Demographic influences on enrolment and completion of an online MBA leadership programme: an Australian university perspective

ABSTRACT

This paper examines the demographic factors of the subjects enroled and completed by an hyperflexible online MBA Leadership (L) programme for 1034 students at Central Queensland University using the deductive research methodology and statistical analysis.

Australian-born students are influenced by their industry experience when enroling in subjects, whilst overseas-born students are influenced by where they are located, suggests more focus on their career. The time students are enroled in the online MBA programme have the most significant influence on the number of subjects enroled and completed. However, age and gender do not influence subjects’ enrolment or completion rate.

KEYWORDS:

• Online MBA
• hyper flexibility
• demographics
• business schools
• enrolment and completion

1. Introduction

The Master in Business Administration (MBA) has long been lauded as a transformative educational experience, offering academic enrichment and a ticket to ascend in the corporate hierarchy. As noted, the MBA serves as a vehicle for many to rise in the corporate world (Maurya 2018; Robbins, Judge, and Campbell 2017). This paper will delve into the reasons behind the elevated stature of the MBA in the business realm and how it equips individuals with the tools necessary for corporate success. The MBA (Master in Business Administration) is a vehicle to rise in the corporate world (Kirkpatrick 2020). The completion of an MBA has been described as a rite of passage facilitating the transition from a comparatively junior role to a more senior one and is, therefore, an excellent example of how individuals may seek to put themselves ‘in the driving seat’ in their further career (Houldsworth et al. 2023). Globally, Business Schools within universities have grown in leaps and bounds in the past decade, fuelled by economic growth, globalisation, and Industry 4.0, delivering a range of business programmes (Lorange 2019).

The MBA remains a highly coveted qualification, attracting diverse students from business, engineering, medicine, science, and arts backgrounds (Alam, Giacosa, and Mazzoleni 2022). Available in both face-to-face and online formats, the allure of the MBA programme has remained strong over the years. It is widely regarded as a valuable academic pursuit and a strategic investment by fresh graduates and mid-level managers alike (Alvarado and Iñiguez 2022). Beyond mere managerial education, the MBA programme plays a pivotal role in shaping future leaders, equipping them with the necessary skills and knowledge for leadership roles (Jaser, 2021). This enduring appeal underscores the MBA's significance in the contemporary professional landscape, adding weight to this research.

Business Schools are a global growth sector in today's economy (Merkert, Hoberg, and Mahadevan 2023; Lorange 2019). Furthermore, looking at the future of an industry trying to guide graduate qualities, changing skill sets (Mahadevan, Bäumler, and Kotzab 2021) and smart career choices, it is necessary to look at its past (Merkert, Hoberg, and Mahadevan 2023). Students select MBA programmes to satisfy their concerns and improve their skills (Al-Mutairi and Saeid 2016).

Globally, business schools have reinvented themselves, driven by economic changes and Industry 4.0, disruptive innovation, Fintech, and Blockchain (Schlegelmilch 2020). Asian-based organisations focus on sales as performance measurement, whilst global organisations focus on efficiency (Mahadevan, Elias, and Samaranayake 2022). This suggests the difference in business practices across the globe needs different management education to meet its challenges. Likewise, the tightness of the labour market has directed the need for more flexibility (Lorange 2019). Furthermore, COVID - 19 has changed how organisations run their business, affecting virtually every industry worldwide (Saul, Xu, and Sun 2020). Whilst University budgets have been significantly impacted by the substantial drop in the number of overseas students coming to Australia, the Online MBA is increasingly becoming popular (Zhou 2021).

Furthermore, international higher education is the third-largest export industry in Australia (Shah, Mahadevan, and Cheng 2021). The CQU online MBA Leadership (L) programme under the Learning, Design, and Innovation Directorate is based on a hyperflexible model: the students can join the programme at any time during the year, and the completion date is not bound by terms or semesters with the completion of 11 units. The Moodle learning management system provides a centralised subject website accessible to all students (Ames 2016). This platform is considered the first to offer a ‘hyperflexible approach’ to course delivery and assessment, as stated by Professor Nick Klomp, the Vice Chancellor of Central Queensland University (Eggleton 2022). The ethics committee of CQUniversity has approved this research.

