Application of software technologies and delivery of business education content in federal universities

This study examined the relationships between the application of software technologies and the delivery of Business Education content in federal universities in South South Nigeria. Specifically, it determined the individual and joint relationships of Cognitive Artificial Intelligence (AI), Voice on the PowerPoint (VOPPT) lectures, Skillshare and Google Forms with Business Education content delivery. A correlational research design was adopted. The population comprised 306 respondents, and the entire population was studied. Data were collected using a structured questionnaire and analysed using simple and multiple linear regression at the.05 level of significance. Five research questions were answered and five null hypotheses were tested. Cognitive AI had a significant positive relationship with Business Education content delivery (R =.574, R² =.330, β =.574, t = 12.235, p <.001). VOPPT also had a significant positive relationship (R =.626, R² =.392, β =.626, t = 14.011, p <.001), while Skillshare demonstrated a significant positive relationship (R =.578, R² =.335, β =.578, t = 12.363, p <.001). Google Forms recorded the strongest individual relationship (R =.804, R² =.646, β =.804, t = 23.572, p <.001). Collectively, the four technologies explained 65.6% of the variance in Business Education content delivery (R =.810, R² =.656, adjusted R² =.652), with the overall regression model being significant, F(4, 301) = 143.634, p <.001. In the joint model, Skillshare (β =.185, t = 2.921, p =.004) and Google Forms (β =.808, t = 14.746, p <.001) made significant unique contributions, whereas Cognitive AI (β = −.107, t = −1.395, p =.164) and VOPPT (β = −.062, t = −.775, p =.439) did not. The results indicate that the four software technologies are individually associated with Business Education content delivery, but their unique contributions differ when considered simultaneously. The findings highlight the importance of strategic technology integration, lecturer capacity development, reliable digital infrastructure and institutional support for the effective delivery of Business Education content in federal universities.

The integration of software technologies into Business Education has become increasingly important as workplaces, professional practices, and learning environments undergo rapid digital transformation. Contemporary Business Education is expected to prepare learners with knowledge, practical skills, technological competence, and adaptive capacities required for participation in digitally mediated economies. For the purpose of the present study, the application of software technologies is operationalised and delimited to four specific technologies: Cognitive Artificial Intelligence (AI), Voice on PowerPoint (VOPPT), Skillshare, and Google Forms. This delimitation was necessary because an interim review of the literature revealed limited empirical attention to these particular software technologies in relation to the delivery of Business Education content, especially within Nigerian federal universities. The study therefore does not treat software technologies as an unrestricted collection of digital applications; rather, the concept is defined within the boundaries of the four technologies investigated. Haleem et al. (2022) observed that digital technologies support instructional material development, collaborative learning, and access to educational opportunities beyond conventional classroom boundaries. Similarly, Hite et al. (2024) noted that software tools increasingly support interactive and immersive forms of learning that are relevant to contemporary instructional practice.

Business Education occupies a particularly important position within this technological transition because of its orientation towards the development of occupational competence and economic participation. Okon (2015) described Business Education as a process through which individuals acquire knowledge, skills, and values necessary for workplace effectiveness and meaningful economic contribution. This orientation implies that the delivery of Business Education content must respond to changes in both educational practice and the world of work. Conventional instructional approaches may be inadequate where learners are expected to develop competencies associated with digital communication, information processing, assessment, entrepreneurship, and technology-supported decision-making. Software technologies may therefore provide additional instructional opportunities through which lecturers and students can interact with content in more flexible and technologically relevant ways.

Within the context of this study, Cognitive AI represents one dimension of software technology with potential relevance to the delivery of Business Education content. Cognitive AI refers broadly to AI-enabled systems capable of supporting activities associated with reasoning, perception, information processing, and adaptive responses. Virk explained the relevance of Cognitive AI to addressing learning gaps through capabilities that approximate aspects of human reasoning and perception. Gershon and Helfenbein (2023) similarly drew attention to the potential of technology-supported personalised learning in responding to differences in learners’ educational needs. Asplund et al. (2022) further emphasised the importance of engaging with digital tools in meeting the changing requirements of vocational learning and training. Existing research on AI in education provides an important, though not always direct, basis for understanding the possible instructional relevance of Cognitive AI. Sollosy and McInerney (2022) emphasised the need to align business curricula with AI-related competencies in order to prepare learners for changing employment requirements. Chen et al. (2020), in their analysis of research on Artificial Intelligence in Education, identified increasing scholarly interest in the field while also pointing to limitations in the application of advanced AI approaches and insufficient engagement with educational theory. These observations are relevant because they demonstrate that AI has become an established area of educational inquiry, although evidence relating specifically to Cognitive AI and the delivery of Business Education content remains comparatively limited.

The broader educational circumstances surrounding the expansion of AI-supported learning also require consideration. Šorgo et al. (2023) demonstrated how inadequate access to digital tools during the COVID-19 period was associated with negative consequences for students’ well-being and motivation, thereby highlighting the importance of equitable technological access in remote learning environments. Fokides and Alatzas (2023) found that an interactive technological interface supported learners’ understanding and enjoyment, illustrating the instructional possibilities of technologies that permit active learner engagement. Masry-Herzallah (2023) identified communication quality, socioeconomic conditions, and access to technology as important influences on the effectiveness of online teaching. Although these studies did not examine Cognitive AI specifically within Business Education programmes, they provide contextual evidence concerning the conditions under which technology-supported learning can facilitate or constrain instructional processes.

Evidence from Nigeria further indicates the relevance of software-supported approaches to tertiary education. Amahi et al. (2023) examined the use of Microsoft Teams and reported its effectiveness for aspects of content delivery in tertiary institutions in Delta State, although its effectiveness differed across instructional responsibilities. The authors also reported a significant relationship between the application of Cognitive AI and Business Education content delivery. This study is particularly relevant to the present investigation because it provides evidence from a related Nigerian educational context. Nevertheless, the limited number of studies that directly examine Cognitive AI as a distinct software technology in relation to the delivery of Business Education content justifies further empirical attention.

Voice on PowerPoint lectures constitute the second operational dimension of software technology investigated in the study. VOPPT combines visual presentation with recorded or narrated audio and may enable learners to engage with instructional materials through more than one mode of representation. Gershon and Helfenbein (2023) highlighted the importance of technology-supported approaches capable of directing learners’ attention and accommodating different patterns of engagement. The potential relevance of narrated presentations to Business Education lies in the possibility of explaining concepts, demonstrating procedures, revisiting instructional content, and making lecture materials available beyond the immediate classroom encounter. The empirical literature on presentation technologies, however, presents mixed findings. Cosgun (2017) reported findings that did not support the superiority of PowerPoint presentations over traditional instructional approaches, while Baker et al. (2018) found no significant difference in learning outcomes between PowerPoint-based and conventional methods. These findings suggest that the mere use of presentation software may not automatically be associated with improved educational outcomes. The instructional design, mode of application, content characteristics, and manner in which learners engage with the technology may be more important than the presence of the technology itself.

