=96) Demographic variable Option Respondents Percentage (%) Gender Male 45 46.9% Female 51 53.1% Age 21-30 19 19.8% 31-40 45 46.9% 41-50 30 31.3% 51-60 1 1% 61 and above 1 1% Years of work experience Less than 1 year 3 3.1% 1-3 years 0 0.0% 4-6 years 14 14.6% 7-10 years 28 29.2% Over 10 years 51 53.1% Number of Employees 1-10 employees 7 7.3% 11-50 employees 22 22.9% 51-250 employees 32 33.3% 251-500 employees 15 15.6% > 500 employees 20 20.8% Nationality Arab 12 12.5% Asian 53 55.2% African 13 13.5% Western 2 2.1% European 16 16.7% Table 2 displays the demographic characteristics of the study participants (n=96). In terms of gender distribution, 46.9% of respondents are male, while 53.1% are female. Age-wise, the majority fall within the 31-40 age group (46.9%), followed by 41-50 (31.3%). Over 10 years of work experience is prevalent among 53.1% of participants. Organization size varies, with 33.3% working in companies with 51-250 employees. Regarding nationality, the largest proportion is Asian (55.2%), followed by European (16.7%) and African (13.5%). These demographic insights provide context for the study's participant composition and potential variations in responses based on these factors. The table 3 shows the level of education where 96 respondents have selected the multiple options. It illustrates the education levels of participants. Among 141 responses from 96 respondents, 67.7% hold Bachelor's degrees, while 7.1% have Master's or post-graduate degrees. Notably, 45.4% possess professional certifications, and a minority hold diplomas (1.4%). No participants indicated having a Ph.D. Table 3: Level of Education Level of education Responses N Percent PHD 0 0% Master’s/ Post-graduate 10 7.1% Bachelor’s Degree 65 67.7% Diploma 2 1.4% Professional Certification 64 45.4% Total 141 100% The table 4 shows the current area of accounting of respondents. The majority (53.3%) specialize in General Accounting and Financial Accounting & Reporting. Internal Audit and Advisory/Consulting have no representation. External Audit, Management Accounting, and Tax Accounting are pursued by 15.9%, 25.2%, and 4.7% respectively. This breakdown provides insights into the diverse accounting focus areas of the participants. Table 4: Area of Accounting N Percent General Accounting / Financial Accounting & Reporting 57 53.3% Internal Audit 1 0.9% External Audit 17 15.9% Management Accounting 27 25.2% Tax Accounting 5 4.7% Advisory/ Consulting 0 0% Total 107 100.0% Table 5 shows the source of awareness and knowledge of respondents. Conversations with colleagues or industry professionals (25.8%) and social media (28.0%) are primary sources. Online articles (12.4%), professional bodies (9.7%), and professional development courses (8.9%) are also significant. Academic studies (6.4%) and hands-on AI application use (6.4%) play a minor role. A smaller fraction (4.2%) is either unaware or unexposed. This breakdown underscores the various channels through which participants gain awareness and knowledge. It reveals the influence of interpersonal interactions and digital platforms in disseminating information about AI. Table 5: Sources of Awareness and Knowledge of Respondents   Responses N Percent Academic studies 15 6.4% Professional development courses or certifications 21 8.9% Professional bodies and Industry conferences and seminars 23 9.7% Conversations with colleagues or industry professionals 61 25.8% Hands-on usage of AI applications in the workplace 15 6.4% Online articles and publications 25 12.4% Social Media 66 28.0% Not aware or not exposed 10 4.2% Total 236 100.0% Table 6 shows the awareness assessment of respondents. Most participants express a desire to learn more (80.2%), while a smaller portion claims good knowledge (8.3%). A minority is unaware of AI in accounting (11.5%). This table presents the varying degrees of awareness, with a significant proportion. The aim is to enhance their understanding of AI's role in the accounting field. Table 6: Awareness of AI in Accounting   Frequency Percent Yes, I have good knowledge and understanding of AI applications in accounting. 8 8.3 I have heard about AI in Accounting, but I would like to learn more. 77 80.2 No, I am not aware of AI being used in accounting. 11 11.5 Total 96 100.0 Correlation among Variables The table 7 shows the correlation among measurement variables. The correlation matrix aids in understanding the connections between different factors under study. It provides insights into potential patterns and associations that contribute to the perceptions and intentions of accountants towards the adoption of AI in the accounting field (Schempan & Rodway, 2020). The matrix explores the relationships between constructs based on how they interact. Correlation coefficients range from -1 to 1 and used to indicate the strength and direction of relationships (Schober et al., 2018). Table 7: Correlation among Measurement Variables   PBO PTR PSI PPC ATT BI AUI PBO 1 -.175 .312** .350** .514** .503** .113   .088 .002 .000 .000 .000 .272 PTR   1 -.314** -.414** -.647** -.521** .351**     .002 .000 .000 .000 .000 PSI     1 .445** .525** .528** -.450**       .000 .000 .000 .000 PPC       1 .679** .683** -.230*         .000 .000 .024 ATT         1 .836** -.372**           .000 .000 BI           1 -.369**             .000 AUI             1 **. Correlation is significant at the 0.01 level (2-tailed). Positive and statistically significant correlations in table 7 are observed between Perceived Benefits and Opportunity (PBO) and Perceived Support and Influence (PSI) (0.312**), as well as between PBO and PPC (0.350**). Similarly, a strong positive correlation exists between Attitude (ATT) and Behavioral Intention to Use (BI) (0.836**). Conversely, significant negative correlations are evident between Perceived Threats and Risks (PTR) and Attitude (ATT) (-0.414**), as well as between PTR and Personal Competence (PPC) (-0.647**). These findings suggest that as perceived threats and risks increase, attitudes towards AI adoption in accounting may become less favorable. The correlation coefficients ** are statistically significant at the 0.01 level (2-tailed), which signifies the robustness of these relationships. In terms of their relationships with AUI, the pattern emerges: BI exhibits the strongest positive correlation (0.503**), followed by PSI (0.445**) and PPC (0.350**). These findings suggest that BI has the highest positive impact on AUI, followed by PSI and PPC. Enhancing BI and focusing on PSI and PPC could contribute to improved AUI positively. Hypothesis Testing The rapid evolution of technology, particularly the integration of artificial intelligence (AI), has significantly transformed various industries, including accounting. The accounting profession is experiencing a paradigm shift, with AI offering opportunities to enhance efficiency, accuracy, and decision-making processes (Awotunde et al., 2021). As organizations increasingly explore AI applications, it becomes imperative to understand how accountants perceive and embrace this technological transformation (Issa et al., 2016). The five devised hypotheses are tested using linear regression analysis due to its suitability for examining relationships between variables and its significance. H1: Perceived support and influence will have a significant positive effect on the accountants’ behavioral intention to use AI in accounting. Table 8: Regression Analysis (PSI and BI) ANOVAa Sum of Squares Df Mean Square F Sig. 1 Regression 16.461 1 16.461 36.429 .000b Residual 42.474 94 .452 Total 58.935 95 a. Dependent Variable: BI_Avg b. Predictors: (Constant), PSI...