Since Massive Open Online Courses (MOOCs) gained popularity in 2012, their role in online education has sparked considerable debate (Jordan 2015). Additionally, research on student demographics, subject enrolments, and completion rates in online MBA programmes in Australia, especially those adopting the hyperflexible model at Central Queensland University, is limited. Investigating these areas is valuable for understanding the wider context of online MBA programmes. This research aims to highlight the distinctiveness of the CQU MBA (Leadership) online programme on a global scale, backed by empirical data.

The research begins with an extensive literature review that sets the groundwork for the study. This review involves a thorough analysis of existing scholarly work, which helps identify gaps in the current knowledge and frame the context of the research. Based on the insights gained from the literature review, three research questions (RQs) are formulated. These questions are designed to guide the investigation and focus on specific aspects that the research aims to explore and understand.

Following the establishment of the RQs, the next step involves developing a detailed methodology. This methodology is carefully crafted to ensure it is well-suited to address the three research questions effectively. It includes the selection of appropriate research methods, tools, and techniques, as well as outlining the process for data collection and analysis. This phase is critical as it lays out a clear and systematic approach for conducting the research, ensuring reliability and validity in the findings.

The research then proceeds to the presentation of findings. This section provides a comprehensive analysis of the data collected, interpreting the results in the context of the research questions and the broader field of study. The findings section is pivotal as it reveals new insights, confirms or challenges existing theories, and contributes to the body of knowledge in the field.

After presenting the findings, the research delves into the potential scope for future research. This part highlights areas not covered in the current study but are ripe for exploration, suggesting directions for subsequent research endeavours. It serves as a bridge between the current research and future studies, offering opportunities for further investigation.

Additionally, the research acknowledges its limitations. This aspect is crucial as it provides transparency about the boundaries and constraints of the study, such as the scope, methodology, or generalizability of the findings. Recognising these limitations is important for understanding the context in which the findings should be interpreted.

Finally, the research concludes with a summary of the key findings, their implications, and their contribution to the existing body of knowledge. The conclusion synthesises the entire research process and findings, providing a clear and concise end to the study while also offering reflections on its significance and impact in the field.

2. Literature review

Firstly, we will review the current research material on the online MBA programmes.

The ‘MBA School of the Future’ acknowledges the need to give customers what they want (Lorange 2019). However, the MBA has been one of the most in-demand postgraduate qualifications over recent decades, despite periodic economic crises (Alvarado and Iñiguez 2022) and the COVID-19 pandemic (Jaser, 2021).

2.1. Online MBA programs

Australia has been home to online MBA programmes since the 1990s, following the business school models of the United States (News 2021; Schlegelmilch 2020). The programmes’ costs vary, ranging from AUD$17,600.00 to AUD$33,600.00 for an online MBA (News 2021). Disruptive innovation has transformed education systems worldwide (Christensen et al. 2018). The CQU online MBA Leadership (L) programme stands out as the most affordable among the available options with the hyperflexible model.

CQUniversity has introduced a ground-breaking initiative called the MBA (Leadership), the first of its kind globally. Unlike Massive Open Online Courses (MOOCs), this programme is designed as a disruptive innovation project to provide access to higher education for students who may face challenges in pursuing traditional avenues. Students enroled in this programme study regular higher education courses without the constraints of fixed completion timelines (semesters). They can complete assessments at their own pace, with the only requirement being completing all necessary assessments within a specified timeframe in the MBA (Leadership) case within five years. Thus, it is crucial to critically evaluate the pedagogical approach and student support methods, focusing on continuous improvement and scholarly perspectives. Jordan (2015) found that completion rates in an online MBA vary significantly according to course length, start date and assessment types.

The global outbreak of the COVID-19 pandemic has significantly disrupted business schools worldwide, leading to a rapid transition to emergency remote teaching (Krishnamurthy, 2020). As a result, students find themselves confused as they adapt to the sudden changes in their learning environment. While many students value physical interaction and are willing to pay higher tuition fees to experience it, the current circumstances have necessitated a shift towards online education.

Online MBAs have become integral to business schools’ portfolios, and the number of students opting for an online version is expected to grow. The literature on the success factors for online MBAs focuses on case studies of particular programmes (Gurubatham and Williams 2020). Many online degree programmes were viewed as low quality compared to traditional programmes, and many universities were reluctant to implement them (Gurubatham and Williams 2020). Conversely, online MBA programmes allow students to earn their degree without altering their daily routines or relocating, as Coman et al. (2020) noted.