Other studies provide indirect support for the instructional potential of multimedia and technology-supported approaches associated with VOPPT. Sulle and Wema (2023), in their study of student knowledge ambassadors and the promotion of electronic resources, identified ICT competence, motivation, communication skills, institutional policies, and support systems as important conditions for successful technology-supported educational practice. Although their study was situated in an academic library environment rather than a Business Education classroom, the findings demonstrate that the effectiveness of digital instructional systems depends substantially on the human and institutional capacities surrounding their use. Weber et al. (2023) examined technology-supported reflective tools and found differences in immersion, enjoyment, and knowledge-based reasoning across the learning conditions studied. Their findings suggest that video-supported and reflective technologies can influence learners’ cognitive and affective engagement. Adams et al. (2023) reported that an evidence-based professional development programme strengthened university teachers’ confidence in educational design, assessment, technology use, and lifelong learning. These findings are relevant to the broader conditions required for effective VOPPT application because narrated PowerPoint instruction depends not only on access to presentation software but also on lecturers’ ability to design and use multimedia resources pedagogically. The study by Oguejiofor and Onyiorah (2023) provides more closely related evidence. Their investigation of audiovisual resources in the teaching of Business Studies found that such resources were inadequately available and underutilised, while technology-supported presentation approaches were associated with instructional delivery. This evidence is important because it relates audiovisual instructional practice to a business-related educational field. Nevertheless, direct empirical research specifically examining Voice on PowerPoint lectures as an independent software technology and its relationship with the delivery of Business Education content at the university level remains limited. The present study therefore addresses a more specific empirical question than the broader literature on PowerPoint, audiovisual materials, or multimedia learning.

Skillshare represents the third software technology examined. As a project-oriented online learning platform, Skillshare offers instructional materials that may support practical learning, independent skill development, creative activities, and engagement with online learning communities. This orientation is potentially relevant to Business Education because the discipline incorporates practical areas in which learners may benefit from demonstrations, guided projects, skill-based tasks, and opportunities to interact with applied content. Hamdi (2023) discussed the capacity of Skillshare to support creativity, entrepreneurship, and digital marketing learning through accessible and professionally oriented instructional experiences. Hurley (2024) further described its development as a global learning platform serving diverse educational and professional interests. Direct empirical evidence linking Skillshare specifically with Business Education content delivery is, however, relatively scarce. This limitation explains why some studies reviewed in this area address adjacent forms of technology-supported practical learning rather than Skillshare itself. Sureephong et al. (2023), for example, investigated virtual reality in library orientation and found that although the technology increased situational interest, it did not significantly improve knowledge acquisition. Their findings caution against assuming that technological novelty necessarily translates into improved learning outcomes. The study is therefore relevant to the broader issue of technology-supported experiential learning, but it does not constitute direct evidence concerning Skillshare. Research on institutional support for online and digital learning also provides contextual insight.

A study grounded in New Institutional Theory examined the role of support units in facilitating MOOCs and online courses and found that grassroots innovation could promote digital development, whereas inadequate leadership support constrained broader institutional transformation. This finding is relevant to Skillshare because the adoption of an online learning platform within formal educational programmes may depend on institutional structures, leadership support, and the integration of the platform into established instructional arrangements. Other studies cited in the earlier literature, including Amos (2023), Angelopoulos and Bates (2023), and Guigui et al. (2023), address advanced computational methods and software-supported approaches to problem-solving, uncertainty estimation, data modelling, and mathematical analysis. Their inclusion should be interpreted cautiously. These studies do not provide direct empirical evidence concerning Skillshare or Business Education content delivery. Their relevance is indirect and lies in illustrating the wider expansion of software-based tools capable of supporting complex learning, analytical, and problem-solving activities. They should not be treated as evidence that Skillshare itself produces particular instructional outcomes. Among the studies reviewed, Okon (2022) provides a closer connection to the present investigation by examining team teaching through Skillshare software and reporting a significant relationship with content delivery effectiveness. This study contributes direct contextual relevance by connecting Skillshare with an instructional strategy and content delivery. Even so, the small body of literature directly investigating Skillshare as a distinct instructional software technology within university Business Education remains insufficient to establish a substantial empirical tradition. This scarcity represents an important part of the research gap addressed by the present study.

Google Forms constitutes the fourth operationalised software technology. Its educational relevance is primarily associated with the collection of information, administration of assessments, organisation of responses, provision of feedback, and monitoring of learners’ progress. In large university classes, these functions may be particularly relevant because lecturers require manageable approaches to assessment and feedback. The growing application of Google technologies in education has therefore generated research on their contribution to instructional and assessment processes. Awwad et al. (2022) reviewed studies on the educational application of Google Apps and identified their potential contribution to student learning, communication, and the development of academic and technical competencies. Abdul and Hasan (2018), using a quasi-experimental approach, examined Google educational tools and found significant differences in favour of students taught through the use of the technologies. Although these studies addressed Google applications more broadly rather than Google Forms specifically, they provide relevant evidence concerning the educational value of the wider Google software environment.

The literature on digital assessment offers further indirect support for examining Google Forms in relation to content delivery. Wang and Wang (2023), using PISA data, found that educational ICT resources did not directly predict academic performance but were associated with forms of student engagement that mediated academic outcomes. This finding is important because it indicates that technology may influence educational outcomes through intermediate instructional processes rather than through a simple direct pathway. O'Neill et al. (2023) examined the validity of an instrument for evaluating teaching and learning experiences and highlighted the importance of psychometrically sound assessment processes. Chang and Tseng (2023) found evidence of moderate to strong reliability in peer assessment across a substantial proportion of the classes investigated. Together, these studies draw attention to the centrality of reliable and appropriately designed evaluation systems in technology-supported education.

The assessment literature also addresses academic integrity, adoption conditions, feedback, and the management of large student populations. Giannakouli et al. (2023) reported that university librarians contributed to the promotion of academic integrity, although their involvement in plagiarism detection and institutional policy development was limited. Camilleri and Camilleri (2023) found that access to training and technical support was important for the continued use of mobile learning technologies. Corning et al. (2023) demonstrated that technology-focused educational programmes could influence student engagement while producing varied assessment outcomes, reinforcing the need for deliberate instructional and evaluation strategies. Yu et al. (2023) developed and validated a model for evaluating peer-assessment quality, while Gobbo et al. (2023) examined automatic grading and feedback technologies and highlighted their potential to reduce subjectivity and improve grading efficiency. Lee (2023) further addressed the challenge of providing feedback in large-scale online learning environments. By integrating peer assessment, self-assessment, and AI-supported evaluation, the study demonstrated a possible approach to expanding feedback capacity in large student populations. Although this work did not investigate Google Forms, it is relevant to the broader assessment and feedback functions represented by the application of software technologies in education.

The preceding literature demonstrates an important distinction between direct and indirect evidence. Direct studies specifically examining Cognitive AI, VOPPT, Skillshare, or Google Forms in relation to the delivery of Business Education content are limited. Consequently, a substantial part of the available literature addresses adjacent issues such as AI in education, multimedia instruction, audiovisual resources, online learning platforms, digital assessment, peer assessment, automated feedback, virtual learning, and institutional support for educational technology. These studies remain relevant because they explain mechanisms and conditions associated with the use of software technologies in educational practice. However, they cannot be interpreted as substitutes for direct empirical evidence concerning the four software technologies operationalised in the present study.

This limitation in the literature is itself theoretically and empirically significant. Research on educational technology has frequently examined broad technology categories or general outcomes such as academic achievement, engagement, satisfaction, adoption, or digital competence. Far fewer studies have disaggregated software technologies into specific applications and examined their separate relationships with the delivery of disciplinary content. The distinction is important because Cognitive AI, VOPPT, Skillshare, and Google Forms perform substantially different instructional functions. Cognitive AI may support reasoning and adaptive engagement with information; VOPPT may support narrated multimedia presentation; Skillshare may facilitate project-oriented and practical learning; and Google Forms may support assessment, response collection, and feedback. Treating all educational technologies as a single construct could obscure these functional differences. The present study is further supported by Leont'ev’s (1981) Activity Theory. The theory conceptualises human activity as a socially situated and tool-mediated process comprising activities, actions, and operations. It explains how individuals pursue objects and goals through the use of mediating artefacts within particular social and institutional arrangements. McAvinia (2016) explained that the theory emphasises the relationship between individuals, cultural tools, social interaction, and the objects towards which activity is directed. Learning is therefore not viewed as an isolated process occurring independently of the tools and social structures that shape human action.

Activity Theory provides an appropriate explanation for the present study because the application of software technologies can be understood as a form of tool-mediated instructional activity. Within the university context, lecturers and students constitute actors engaged in activities directed towards the communication, understanding, assessment, and application of Business Education content. Cognitive AI, VOPPT, Skillshare, and Google Forms may function as mediating software artefacts through which instructional actions are organised. Their relationship with content delivery may depend not only on their technical characteristics but also on the goals of users, institutional rules, available infrastructure, users’ competencies, and the organisation of teaching and learning activities. This theoretical position is consistent with the observation by Timya and Duktur (2023) that lecturers require relevant digital competencies to avoid widening educational inequalities and with Amahi et al. (2023), who emphasised the importance of staff capacity in the application of software technologies.

The conceptual and empirical literature therefore points to a clear research problem. There is substantial scholarly interest in digital transformation, AI, multimedia learning, online platforms, digital assessment, and technology-supported feedback. Haleem et al. (2022), Vo and Csapó (2023), and Youngok and Emma (2024) illustrate the broader movement towards technologically mediated learning environments and changing expectations regarding educational delivery. Yet this expanding body of work has not produced sufficient direct evidence on the relationship between the four specific software technologies investigated in this study and the delivery of Business Education content.

The existing evidence is also fragmented across educational levels, disciplines, countries, and technological applications. Several studies were conducted outside Nigeria, while others focused on primary, secondary, or general higher education rather than university Business Education. Some Nigerian studies addressed related technologies or business-related subjects without examining the four specific software technologies as distinct variables within federal universities in the South-South geopolitical zone. In addition, much of the literature emphasised learning achievement, engagement, technology adoption, or general educational outcomes rather than the delivery of disciplinary content.

The present study responds to these limitations by operationalising the application of software technologies through Cognitive AI, Voice on PowerPoint lectures, Skillshare, and Google Forms and examining their relationships with the delivery of Business Education content in the sampled federal universities in South-South Nigeria. The inclusion of indirectly related studies was therefore necessitated by the limited body of literature with a direct empirical focus on these specific variables. Rather than representing conceptual weakness, this scarcity demonstrates the originality and need for a focused investigation. The study consequently contributes evidence on an underexplored intersection of specific software applications, disciplinary content delivery, and university Business Education within the Nigerian higher education context.

Statement of the problem

Modern education increasingly relies on the application of software technologies to enhance content delivery through engagement, personalization, and real-world relevance, access to online resources, global collaboration, and skills development. Universities globally are adopting policies for integrating e-learning resources and training students for effective utilization across teaching, administration, and learning. Despite their potential, challenges persist in the application of software technology and tools for business education. Issues such as learning gaps, high instructor workload, and assessment difficulties remain prevalent. Notably, there is a lack of empirical evidence on the extent of e-learning adoption for content delivery in business education within Nigerian universities. This raises concerns about lecturers’ competence in using these tools and the implications for graduate readiness in a technology-driven labor market.

Graduates often face employment barriers due to skill gaps - an ironic outcome for a program aimed at skill development. As technical and work-related skills become increasingly technology-driven, the underuse of e-learning resources in delivering business education content worsens this gap. The researcher observed that many business education environments remain traditional in their instructional approaches, which are inadequate for addressing contemporary educational challenges. Particularly in South-South Nigeria, these outdated methods contribute to poor delivery outcomes.

The limited application of tools like cognitive AI for bridging learning gaps, VOPPT for easing instructor workload and Google Forms for student assessment - alongside insufficient research on their relationship to business education delivery - represents a significant gap. This study aims to address that gap by exploring how the application of these software technologies can enhance content delivery and potentially improve educational outcomes.

Objectives of the study

This study aimed specifically to: Determine the extent of the relationship between the application of Cognitive Artificial Intelligence (AI) and the delivery of business education content in federal universities in South-South Nigeria.

Determine the extent of the relationship between the application of Voice on PowerPoint (VOPPT) lectures and the delivery of business education content in federal universities in South-South Nigeria.

Determine the extent of the relationship between the application of Skillshare and the delivery of business education content in federal universities in South-South Nigeria.

Determine the extent of the relationship between the application of Google Forms and the delivery of business education content in federal universities in South-South Nigeria.

Determine the extent of the joint relationship among the applications of Cognitive AI, Voice on PowerPoint (VOPPT), Skillshare, and Google Forms, and the delivery of business education content in federal universities in South-South Nigeria.

Research questions

To guide the study, the following research questions were posed: To what extent does the application of Cognitive AI relate to the delivery of business education content in federal universities in South-South Nigeria?

What is the extent of the relationship between the application of Voice on PowerPoint (VOPPT) lectures and the delivery of business education content in federal universities in South-South Nigeria?

What is the extent of the relationship between the application of Skillshare and the delivery of business education content in federal universities in South-South Nigeria?

What is the extent of the relationship between the application of Google Forms and the delivery of business education content in federal universities in South-South Nigeria?

What is the joint relationship among the applications of Cognitive AI, Voice on PowerPoint (VOPPT), Skillshare, and Google Forms, and the delivery of business education content in federal universities in South-South Nigeria?

Statement of hypotheses

The study was guided by the following null hypotheses: There is no significant relationship between the application of Cognitive AI and the delivery of business education content in federal universities in South-South Nigeria.

There is no significant relationship between the application of Voice on PowerPoint (VOPPT) lectures and the delivery of business education content in federal universities in South-South Nigeria.

There is no significant relationship between the application of Skillshare and the delivery of business education content in federal universities in South-South Nigeria.

There is no significant relationship between the application of Google Forms and the delivery of business education content in federal universities in South-South Nigeria.

There is no significant joint relationship among the applications of Cognitive AI, Voice on PowerPoint (VOPPT), Skillshare, and Google Forms, and the delivery of business education content in federal universities in South-South Nigeria.

This study adopted a correlational research design to examine the relationship between the application of software technologies and the delivery of Business Education content in federal universities in South-South Nigeria. The design was considered appropriate because the study investigated naturally occurring relationships among the variables without manipulation or experimental intervention. Specifically, the design enabled the researcher to determine the extent to which the application of Cognitive Artificial Intelligence (AI), Voice on PowerPoint (VOPPT) lectures, Skillshare, and Google Forms was related to the delivery of Business Education content and to establish their joint relationship with content delivery.

The study was conducted in three federal universities offering established Business Education programmes in the South-South geopolitical zone of Nigeria, namely the University of Benin (UNIBEN), University of Calabar (UNICAL), and University of Uyo (UNIUYO). The choice of the universities was based on their established Business Education programmes, institutional experience in higher education, and relevance to the investigation of software-supported instructional practices. The population of the study comprised 1,450 respondents, consisting of 53 Business Education lecturers and 1,397 undergraduate students in the three selected universities during the 2023/2024 academic session. The lecturer population comprised 13 professors, eight senior lecturers, 13 Lecturer I, 14 Lecturer II, and five assistant lecturers. The undergraduate population comprised 1,397 students distributed across Years 1–4. The population frame therefore provided a total of 53 lecturers and 1,397 undergraduates, giving a combined population of 1,450 respondents.