2.2. Reasons for choosing an online MBA program

Students’ decision-making process when selecting an MBA programme is influenced by various factors such as accreditation, flexibility, and service availability Iglesias, Entrialgo, and Müller (2021) (Eastman, Bocchi, and Rydzewski 2013). Accreditation, particularly the AACSB certification, validates the quality of distance education providers in higher education (Eastman, Bocchi, and Rydzewski 2013). Students perceive an AACSB-certified programme as a reputable option for pursuing an online MBA degree (Eastman, Bocchi, and Rydzewski 2013). Additionally, students tend to compare the quality of online MBA programmes with traditional ones, although research indicates little to no difference between the two formats. Online distance learning is often considered more rigorous and attracts disciplined students (Photopoulos et al. 2023). The Princeton Review (2023) points out 50 top online MBA programmes in the US. Likewise, the University Reviews (2022) lists RMIT online MBA as one of the top 10 in Australia.

Moreover, online graduate education has opened opportunities for individuals who may not have been able to pursue an MBA through traditional classroom settings (Dash 2000). The convenience offered by online programmes emerges as a primary consideration for many prospective students (Eastman, Bocchi, and Rydzewski 2013). In their study titled ‘Important Characteristics in An MBA Programme: The Perceptions Of Online MBA Students,’ Rydzewski, Eastman, and Bocchi (2010) focused on two critical areas of interest in online MBA programmes. Firstly, they explored the significance of various programme characteristics such as availability, programme quality, length, cost, and curriculum courses. Secondly, they examined how the importance of these characteristics varies among different demographic variables. This research draws upon the insights Rydzewski, Eastman, and Bocchi (2010) and Moore and Greenland (2017) presented to inform their approach and analysis.

2.3. Synthesis of the literature review

When examining the broader landscape of MBA education on a global scale and within the specific context of Australia and online programmes, it becomes evident that existing research predominantly focuses on aspects such as programme cost, quality, and the range of courses offered based on Rydzewski, Eastman, and Bocchi (2010)’s thinking.

This observation leads us to formulate the first research question (RQ1): What is the current state of play of the students in the CQU online MBA (L) programme? To understand the student demographics comprehensively, it is necessary to delve into the current status of 686 Australian-born students (ABS) and 349 overseas-born students (OBS) within the online programme (shown in Table 1). This leads to RQ2: What is the state of play of the Australian-born students (ABS) in the CQU online MBA (L) programme, and RQ3: What is the state of play of overseas-born students (OBS) in the CQU online MBA programme?

Table 1. Categorisation of student demographics.

	Postcodes	Age of Student (years)	Industry Experience (years)	Country of Birth	Time enroled in the course	Gender
1	NSW Metro	< 24.9	0–5	Australia and New Zealand	0.1–0.5 years	Male
2	NSW Rural	Between 24.9 and 29.9	6–10	China, Hong Kong and Taiwan	0.6–1.0 years	Female
3	QLD Metro	Between 29.9 and 34.9	11–15	India, Pakistan, Sri Lanka, Bangladesh	1.1–1.5 years
4	QLD Rural	Between 34.9 and 39.9	16–20	Europe	1.6–2.0 years
5	Vic Metro	Between 34.9 and 39.9	21–25	Pacific Islands	2.1–2.5 years
6	Vic Rural	Between 44.9 and 49.9	26–30	South East Asia	2.6–3.0 years
7	Tas Metro	Between 49.9 and 54.9	31–35	Middle East	3.1–3.5 years
8	Tas Rural	Between 54.9 and 59.9	36–40	Africa	greater than 3.6 years
9	SA Metro	> 59.9	41–45	USA and Canada
10	SA Rural		46–50	South America
11	WA Metro		51–55	United Kingdom, Ireland and Scotland
12	WA Rural		>56 years	Unknown
13	ACT Metro
14	China
15	Overseas
16	NT Metro
17	NT rural

RQ2 and RQ3 aim to shed light on potential differences between ABS and OBS regarding their enrolment and completion of units. To visually depict the conceptual framework linking the number of completed and enroled units with the five demographic variables, Figure 1 has been developed.

Figure 1. Framework for research questions.

To address the three research questions (RQs) related to the online MBA programme, a robust methodology will be employed. The methodology will provide a systematic approach to gathering and analysing data to answer the research questions effectively.

Quantitative data will be collected through the student management system for the MBA programme at CQU. The data from the CQU MBA (L) online student system has 350 data points for analysis. This justifies the use of statistical methods. Statistical analysis techniques, including correlation analysis, Chi-square tests and ANOVA (analysis of variance) tests, will be applied to examine the relationships between variables and answer the research questions (Mahadevan, Elias, and Samaranayake 2022).