Lecturers and students were combined as respondents because both groups assessed the same constructs and questionnaire items concerning the application of software technologies and the delivery of Business Education contents within the same instructional environment. Since the study focused on the overall relationship between software technology application and content delivery rather than differences between lecturers and students, their responses were appropriately treated as a combined assessment of the instructional environment, and separate group analyses were not required.

A multistage sampling procedure was adopted. Census sampling was used to include all 53 Business Education lecturers because their number was manageable and their direct involvement in teaching made their inclusion important to the study. For the undergraduate students, the Krejcie and Morgan (1970) sample size determination procedure was used to obtain a sample of 253 students from the undergraduate population of 1,397. The 253 students were proportionately distributed across the three universities to ensure representation according to the size of the undergraduate population in each institution. Accordingly, the final sample comprised 306 respondents, consisting of 53 lecturers and 253 undergraduate students. The lecturer sample represented the entire lecturer population, while the undergraduate sample represented the respective institutional proportions of the student population.

Data were collected using the Application of Software Technologies and Delivery of Business Education Content Questionnaire (ASTDBECQ), developed by the researcher in accordance with the objectives, research questions, hypotheses, and operational definitions of the study. The questionnaire contained 39 substantive items organised into five sections. Section A, comprising items 1–7, measured the application of Cognitive Artificial Intelligence. The items assessed the extent to which Cognitive AI facilitates reasoning, learning of Business Education content, interaction among students, ability to handle vocational tools, ability to handle vocational education equipment, teachers’ use of synchronous teaching, and the ability to use information and communication technology resources. Section B, comprising items 8–14, measured the application of Voice on the PowerPoint lecture. The items covered the ability to narrate the content of PowerPoint slides, enabling the audience to hear the lecturer’s voice, generation of audio files, use of audio files, enhancement of computer skills, provision of clues to students, and multimedia presentation.

Section C, comprising items 15–21, measured the application of Skillshare. The items addressed the provision of video guides for skills acquisition, optimisation and modelling in mastering skills, use of modelling to solve similar problems, reduction of uncertainty through practical activities, encouragement of hands-on learning across specialised areas of Business Education, engagement in machine-learning tasks, and provision of framework models for unifying complex learning problems. Section D, comprising items 22–28, measured the application of Google Forms. The items assessed its provision of educational ICT resources for improving student performance, assistance in evaluating learning quality, promotion of quality appraisal frameworks reflecting current standards, recording of students’ results in examination portals, detection of academic misconduct, attention to gender disparity, and provision of adequate feedback to students.

Section E, comprising items 29–39, measured the delivery of Business Education content. The items assessed whether facts were communicated adequately, skills taught were properly understood, learning experiences were shared, students were evaluated in a timely manner, content delivery was flexible, students were exposed to industrial skills and e-platform skills, and whether software-supported delivery aided Accounting Education, Marketing and Distribution Education, Office Management Education, and Entrepreneurship Education. Thus, items 1–28 measured the four explanatory variables, while items 29–39 measured the criterion variable, delivery of Business Education content.

The questionnaire employed a four-point researcher-made response format comprising Strongly Agree (SA), Agree (A), Disagree (D), and Strongly Disagree (SD), scored 4, 3, 2, and 1, respectively. The four-point format was adopted to obtain a clear directional response from respondents and avoid the neutral midpoint that could otherwise obscure their perceptions regarding the application of the specified software technologies and the effectiveness of Business Education content delivery.

The instrument was subjected to face and content validation by three experts in Business Education and Measurement and Evaluation. The experts examined the items for clarity, relevance, appropriateness, comprehensiveness, and alignment with the objectives and variables of the study. Their observations and recommendations were incorporated into the final version of the questionnaire. Reliability of the instrument was established through a pilot study involving 40 respondents from the University of Cross River State who were excluded from the main study. Cronbach’s alpha coefficients for the instrument’s subscales ranged from.79 to.99, while the overall reliability coefficient was.83. These coefficients indicated satisfactory internal consistency and provided evidence that the instrument was sufficiently reliable for the main study.

Following approval from the Heads of the selected Business Education departments, the researcher administered the questionnaire to the sampled respondents. Copies were administered personally during scheduled visits to the institutions, while electronic copies were provided to respondents who were unavailable during the physical administration. Respondents were informed about the purpose of the study, participation was voluntary, and appropriate measures were taken to ensure informed consent, confidentiality, anonymity, and the right to withdraw from participation.

The data generated from the questionnaire were analysed using simple linear regression and multiple linear regressions. Simple linear regression was used to address the first four objectives by determining separately the relationship between Cognitive AI, VOPPT lectures, Skillshare, and Google Forms and the delivery of Business Education content. The coefficient of multiple determinations (R²), adjusted R², multiple correlation coefficients (R), and standard error of the estimate were used to determine and interpret the extent of each relationship. Multiple linear regressions was used to address the fifth objective by examining the joint relationship among Cognitive AI, VOPPT lectures, Skillshare, and Google Forms and the delivery of Business Education content. The R, R², adjusted R², standard error of the estimate, and F statistic were used to assess the overall model, while the standardised regression coefficients (β), t statistics, and corresponding p values were used to determine the unique contribution of each software technology when the four predictors were considered simultaneously. All null hypotheses were tested at the.05 level of significance. The use of simple linear regression and multiple linear regression was appropriate to the research questions and hypotheses. For each simple linear regression involving one predictor variable and 306 respondents, the degrees of freedom were 1 for Regression, 304 for Residual, and 305 for Total. For the multiple linear regression involving four predictor variables and 306 respondents, the degrees of freedom were 4 for Regression, 301 for Residual, and 305 for Total. These degrees of freedom were determined from the number of predictor variables and the total number of valid observations included in each analysis. Accordingly, the regression tables and interpretations were presented using the appropriate degrees of freedom.

The direction of the bivariate relationship and the regression coefficient were interpreted separately. The correlation coefficient indicates the direction of the simple association between the variables, whereas the regression coefficient indicates the predictor’s unique contribution to the dependent variable within the regression model. Based on the recalculated regression results, the coefficients and their interpretations have been corrected accordingly. The assumptions of linear regression, including linearity, independence of observations, normality of residuals, homoscedasticity, and absence of multicollinearity, were tested and found to be adequately satisfied before the regression analyses.

Although the study provides useful evidence on the application of software technologies and delivery of Business Education content, certain limitations should be acknowledged. First, the study did not include formal multicollinearity diagnostics in the multiple regression analysis. The relatively high intercorrelations among some software technology variables indicate substantial shared variance, which may have influenced the stability and precision of their individual regression coefficients. Consequently, the unique contributions of the predictors in the joint model should be interpreted with caution. Future studies should incorporate tolerance and variance inflation factor diagnostics when examining the joint contribution of related software technologies. Another limitation of the study is that construct validity was established primarily through expert face and content validation and did not include exploratory or confirmatory factor analysis. Although expert validation provided evidence of the relevance, clarity and adequacy of the ASTDBECQ items, the absence of EFA or CFA means that the underlying factor structure and empirical distinctiveness of the instrument’s dimensions were not statistically verified. Consequently, the findings should be interpreted within the context of the study and should not be taken as definitive evidence that the identified dimensions of the ASTDBECQ are consistently structured across different populations, institutions or settings. Future studies should subject the instrument to exploratory and confirmatory factor analyses to strengthen evidence of construct validity and improve the generalisability of findings derived from the instrument.

Ethical approval was obtained from the University of Calabar Research Ethics Review Board (UNICAL-RERB), reference number UC/DR&D/RERB/56. The approval confirmed that the study complied with applicable ethical requirements for research involving human participants. Respondents were informed of the purpose and procedures of the study, participation was voluntary, informed consent was obtained, and confidentiality, anonymity, and the right to withdraw from participation without penalty were maintained throughout the study.

Research question one

To what extent does the application of cognitive AI relate to the delivery of business education contents in federal universities in South South Nigeria?

The research question is answered using the coefficient of determination. The analysis is based on respondents’ ratings of questionnaire items 1–7 on the application of cognitive AI and items 29–39 on the delivery of business education contents. The result is presented in Table 1.

The result in Table 1 shows that the application of cognitive AI has a positive moderate relationship with the delivery of business education contents in federal universities in South South Nigeria (R =.574). The coefficient of determination indicates that the application of cognitive AI accounts for 33.0% of the variance in the delivery of business education contents (R² =.330). After adjusting for the sample size and number of predictor variables, the application of cognitive AI accounts for 32.8% of the variance in the delivery of business education contents (adjusted R² =.328). This finding indicates that the application of cognitive AI has a moderate relationship with the delivery of business education contents, with approximately 32.8% of the variation in content delivery associated with the application of cognitive AI. The remaining 67.2% of the variance is associated with other factors not included in the model. The standard error of the estimate of 2.661 indicates the average extent to which the observed values of business education content delivery deviate from the values predicted by the regression model.

Research question two

To what extent does the application of Voice on the PowerPoint (VOPPT) lecture relate to the delivery of business education contents in federal universities in South South Nigeria?

The research question is answered using the coefficient of determination. The analysis is based on respondents’ ratings of questionnaire items 8–14 on the application of Voice on the PowerPoint (VOPPT) lecture and items 29–39 on the delivery of business education contents. The result is presented in Table 2.

The result in Table 2 shows that the application of Voice on the PowerPoint (VOPPT) lecture has a positive moderate relationship with the delivery of business education contents in federal universities in South South Nigeria (R =.626). The coefficient of determination indicates that the application of VOPPT lecture accounts for 39.2% of the variance in the delivery of business education contents (R² =.392). After adjusting for the sample size and number of predictor variables, the application of VOPPT lecture accounts for 39.0% of the variance in the delivery of business education contents (adjusted R² =.390). The finding indicates that the application of VOPPT lecture has a moderate relationship with the delivery of business education contents, with approximately 39.0% of the variation in content delivery associated with the application of VOPPT lecture. The remaining 61.0% of the variance is associated with other factors not included in the model. The standard error of the estimate of 2.534 indicates the average extent to which observed content delivery scores deviate from the values predicted by the regression model.

Research question three

To what extent does the application of Skillshare relate to the delivery of business education contents in federal universities in South South Nigeria?

The research question is answered using the coefficient of determination. The analysis is based on respondents’ ratings of questionnaire items 15–21 on the application of Skillshare and items 29–39 on the delivery of business education contents. The result is presented in Table 3.

The result in Table 3 shows that the application of Skillshare has a positive moderate relationship with the delivery of business education contents in federal universities in South South Nigeria (R =.578). The coefficient of determination indicates that the application of Skillshare accounts for 33.5% of the variance in the delivery of business education contents (R² =.335). After adjusting for the sample size and number of predictor variables, the application of Skillshare accounts for 33.3% of the variance in the delivery of business education contents (adjusted R² =.333). The finding indicates that the application of Skillshare has a moderate relationship with the delivery of business education contents, with approximately 33.3% of the variation in content delivery associated with the application of Skillshare. The remaining 66.7% of the variance is associated with other factors not included in the model. The standard error of the estimate of 2.652 indicates the average extent to which observed content delivery scores deviate from the values predicted by the regression model.

Research question four

To what extent does the application of Google Forms relate to the delivery of business education contents in federal universities in South South Nigeria?

The research question is answered using the coefficient of determination. The analysis is based on respondents’ ratings of questionnaire items 22–28 on the application of Google Forms and items 29–39 on the delivery of business education contents. The result is presented in Table 4.

The result in Table 4 shows that the application of Google Forms has a strong positive relationship with the delivery of business education contents in federal universities in South South Nigeria (R =.804). The coefficient of determination indicates that the application of Google Forms accounts for 64.6% of the variance in the delivery of business education contents (R² =.646). After adjusting for the sample size and number of predictor variables, the application of Google Forms accounts for 64.5% of the variance in the delivery of business education contents (adjusted R² =.645). The finding indicates that the application of Google Forms has a strong relationship with the delivery of business education contents, with approximately 64.5% of the variation in content delivery associated with the application of Google Forms. The remaining 35.5% of the variance is associated with other factors not included in the model. The standard error of the estimate of 1.934 indicates the average extent to which observed content delivery scores deviate from the values predicted by the regression model.

Research question five

What is the joint relationship among the application of Cognitive AI, Voice on the PowerPoint (VOPPT), Skillshare, Google Forms and delivery of Business Education contents in federal universities in South-South Nigeria?

The research question was answered using the coefficient of multiple determination (R²), adjusted R² and multiple correlation coefficient (R) obtained from multiple linear regression analysis. The analysis was based on the data generated from responses to questionnaire items 1–28 measuring the application of Cognitive AI, Voice on the PowerPoint (VOPPT), Skillshare and Google Forms, and items 29–39 measuring the delivery of Business Education contents. The result is presented in Table 5.

The result in Table 5 shows a strong joint relationship between the application of Cognitive AI, Voice on the PowerPoint (VOPPT), Skillshare and Google Forms and the delivery of Business Education contents in federal universities in South-South Nigeria, as indicated by the multiple correlation coefficient of.810. The coefficient of determination (R²) of.656 indicates that the four digital instructional applications jointly account for 65.6% of the variance in the delivery of Business Education contents. The adjusted R² of.652 further indicates that, after adjustment for the number of predictors and sample size, the four applications jointly account for 65.2% of the variance in the delivery of Business Education contents. The adjusted R² of.652 indicates that, after adjustment for the number of predictors and sample size, 65.2% of the variance in the delivery of Business Education contents is accounted for by the four predictors, leaving 34.8% unaccounted for after adjustment. The result therefore indicates that Cognitive AI, Voice on the PowerPoint (VOPPT), Skillshare and Google Forms collectively have a strong relationship with the delivery of Business Education contents in federal universities in South-South Nigeria. In terms of their individual contributions within the joint model, Google Forms recorded the strongest positive unique contribution (β =.808), followed by Skillshare (β =.185), while Cognitive AI (β = −.107) and VOPPT lecture (β = −.062) did not make statistically significant unique contributions when the four predictors were considered simultaneously. The findings show that the four digital instructional applications jointly explain 65.6% of the variation in the delivery of Business Education contents, with 65.2% remaining after adjustment. Thus, the joint application of Cognitive AI, Voice on the PowerPoint (VOPPT), Skillshare and Google Forms demonstrates a strong relationship with the delivery of Business Education contents in federal universities in South-South Nigeria.