3. Methodology

There are numerous research papers on online MBA globally; however, they lack empirical research. Previous researchers have focused on the characteristics of the programme or what students look for. The uniqueness of this paper is that it focuses on the hyperflexible model MBA (L) of CQUniversity and examines the enrolment and completion rate of units.

The research adopted a two-stage approach to accomplish its objectives. Firstly, an extensive literature review was conducted to develop a conceptual framework examining the factors influencing unit enrolment and completion. The student system refers to the subject as ‘units’. Subsequently, empirical testing of the proposed framework was conducted utilising data obtained from the student system. The data collected is uploaded onto an Excel spreadsheet. The data collected were in categorical form, such as age group, industry experience, and time enrolled in the course, which require non-parametric tests for empirical analysis. This paper's empirical research involved a comprehensive application of statistical techniques, including correlation analysis and Analysis of Variance (ANOVA) tests. Mahadevan, Elias, and Samaranayake (2022) has applied non-parametric tests such as correlation analysis and ANOVA in their research.

Figure 1 provides an overview of the statistical analysis conducted within this study. It is worth noting that previous studies, such as those undertaken by Fernandes et al. (2021) and Bae (2019), have also employed correlation analysis and ANOVA tests to investigate relationships among factors using empirical data. Given the categorical nature of the data collected in this research, techniques such as exploratory factor analysis and structural equation modelling (SEM) were not applicable. The investigation unveiled a limited body of literature on Australian MBA education addressing student demographics. Ethical considerations will be addressed throughout the research process, ensuring confidentiality and informed consent of participants. Data protection protocols are followed, and ethical guidelines of research institutions will be adhered to.

4. Framework for hypothesis development

Research by Blass and Weight (2005) of online MBA students have factored in age and Industry Experience. Meanwhile, Rydzewski, Eastman, and Bocchi (2010) have included industry experience, ethnic group, and age group to study the quality of the MBA programme of a single business school in the US. Similar studies are not available for Australian Online MBA programmes.

In addressing each RQ, five hypotheses were developed by using the number of Units Enroled students as a dependent variable with industry experience, postcode, time enroled in the programme, age, and gender. Similarly, another five hypotheses are developed using the number of units completed (UC). Figure 2 shows the framework for hypotheses development.

Figure 2. Research methodology framework.

In building the hypotheses, Simpson et al. (2005); and Everett and Armstrong (1990) studied the influence of industry experience, gender, and age on the MBA (L) enrolment and completion rate in a traditional business school. Kelan and Jones (2010) have studied the importance of age and gender in the enrolment of online MBA program studied the importance of age and gender in the enrolment of MBA (L) programmes. Di Milia and Jiang (2022) also included age and gender in their studies undertaking clinical placement. However, these researchers have not investigated the number of units students enroled in and the number of UC concerning the student demographics.

The number of units enroled and completed units are the dependent variables. However, the industry experience, age, gender, postcode, and time students are enroled in the programme are independent variables termed as the factors: a total of seven variables will be examined in this research. The data we used for this research was collected over two periods, ranging from 01/01/20–31/12/21. The following hypotheses are developed for the Units Enroled and the student demographics. The ten hypotheses were developed for those students that are Australian-born and overseas-born. Based on Kelan and Jones (2010)’s work, we formed these hypotheses. Thus, there are a total of 30 hypotheses. The variables are abbreviated as UC – Units completed; UE – Units enroled; D1- Industry Experience; D2- Postcode; D3 – Time enroled in the course; D4 - Age; and D5 – Gender. The development of the 30 hypotheses is shown in Figure 3.

1. Ho: IE positively influences the number of UE.

2. Ho: Postcode positively influences the number of UE.

3. Ho: Time enroled in the MBA (L) programme positively influences the number of UE.

4. Ho: Age positively influences the number of UE.

5. Ho: Gender positively influences the number of UE.

Figure 3. Framework for development of 30 hypotheses.

The following hypotheses are developed for the number of units completed (UC) and the student demographics.

1. Ho: IE positively influences the number of UC.

2. Ho: Postcode positively influences the number of UC.

3. Ho: Time enroled in the MBA programme positively influences the number of UC.

4. Ho: Age positively influences the number of UC.

5. Ho: Gender positively influences the number of UC.

The 1034 students located globally comprised 685 students that are Australian born, and 389 were overseas born. The data was collected in the student system and was loaded onto an Excel spreadsheet. Using the student information on the country of birth, it was further sorted into Australian-born and overseas-born students for the seven variables.

Rydzewski, Eastman, and Bocchi (2010) used the following age groups to study MBA (L) students: 18–25; 26–30; 31–35; 36–40; and over 40. Further, the industry experience in years is divided into the following categories: 0–1; 2–3; 4–6; 7–9; and greater than 10. Using Rydzewski, Eastman, and Bocchi (2010) ‘s rationale, the authors have developed the categories as shown in Table 1. Each variable has been categorised as follows: postcodes - 1–17 categories; age - 1–9 categories; industry experience has 12 categories (0–56 years); time enroled in the course has eight types; country of birth 12 categories; and gender - 2 categories - Male or Female.

The postcodes of Australian-born students have been divided into metropolitan and rural. On the other hand, overseas locations have only metropolitan postcodes.

5. Descriptive analysis

Table 2 summarises the descriptive statistics for the student demographics variables. The maximum age of the student is 75.7 years, the average is 43.2 years, and the minimum is 24.4 years. This suggests that most students have substantial working experience before joining the CQU hyperflexible MBA (L) programme. The average industry experience is 24 years, and the maximum is 58 years, which relates to the student of 75.7 years (which is an outlier). The total time allowed to complete the 11 units is five years. The average time the students have been enroled in the programme is 1.6 years, and the maximum is 3.8 years. Thus, students can be enroled 76 per cent of the time (or 3.8 years) before completing the 11 units: the descriptive statistics confirm and support the hyper flexibility of the programme.

Table 2. Breakdown of Student demographics.

Breakdown of student demographics	Numbers
Global students	1034
Australian born students	685
Overseas born students	349
Male	631
Female	393
Australian Students in Metropolitan areas	411
Australian Students in rural areas	242
Students located in Australia	673
Students located overseas	361
Breakdown of student demographics by age/time	Years
Average age of students	43.2
Youngest student	22.7
Oldest student	75.7
Average Industry Experience leaving school	24.4
Minimum Industry Experience since leaving school	4.0
Maximum Industry Experience since leaving school	58.0
Average time in the system since enrolment	1.6
Maximum time in the system since enrolment	3.8
Minimum time in the system since enrolment	0.1
Time allowed for completion of 11 units	5.0
Breakdown by Programme	Percentage
Students in MBA programme is 982 (95%)	982 (95%)
Students in Graduate Diploma is 45 (4%)	45 (4%)
Students in Graduate certificate is 6 (1%)	6 (1%)

6. Statistical analysis

Table 3 displays the results of the correlation and statistical analysis conducted for three distinct scenarios: the total number of students or global students, Australian Born Students only, and students who are OBS (living in Australia and overseas). The subsequent investigation is divided into two sections to provide a comprehensive analysis. The first section examines the relationship between the ‘number of UE’ and various student demographic factors, while the second section focuses on the relationship between the ‘number of UC’ and student demographics.

Table 3. Results of statistical analysis of number of units and student demographics.