Null hypothesis one

Application of cognitive AI does not significantly relate to the delivery of business education contents in federal universities in South South Nigeria.

To test this hypothesis, simple linear regression analysis is applied, with the application of cognitive AI as the independent variable and the delivery of business education contents as the dependent variable. The F test is used to determine the significance of the overall regression model, while the t test is used to determine the significance of the regression constant and the predictive contribution of the application of cognitive AI. The results are presented in Table 6.

The results in Table 6 show that the application of cognitive AI has a positive moderate relationship with the delivery of business education contents in federal universities in South South Nigeria (R =.574). The regression model accounts for 33.0% of the variance in the delivery of business education contents (R² =.330), while the adjusted R² of.328 indicates that 32.8% of the variance is accounted for after adjusting for the sample size and number of predictor variables. The overall regression model is statistically significant, F(1, 304) = 149.685, p <.001. Therefore, the null hypothesis is rejected. This indicates that the application of cognitive AI significantly relates to the delivery of business education contents in federal universities in South South Nigeria. The regression coefficient for cognitive AI is positive and statistically significant (B =.935, SE =.076, β =.574, t = 12.235, p <.001), indicating that an increase in the application of cognitive AI is associated with an increase in the delivery of business education contents. The regression constant is also statistically significant (B = 61.174, SE = 3.605, t = 16.970, p <.001). Overall, the findings indicate that cognitive AI makes a significant positive contribution to the prediction of the delivery of business education contents.

Null hypothesis two

There is no significant relationship between the application of Voice on the PowerPoint (VOPPT) lecture and the delivery of business education contents in federal universities in South South Nigeria.

To test this hypothesis, simple linear regression analysis is applied, with the application of Voice on the PowerPoint (VOPPT) lecture as the independent variable and the delivery of business education contents as the dependent variable. The F test is used to determine the significance of the overall regression model, while the t test is used to determine the significance of the regression constant and the predictive contribution of VOPPT lecture. The results are presented in Table 7.

The results in Table 7 show that the application of VOPPT lecture has a positive moderate relationship with the delivery of business education contents in federal universities in South South Nigeria (R =.626). The regression model accounts for 39.2% of the variance in the delivery of business education contents (R² =.392), while the adjusted R² of.390 indicates that 39.0% of the variance is accounted for after adjusting for the sample size and number of predictor variables. The standard error of the estimate is 2.534. The overall regression model is statistically significant, F(1, 304) = 196.304, p <.001. Therefore, the null hypothesis is rejected. This indicates that the application of VOPPT lecture significantly relates to the delivery of business education contents in federal universities in South South Nigeria. The regression coefficient for VOPPT lecture is positive and statistically significant (B = 1.134, SE =.081, β =.626, t = 14.011, p <.001), indicating that an increase in the application of VOPPT lecture is associated with an increase in the delivery of business education contents. The regression constant is also statistically significant (B = 64.841, SE = 3.786, t = 17.128, p <.001). Overall, the findings indicate that the application of VOPPT lecture makes a significant positive contribution to the prediction of the delivery of business education contents.

Null hypothesis three

There is no significant relationship between the application of Skillshare and the delivery of business education contents in federal universities in South South Nigeria.

To test this hypothesis, simple linear regression analysis is applied, with the application of Skillshare as the independent variable and the delivery of business education contents as the dependent variable. The F test is used to determine the significance of the overall regression model, while the t test is used to determine the significance of the regression constant and the predictive contribution of Skillshare. The results are presented in Table 8.

The results in Table 8 show that the application of Skillshare has a positive moderate relationship with the delivery of business education contents in federal universities in South South Nigeria (R =.578). The regression model accounts for 33.5% of the variance in the delivery of business education contents (R² =.335), while the adjusted R² of.333 indicates that 33.3% of the variance is accounted for after adjusting for the sample size and number of predictor variables. The overall regression model is statistically significant, F(1, 304) = 152.850, p <.001. Therefore, the null hypothesis is rejected. This indicates that the application of Skillshare significantly relates to the delivery of business education contents in federal universities in South South Nigeria. The regression coefficient for Skillshare is positive and statistically significant (B = 1.124, SE =.091, β =.578, t = 12.363, p <.001), indicating that an increase in the application of Skillshare is associated with an increase in the delivery of business education contents. The regression constant is also statistically significant (B = 64.565, SE = 4.221, t = 15.297, p <.001). Overall, the findings indicate that the application of Skillshare makes a significant positive contribution to the prediction of the delivery of business education contents.

Null hypothesis four

There is no significant relationship between the application of Google Forms and delivery of business education contents in federal universities in South South Nigeria.

To test this hypothesis, simple linear regression analysis is applied, with the application of Google Forms as the independent variable and the delivery of business education contents as the dependent variable. The F test is used to determine the significance of the overall regression model, while the t test is used to determine the significance of the regression constant and the predictive contribution of Google Forms. The results are presented in Table 9.

The results in Table 9 show that the application of Google Forms has a strong positive relationship with the delivery of business education contents in federal universities in South South Nigeria (R =.804). The regression model accounts for 64.6% of the variance in the delivery of business education contents (R² =.646), while the adjusted R² of.645 indicates that 64.5% of the variance is accounted for after adjusting for the sample size and number of predictor variables. The standard error of the estimate is 1.934. The overall regression model is statistically significant, F(1, 304) = 555.639, p <.001. Therefore, the null hypothesis is rejected. This indicates that the application of Google Forms significantly relates to the delivery of business education contents in federal universities in South South Nigeria. The regression coefficient for Google Forms is positive and statistically significant (B = 1.475, SE =.063, β =.804, t = 23.572, p <.001), indicating that greater application of Google Forms is associated with higher delivery of business education contents. The regression constant is also statistically significant (B = 72.138, SE = 3.019, t = 23.897, p <.001). Overall, the findings indicate that the application of Google Forms makes a significant positive contribution to the prediction of delivery of business education contents.

Null hypothesis five

There is no significant joint relationship among the application of Cognitive AI, Voice on the PowerPoint (VOPPT) lecture, Skillshare, Google Forms and the delivery of Business Education contents in federal universities in South-South Nigeria.

To test this hypothesis, multiple linear regression analysis was applied, with the application of Cognitive AI, Voice on the PowerPoint (VOPPT) lecture, Skillshare and Google Forms as the independent variables and delivery of Business Education contents as the dependent variable. The F test was used to determine the significance of the overall regression model, while the t test was used to determine the significance of the individual regression coefficients of the four predictor variables. The results are presented in Table 10.