Dependent Variable	Factor	Correlation Analysis	Kruskal Wallis ANOVA	Post Hoc Pairwise	Correlation Analysis	Kruskal Wallis ANOVA	Post Hoc Pairwise	Correlation Analysis	Kruskal Wallis ANOVA	Post Hoc Pairwise
		Global Students (1034)			Australian Born Students (685)			Overseas Born Students (349)
Number of Units Enroled	Age	R = 0.7% P > 0.05	na	na	R = 11.4 P = 0.03 Sig at 0.01%	KWH = 67.592 P > 0.05	na	R = 8.5% P > 0.05	na	na
	Postcode	R = - 0.28% P > 0.05	na	na	R = 4.0% P > 0.05	na	na	R = - 15% P = 0.005 Sig at 0.01%	KWH = 752 P = 0.000	Pairwise shows that Students that enroled 1 and 2 units are influenced by postcodes.
	Gender	R = 0.7% P > 0.05%	na	na	R = 3.8% P > 0.05	na	na	R = −4.3% P > 0.05	na	na
	Industry Experience	R = 4.4.% P > 0.05	na	na	R = 12.7% P = 0.01 Sig at 0.01%	KWH = 54.2 P = 0.092	8 Units shows the greatest influence at P = 0.04 Sig at 0.01%	R = - 8.9% P > 0.05	na	na
	Time enroled in the course	R = 13.2% P = 0.000 Sig at 0.01%	KWH = 26.742 P = 0.000	Students enroled in 2 units are influenced by the time enroled in the course. P = 0.015	R = 65.8% P = 0.000 Sig at 0.01%	KWH = 1050 P = 0.000	8–11 units Sig at 0.05% 11 for category 1	R = 64.1% P = 0.000 Sig at 0.01%	KWH = 446 P = 0.000	7–11 units have greatest influence at P = 0.02
Number of Units Completed	Age	R- 1.6%	na	na	R = 8.8% P = 0.022 Sig at 0.05%	KWH = 35.00 P > 0.05	na	R = - 3.5% P > 0.05	na	na
	Postcode	R = −3.3%	na	na	R = - 3.6% P. > 0.05	na	na	R = 3.4% P > 0.05	na	na
	Gender	R = 1.6%			R = - 1.4% P > 0.05	na	na	R = 4.7% P > 0.05	na	na
	Industry Experience Since leaving school	R = 11.7% Sig at 0.01 P = 0.000	KWH = 61.9 P = 0.000	Students who have completed 11 units are influenced by IE p = 0.022	R = 9.9% P = 0.010 Sig at 0.01	KWH = 32 P > 0.05	na	R = −2.0%	na	na
	Time enroled in the course	R = 66.7% P = 0.000 Sig at 0.01%	KW = 381.0 P = 0.000	Students who have completed 7–10 units are influenced by the time enroled in the course. p = 0.000	R = 13.3% P = 0.000 Sig at 0.001%	KWH = 74 P = 0.015 Sig at 0.05%	Zero UC has the greatest influence	R- 16.2% P = 0.02 Sig at 0.01%	KWH = 63.4 P = 0.015	Accept

Table 3 provides the statistical analysis, where R represents the strength of the relationship between the variables supported by the p-value, which shows statistical significance when p < 0.05. Similarly, the Kruskall-Wallis ANOVA shows statistical significance (p < 0.005) for two groups of independent variables – the post hoc pairwise comparisons show which of these groups differ from each other.

In addressing (RQ1), within a sample size of 1034 students, a strong positive correlation (R = 13.2%, P = 0.000) was observed between the number of units in which students were enroled and the duration of their enrolment in the course. This ties in with the hyperflexible model at CQU. The results were further supported by a one-way Kruskal-Wallis analysis (ANOVA), which confirmed the statistical significance of this relationship (KWH = 26.7, P = 0.000). Subsequent pairwise comparisons revealed that the enrolment duration significantly influenced the number of units for students enroled in two units.

In line with previous research conducted by Babich (2019), the age of students at the time of enrolment did not demonstrate statistical significance. However, it is noteworthy that the students’ age strongly influences student persistence in completing units within the MBA (L) programme (with a p-value of 0.0068). Thus, the flexibility in the completion of 11 units. This is in line with McSporran and Young (2001) view of women and older students being more committed to online learning.

Those students that completed 11 units are influenced by their level of industry experience confirmed by pairwise post hoc texts, with a strong correlation of r = 11.7%, p = 0.000, sig at 0.01%. There is a strong correlation between the time enroled in the course and the number of units enroled by the student (r = 66.7%, p = 0.000, sig at 0.01%). The pairwise comparisons confirm that students who have completed 7–10 units are influenced by the time they are enroled in the programme. The authors argue that the hyperflexible business model supports the completion rate.

Research Question 2 (RQ2) examined the influence of age, postcode, and gender on the Unit Enrolment Rate (UE), for the Australian-born students. The findings revealed that none of these demographic factors significantly impacted UE. However, a strong positive correlation (r = 65.8%, p = 0.000) was observed between the number of units enroled (UE) and the duration of students’ enrolment on the course. This correlation was further supported by a statistically significant result from a Kruskal-Wallis test (KWH = 1050, p = 0.000). Subsequent pairwise comparisons indicated that students enroled in 8–11 units were significantly influenced by the duration of their enrolment.

Furthermore, there was a significant correlation (12.7%, p = 0.000) between the number of units enroled (UE) and the Industry experience (IE) of ABS students. This correlation was confirmed by a statistically significant result from a Kruskal-Wallis test (KWH = 54.2, p = 0.000). Pairwise comparisons demonstrated that students who had enroled in 8 units were significantly influenced by their level of industry experience. Thus reinforcing that mature-aged students can be more focused on completing the programme in a shorter time.

Regarding Unit Completion (UC), a moderate positive correlation (r = 13.3%, p = 0.000) was observed between UC and the duration of students’ enrolment in the course. This correlation was supported by a statistically significant result from a Kruskal-Wallis test (KWH = 74, p = 0.015). Pairwise comparisons indicated that students who had not completed any units were significantly influenced by the duration of their enrolment, suggesting that Australian-born students might delay their enrolment.

Moreover, there was a moderate positive correlation (r = 13.3%, p = 0.000) between the number of units completed (UC) and the Industry Experience (IE). However, the statistical analysis did not reveal a significant relationship between these variables (p > 0.05).

In addressing Research Question 3 (RQ3), the authors examined the Industry experience (IE), age, and gender of the number of units enroled by overseas-born students (OBS). The findings indicated that IE, age, and gender did not significantly impact the number of units enroled by OBS. However, a moderate positive correlation (r = 15.0%, p = 0.05) was observed between the number of units enroled by OBS and the postcode of their residence. This correlation was further supported by a statistically significant result from a Kruskal-Wallis test (KWH = 752, p = 0.000). The Pairwise comparisons demonstrated that their postcode location significantly influenced students enroled in 1 and 2 units.

Furthermore, a strong positive correlation (r = 64.0%, p = 0.000) was identified between the number of units enroled by OBS and their course enrolment duration. This correlation was statistically significant according to the Kruskal-Wallis test (KWH = 446, p = 0.000). Pairwise comparisons confirmed that overseas students enroled in 7–11 units were significantly influenced by the duration of their enrolment. However, age, gender, industry experience, and enrolment duration did not significantly influence the number of units completed by overseas-born students.

Moreover, a moderate positive correlation (r = 16.2%, p = 0.000) occurs between the number of units completed (UC) by OBS and their course enrolment duration. The Kruskal-Wallis ANOVA indicated a statistically significant relationship (KWH = 63.4, p = 0.000). However, the pairwise comparisons did not confirm a significant influence of the number of units on the duration of the enrolment. It is important to note that this relationship is accepted despite the lack of significant pairwise comparisons, as the data may have a skewed distribution. This aligns with the perspective proposed by Arulkadacham et al. (2021) and Babich (2019), suggesting that a student's previous educational background is a stronger predictor of their persistence towards graduation or completion of units.

7. Results and findings

The authors have employed MINITAB to establish robust evidence of reliability and validity, including conducting a Post-hoc Kruskall Wallis pairwise comparison as part of the analysis in this paper. This approach draws inspiration from the research conducted by Mahadevan, Elias, and Samaranayake (2022), which involved the utilisation of various categorical variables, such as age groups, to draw conclusions regarding supply chain collaborative practices.

The statistical and descriptive statistics confirm that age of the student and gender, with a male: female ratio of 1.6: 1, do not influence the number of subjects (units) enroled or completed, regardless of where the students were born. The oldest student in the CQU Online programme is 75, and the youngest is 22.9 years, averaging 43.2 years. This confirms that the programme is hyperflexible, which allows self-paced study, allowing students to complete the 11 subjects in 1.6 years.

On the contrary, McSporran and Young (2001) found that women and older students seem more motivated to study online, while male students need the discipline that classroom sessions provide. Moreover, young men tend to be more IT savvy than women.

The students’ industry experience influences the rate of units’ enrolment in the case of Australian-born students. However, this is not the case with the students who have completed the units. This suggests that Australian-born students take longer to complete their enroled units. Furthermore, Moore and Greenland (2017) found that unexpected employment challenges are the biggest driver of student dropout for online students in Australia. However, Moore and Greenland (2017) confirm those with more years of industry experience rated at a significantly higher level of importance than those with fewer years of industry experience.

Furthermore, it is also possible that these students are focussing on their careers. The social pressures of employment may hinder the process of completion. The time constraints of everyday life are a significant factor in determining if one can take on an MBA programme (Richardson, McGowan, and Styger 2018).

The time during which all the students (global, Australian, overseas) are enroled in the programme influences the rate of units’ enrolment and completion. Thus, it can be argued that Australian and overseas-born students could have different time frames for completing the units over the five years.

Their geographic locations or postcodes influence the rate of overseas-born students enroling in the programme. This suggests that students in metropolitan areas tend to enrol in the units faster than their rural counterparts: OBS living in Australia may not be in full-time employment, hence enroling in as many units as possible and seeking to complete the units in the shortest possible time.