The results in Table 10 show that the application of Cognitive AI, Voice on the PowerPoint (VOPPT) lecture, Skillshare and Google Forms have a strong joint relationship with the delivery of Business Education contents in federal universities in South-South Nigeria (R =.810). The coefficient of determination (R²) of.656 indicates that the four predictor variables jointly account for 65.6% of the variance in the delivery of Business Education contents. The adjusted R² of.652 indicates that, after adjustment for the sample size and number of predictors in the model, the four variables jointly account for 65.2% of the variance in the delivery of Business Education contents. The adjusted R² of.652 indicates that, after adjustment for the number of predictors and sample size, 65.2% of the variance in the delivery of Business Education contents is accounted for by the four predictors, leaving 34.8% unaccounted for after adjustment. Since the obtained p value is less than the.05 level of significance, the null hypothesis is rejected. This indicates that the application of Cognitive AI, Voice on the PowerPoint (VOPPT) lecture, Skillshare and Google Forms jointly has a statistically significant relationship with the delivery of Business Education contents in federal universities in South-South Nigeria. The individual regression coefficients further show that Cognitive AI does not make a statistically significant unique contribution to the model (β = −.107, t = −1.395, p =.164). Similarly, VOPPT lecture does not make a statistically significant unique contribution to the prediction of delivery of Business Education contents (β = −.062, t = −.775, p =.439). Skillshare, however, makes a statistically significant positive unique contribution (β =.185, t = 2.921, p =.004), while Google Forms makes the strongest statistically significant positive unique contribution to the prediction of delivery of Business Education contents (β =.808, t = 14.746, p <.001). Therefore, the null hypothesis is rejected. The findings indicate that Cognitive AI, VOPPT lecture, Skillshare and Google Forms jointly have a significant relationship with the delivery of Business Education contents in federal universities in South-South Nigeria. Although the four predictors jointly explain 65.6% of the variance in the delivery of Business Education contents, only Skillshare and Google Forms make statistically significant unique contributions when all four predictors are considered simultaneously.

The major findings of the study show that the application of software technologies is significantly associated with the delivery of Business Education content in federal universities in South-South Nigeria, although the strength and unique contribution of the individual software technologies differ. Specifically, Cognitive AI, Voice on PowerPoint (VOPPT) lectures, Skillshare and Google Forms each demonstrated statistically significant positive relationships with Business Education content delivery when examined separately. Google Forms recorded the strongest individual relationship, followed by VOPPT, Skillshare and Cognitive AI. However, the multiple regression analysis revealed a more differentiated pattern. The four software technologies jointly had a strong and statistically significant relationship with Business Education content delivery, accounting for 65.6% of its variance. When considered simultaneously, Google Forms made the strongest significant unique contribution, followed by Skillshare, whereas Cognitive AI and VOPPT did not make statistically significant unique contributions. The findings therefore indicate that the application of software technologies is an important component of Business Education content delivery, but that the individual technologies do not contribute equally or independently when their shared effects are considered.

The finding that the application of Cognitive AI has a significant positive relationship with the delivery of Business Education content indicates that greater application of Cognitive AI is associated with improved delivery of Business Education content in federal universities in South-South Nigeria. Cognitive AI can support lecturers in generating, organising, adapting and presenting instructional content, thereby providing opportunities for more flexible and responsive approaches to content delivery. The finding is consistent with Chen et al. (2020), who, in their analysis of 45 scholarly articles, identified significant educational applications of artificial intelligence and its potential to transform teaching and learning processes. Similarly, Šorgo et al. (2023) reported the usefulness of technology-mediated approaches during the forced transition to remote learning occasioned by COVID-19. Fokides and Alatzas (2023) further emphasised the capacity of software-supported educational approaches to address learning gaps and facilitate effective delivery, while Masry-Herzallah (2023) documented the importance of technology in sustaining educational activities during major disruptions. Amahi et al. (2023) also reported a significant influence of Cognitive AI applications on instructional delivery in tertiary institutions in Delta State.

The present finding, however, requires a distinction between its separate and joint regression results. When Cognitive AI was examined independently, it demonstrated a positive moderate relationship with Business Education content delivery (R =.574; R² =.330) and made a statistically significant positive contribution (β =.574, p <.001). Thus, Cognitive AI alone accounted for 33.0% of the variance in content delivery. However, when Cognitive AI was entered into the multiple regression model together with VOPPT, Skillshare and Google Forms, its unique contribution was no longer statistically significant (β = −.107, p =.164). This should not be interpreted to mean that Cognitive AI has a negative relationship with Business Education content delivery. Rather, it indicates that the variance in content delivery associated with Cognitive AI overlaps considerably with variance associated with the other software technologies. Consequently, while Cognitive AI is significantly related to content delivery when considered independently, the present evidence does not establish it as a significant unique predictor when the four software technologies operate simultaneously.

The finding that the application of Voice on PowerPoint (VOPPT) lectures has a significant positive relationship with the delivery of Business Education content demonstrates the relevance of voice-enhanced PowerPoint software in communicating and presenting Business Education content. VOPPT combines visual presentation with lecturer narration, thereby enabling instructional content to be communicated through both textual and graphical representation and recorded verbal explanation. This combination may be particularly useful for Business Education subjects in which concepts, procedures and practical illustrations require sequential explanation. The finding is consistent with Cosgun (2017), who affirmed the usefulness of PowerPoint presentations in educational content delivery. Sulle and Wema (2023) similarly highlighted the relevance of voice-supported PowerPoint presentations in promoting learner engagement. Related evidence from Buschman et al. (2023), Weber et al. (2023), Adams et al. (2023), and Oguejiofor and Onyiorah (2023) further supports the use of technology-mediated presentation approaches in teaching and learning. However, the finding differs from Baker et al. (2018), who found no significant difference in learning outcomes attributable to instructional approaches involving PowerPoint. The difference may partly arise from the distinction between the outcome investigated in the present study, which is content delivery, and learning outcomes examined in the earlier study.

The separate regression analysis show that VOPPT had a positive moderate relationship with Business Education content delivery (R =.626; R² =.392), with a statistically significant positive coefficient (β =.626, p <.001). Thus, VOPPT independently accounted for 39.2% of the variance in content delivery. Nevertheless, its contribution changed considerably in the joint model. When Cognitive AI, Skillshare and Google Forms were simultaneously included, VOPPT did not make a statistically significant unique contribution (β = −.062, p =.439). Therefore, the finding supports the usefulness of VOPPT as a software technology associated with Business Education content delivery, but does not support the conclusion that VOPPT independently predicts additional variance in content delivery after the other software technologies are controlled.

The finding that the application of Skillshare has a significant positive relationship with the delivery of Business Education content indicates that the use of Skillshare software is associated with enhanced delivery of Business Education content. As a software platform that facilitates access to structured learning materials and project-oriented learning experiences, Skillshare can provide opportunities for learners to engage with content beyond conventional classroom presentation. Hamdi (2023) described Skillshare as a platform supporting professional and creative development, while Hurley (2024) highlighted its development into a global learning community. The finding is consistent with the studies of Sureephong et al. (2023), Angelopoulos and Bates (2023), Amos (2023), and Guigui et al. (2023), which reported positive applications of Skillshare and related software-supported learning environments. However, it contrasts with Okon (2022), who reported no significant effect of Skillshare in the context of team teaching. Such divergence may reflect differences in the instructional context, population, manner of software application and outcome variable examined.

In the present study, Skillshare demonstrate a positive moderate relationship with Business Education content delivery (R =.578; R² =.335), and its individual regression coefficient was statistically significant (β =.578, p <.001). More importantly, Skillshare retained a statistically significant positive unique contribution in the joint regression model (β =.185, t = 2.921, p =.004). This finding distinguishes Skillshare from Cognitive AI and VOPPT. Although its unique contribution is considerably smaller than that of Google Forms, Skillshare continued to explain a statistically significant proportion of variance in content delivery after the other three software technologies were taken into account. Thus, the evidence suggests that Skillshare has both a significant separate relationship and a significant unique relationship with Business Education content delivery.