8. Limitations and further research

The current student system at CQU lacks comprehensive data on the first-degree details and the industry affiliations of students enroled in the online MBA (L) programme. Incorporating this additional information would contribute to a more robust strategic understanding of the industry experience, specifically concerning the industry types in which the students are engaged in the future.

It should be noted that the analysis conducted in this study did not incorporate student grades. Integrating student grades would have facilitated an examination of their connection to enrolment and completion rates of the subjects, thus augmenting our understanding of these factors. There is potential for various mathematical models to be employed in analysing the relationship between enroled units and completed units. Such modelling can shed light on the factors influencing unit enrolment and completion.

It's important to acknowledge that the student data used in this study was gathered during the COVID-19 pandemic, from January 2020 to December 2021. As a result, it's essential to recognise that this specific timeframe may have implications for the research findings that require careful consideration. It's worth noting that the MBA (L) programme at CQU, being delivered online, may not have been significantly affected by the pandemic.

This research is grounded in data from a single university employing the hyperflexible model. Despite this, it yields statistically significant findings that underscore the impact of demographics on MBA (L) programme. Future research could benefit from conducting a comparative analysis of online MBA programmes, focusing on benchmarks related to subject enrolment and completion rates. Such an analysis would be instrumental in providing a deeper understanding of the efficacy and outcomes of these programmes.

9. Conclusions

This research underscores the disparities in enrolment and completion rates between Australian-born students and overseas-born students within the MBA (L) programme. Economic factors, primarily related to employment, emerge as a predominant influence on the decisions of Australian-born students. Moore and Greenland (2017) discovered that 35.8% of students withdrew from Australian open-access online courses due to shifting work commitments among this group. Other factors include social factors such as family-related matters.

Conversely, overseas-born students exhibit a strong commitment to full-time studies, focusing on swiftly completing the programme to enhance their employability in Australia. Statistical analysis further validates that the geographical location, specifically postcodes, impacts the enrolment rate of overseas-born students. This suggests that students residing in metropolitan areas tend to enrol in units more rapidly than their rural counterparts. Overseas-born students in Australia may opt for higher unit enrolments, aiming to expedite the programme's completion, especially if they are not engaged in full-time employment.

It is noteworthy that the timing of enrolment in the programme influences both the rate of unit enrolment and completion for all student categories, whether they are of global, Australian, or overseas origin.

10. Contribution to research

This research study effectively bridges two notable voids in the current academic literature. Firstly, it provides empirical evidence of unit enrolment and completion among 1034 students worldwide in the distinct hyperflexible MBA model. Secondly, it tackles the scarcity of Australian-based scholarly work in the realm of online MBA education, an area that has been previously underrepresented in academic research.

Acknowledgement

The authors would like to thank Professor Kate Ames, Dr Anja Pabel, and the MBA Leadership team at the Leadership, Design and Innovation Directorate for their expert strategic and operational management of this distinctive program on a global scale. The rigorous data collection process facilitated the authors in acquiring crucial insights into the program, enabling them to evaluate strategic opportunities for the continuous improvement of the CQ University Online MBA Leadership program.

Disclosure statement

There is no conflict of interest in publishing this paper.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Notes on contributors

Kumaraguru Mahadevan

Dr Kumaraguru Mahadevan has a multifaceted career that encompasses a broad range of expertise in consulting, business development, supply chain management, sustainability, project management, and operations management in multinational organisations in Australia. His research has attracted widespread recognition through numerous publications and presentations at international conferences, addressing topics such as supply chain management, Industry 4.0, higher education, business transformation, and sustainability. His teaching interests are Business Analytics, Operations Management, Supply Chain Management and Strategic Management.

Noal Atkinson

Noal Atkinson is the course manager for the MBA Leadership at Central Queensland University. The MBA Leadership at CQU is one of the largest MBA's in Australia, with over 1000 admitted students. In 2022, he was awarded the MBA Lecturer of the Year by the industry group MBA Aus. In 2023, he received the Vice Chancellors Award for Best Learning and Teaching awards. Noal's research tree has two main areas: teaching in higher education and health technology. Noal is in the final stages of his PhD with the topic “Better Health Care: Understanding Why Doctors Resist Technology and How Information Technology Resistance Affects Health Organisations”. Noal previously worked for Curtin University in the Master of Supply Chain Management program.