The finding that the application of Google Forms has a strong positive relationship with the delivery of Business Education content represents one of the most substantial findings of the study. Google Forms record the highest individual correlation with content delivery (R =.804) and explained 64.6% of the variance in the dependent variable. The finding is consistent with Awwad et al. (2022), who reported the usefulness of Google applications in academic activities, and Abdul and Hasan (2018), who demonstrated the applicability of Google-based software in curriculum-related assessment. It is also consistent with O'Neill et al. (2023), Chang and Tseng (2023), Yu et al. (2023), and Camilleri and Camilleri (2023), who documented positive applications of Google-based technologies for assessment, participation, feedback and other educational activities. Corning et al. (2023) and Gobbo et al. (2023) further identified the usefulness of software applications in managing instructional demands and evaluating academic activities.

The strong relationship observed in the present study may be associated with the versatility of Google Forms in supporting several aspects of Business Education content delivery. The software can facilitate the administration of quizzes, formative assessments, feedback activities, opinion gathering, learner responses and rapid compilation of instructional information. These functions enable lecturers to obtain information from students and respond to learning needs during the content-delivery process. Consequently, Google Forms may strengthen the interaction between content presentation, assessment and feedback.

The multiple regression result provides particularly strong evidence of the importance of Google Forms. It recorded the largest statistically significant unique contribution among the four software technologies (β =.808, t = 14.746, p <.001). Thus, unlike Cognitive AI and VOPPT, Google Forms retained a substantial independent association with Business Education content delivery after the other software technologies were entered into the model. This suggests that the application of Google Forms is not merely reflecting a general tendency to use software technologies; it has a distinct explanatory contribution within the combined software-technology model examined in this study.

The fifth finding demonstrates that the application of Cognitive AI, VOPPT, Skillshare and Google Forms has a strong and statistically significant joint relationship with the delivery of Business Education content. The multiple correlation coefficient of.810 indicates a strong relationship between the four software technologies collectively and Business Education content delivery. The coefficient of determination of.656 shows that the four applications jointly accounted for 65.6% of the variance in content delivery, while the adjusted R² of.652 indicates that 65.2% remained accounted for after adjustment for sample size and the number of predictors. The overall regression model was statistically significant, F(4, 301) = 143.634, p <.001. This provides strong empirical evidence that the application of these software technologies collectively is significantly related to the delivery of Business Education content in federal universities in South-South Nigeria. The finding supports Wang and Wang (2023), who emphasised the contribution of software technologies to educational processes and performance. It also accords with Giannakouli et al. (2023) and Lee (2023), who demonstrated the relevance of software technologies to teaching, learning and assessment. The present study extends this body of evidence by showing that four specific software technologies collectively explain a substantial proportion of the variation in Business Education content delivery.

Nevertheless, the individual coefficients in the joint model provide an important qualification. Google Forms made the strongest statistically significant unique contribution (β =.808, p <.001), while Skillshare also made a significant positive unique contribution (β =.185, p =.004). Cognitive AI (β = −.107, p =.164) and VOPPT (β = −.062, p =.439), despite their significant positive relationships in the separate regression analyses, did not make statistically significant unique contributions in the joint model. The negative signs of their coefficients should therefore not be interpreted as evidence of negative relationships with content delivery. Rather, they indicate that, after the shared variance among the four software technologies is not taken into account, Cognitive AI nor VOPPT contributes statistically significant additional explanatory power. This distinction is central to the interpretation of the study. The findings establish that each of the four software technologies is significantly related to Business Education content delivery when considered separately, but they do not establish that each independently predicts content delivery when all four are considered simultaneously. The joint result is therefore best understood as evidence of the collective importance of software technology application, with Google Forms and Skillshare providing the significant unique contributions within the combined model.

The findings demonstrate that the application of software technologies is significantly associated with the delivery of Business Education content in federal universities in South-South Nigeria. Google Forms emerged as the most prominent software technology in both the separate and joint analyses, while Skillshare demonstrated an additional significant unique contribution in the joint model. Cognitive AI and VOPPT showed meaningful separate relationships with content delivery but did not retain statistically significant unique contributions after the other software technologies were controlled. The findings consequently support a differentiated understanding of software technology application in Business Education: the value of a software technology may be evident when it is used independently, yet its distinct contribution may diminish when other related software technologies are simultaneously applied. This reinforces the need for universities and Business Education lecturers to consider the specific instructional functions and complementary roles of software technologies rather than assuming that every technology will make an equivalent independent contribution to content delivery.

Implications of the findings

The findings have practical implications for the major stakeholders in Business Education. For students, the application of software technologies such as Cognitive AI, VOPPT, Skillshare and Google Forms can support more accessible, interactive and responsive delivery of Business Education content. For university administrators, the findings justify sustained investment in appropriate software technologies, reliable internet connectivity, staff training and technical support, with particular attention to software applications that demonstrate strong practical contributions to content delivery. For lecturers, the findings emphasise the need to develop competencies in selecting and integrating software technologies according to specific instructional purposes rather than adopting technologies indiscriminately. For curriculum planners, the findings support the deliberate integration of relevant software technology competencies and applications into Business Education curricula to keep content delivery current, flexible and practice-oriented. For government and educational policymakers, the findings provide empirical support for policies and funding initiatives that strengthen software technology infrastructure and continuous professional development for university lecturers. For researchers and scholars, the findings provide empirical evidence for further investigation of the individual and combined contributions of software technologies to effective Business Education content delivery.

The study concludes that effective Business Education content delivery in federal universities in South-South Nigeria increasingly depends on the purposeful integration of software technologies into instructional practice. The evidence demonstrates that software technology application has moved beyond being merely an optional enhancement to becoming an important component of contemporary university teaching. Its value lies in enabling lecturers to present, communicate, assess, adapt and reinforce Business Education content through more flexible and responsive approaches.

The study further concludes that the educational value of software technologies is determined less by the mere availability of technology than by how appropriately it is selected, integrated and utilised for specific instructional purposes. Consequently, universities should move beyond technology acquisition towards strategic utilisation, lecturer competence development, institutional support and sustained integration of relevant software technologies into Business Education practice. Such an approach will strengthen the responsiveness and relevance of Business Education and better position its graduates to function effectively within an increasingly technology-driven workplace.

Recommendations

Federal universities should institutionalize the application of Cognitive Artificial Intelligence (AI) in Business Education through approved institutional guidelines that specify appropriate uses for content development, explanation, illustration and learner support. Departments should provide lecturers with access to suitable AI tools, training on responsible use, and clear procedures for verifying AI-generated content to ensure accuracy, relevance and academic integrity.

University management should strengthen the application of Voice on PowerPoint (VOPPT) lectures by providing lecturers with appropriate presentation and audio-recording facilities, reliable electricity, suitable software and technical support. Business Education departments should encourage lecturers to develop concise, well-structured voice-over presentations for topics that require repeated explanation or independent student review.

Business Education departments should strengthen the application of Skillshare by identifying courses and learning activities that can appropriately complement classroom instruction. Lecturers should guide students in selecting relevant Skillshare learning materials, incorporate suitable project-based activities into coursework, and monitor students’ participation and completion rather than treating access to the platform as an end in itself.

University management and Business Education lecturers should expand the application of Google Forms for formative assessment, quizzes, feedback, assignments, surveys and rapid assessment of students’ understanding during content delivery. Departments should provide lecturers with practical training in designing effective Google Forms activities and using the responses generated to adjust instruction and provide timely feedback to students.

Federal universities should adopt an integrated software-technology implementation framework that combines Cognitive AI, VOPPT, Skillshare and Google Forms according to their distinct instructional purposes. Such integration should be incorporated into course delivery plans, supported through reliable internet and power supply, staff development and technical assistance, and periodically evaluated to determine which applications are producing meaningful improvements in Business Education content delivery.