International Students‟ Perception of A Post-92 University Registration Process

This study analyses variances among four demographic characteristics – age, gender, continent, and program of study on international students‟ perception of a Post-1992 UK university‟s registration process. Analysis was done with respect to six structured survey questions (dependent variables), which serve as dimensions for measuring students‟ perception. Theoretical assumptions were equally drawn from total quality management and lean synchronization as suggested improvement techniques for achieving quality service objectives of higher education institutions. Primary data was randomly collected through a well-structured questionnaire, after authenticating its validity. The data was obtained from one hundred and nine international students, while a two-way factor analysis of variance was used in testing six main hypotheses formulated with respect to the students‟ age, gender, continent, and program of study. Results show that no variances exist among students‟ perception of the registration process with respect to their age, gender, and program of study. However, their perception varies with respect to their continents. Implication of the study to higher education management were also discussed. Even though the sample is not representative of the entire international students‟ population of study, the study reveals aspects of universities‟ service operations that requires on-going improvement.


Statement of the Problem
Service firms make significant contributions to the economy of most nations (Casey et al., 2009;Chase & Apte, 2007;Janer et al., 2015;Kanji et al., 1999), thus strategically positioning them as crucial research area among management scholars. But to excel within the industry, service providers ought to address service quality better than competitors (Heizer et al., 2017). This can be done externally or internally. Mainstream research (Juran et al., 1974;Juran & De Feo, 2010;Ziegel, 1990) have always emphasized measuring external service performance with respect to customers" perceived level of satisfaction. While internal service quality measurements tend to focus on firms" internal service processes. Higher education institutions, as open systems, face both external pressure from competitors as well as internal challenges regarding students" satisfaction. Despite on-going research efforts attempting to offer legitimate pathways for improving service quality in higher education, determining the right improvement methods is still a long way ahead. This is because existing studies (Timiyo, 2016(Timiyo, , 2017Timiyo & Sriram, 2021) have often focused on the role leadership play towards achieving the quality objective of the institutions. Hence the studies fail to take into cognizance viewpoints of relevant stakeholders in higher education institutions.
The different stakeholders of higher education institutions include parents, students, faculty members and administrative personnel, each differ in their interpretations of what quality service ought to look like in higher education. Parents often define service quality of higher education institutions based on the ranking (often referred to inputs) of each school on some national survey reports as well as graduates" employment prospect (referred to as outputs). Students on the other hand view quality based on course delivery methods and categories of programs being offered (Chua, 2004). From a business perspective, faculty members and employers view quality with respect to the totality of services provided by education institutions including recruiting qualified candidates and giving them the right tasks and responsibilities to perform within the institution. However, even as major benefactors, thereis tendency to undermine students" evaluation of higher education service quality hence, this has often been ignored even by education service providers. Addressing students" concerns and dissatisfaction should be of paramount interest, not just to education service providers (Douglas et al., 2006;Fryer et al., 2007), but the government of any nation as well.

Significance of the Problem
On the part of government, the UK government formerly established the Higher Education Funding Council for England (HEFCE) mainly to address students" concern throughout the country. Even though the agency is dissolved, it played a key role in shaping the dialogue pertaining to higher education service quality, particularly from studets" perspectives. Through its National Student Survey (NSS), the HEFCE periodically monitor and measure students" opinions of UK education system. The body offers a forum where all duly registered honours students voice their opinions and suggest ways, they think UK HEIs can be improved for superior performance. Findings from such surveys formed the basis for allocating government funding resources among education institutions in United Kingdom. Even after successfully recruiting students, higher education institutions must keep students satisfied. One way of achieving this objective is through the provision of good quality services based on available resources (Roffe, 1998). Efficient customer service management is crucial to success in the service industry (Karimi et al., 2001). Despite access to modern technologies, placing them ahead of other service firms (Noraziah et al., 2011), higher education institutions still struggle to provide quality services. At least, particularly, to international students.

Aim and Objectives of the Research
One area that have often pose a challenge to international students is universities" registration process, especially online New Students" Registration Process (NSRP). The process has further been heightened by the current global health crisis called the coronavirus pandemic, which affected all aspects of education activities. Thus, forcing schools to transition to online platforms. This research examines the impact of four demographic factors namely age, gender, continent, and program of study, on a post-1992 university"s registration process. With the intent of determining whether these factors play any influential role in shaping the views of international students concerning the registration process of the school. Hence the fundamental question the study seeks to address is what impact does students" demographic factors has on how they view universities" registration Process? Apart from addressing six hypotheses formulating with respect to each demographic factor, the research also identified and suggested possible ways of addressing some of challenges students face during new students" registration process.

Literature Review
The second section of this paper reviews relevant literature on new studnts" registration process, service quality, and improvement techniques capable of enhancing the service quality of education institutions.

New Students' Registration Process
The role of New Students" Registration Process (NSRP) is to ensure that every student is fully registered into their choice of education institution of learning, having met all relevant academic and financial requirements, and ready to resume classes (Johnson, 2005). Improving the NSRP can ultimately lead to improving the overall students" experiences of education services. One way of doing this is by applying Continuous Improvement Techniques (CIT) to simplify the registration process (Tarí , 2008). This involves, sometimes, adopting quantitative techniques to effectively measure the quality of services in these institutions, while at the same time trying to simplify the service process (Terziovski, 2002). Continuous improvement techniques basically involve quantitative tools or models used in checking the functionality of service systems, in order to make necessary adjustments if variations occur within the process (Statit Quality Control, 2007).
Numerous studies (Slack et al., 2010;Slack & Brandon-Jones, 2018) have provided clear distinction between approaches and elements of improvement techniques, two of these will be further explored in this research. The studies categorized business process re-engineering, total quality management, lean synchronization, and six sigma as quality improvement approaches, while techniques such as scatter diagrams, benchmarking, Pareto principle, and Ishikawa diagrams were classified elements of quality improvement. These different, yet, interrelated classifications suggest that quality improvement objectives can be achieved through one or a combination of these elements. Also, their relevance cuts across industry spectrum as the tools have aided managerial decision-making process both within the manufacturing and service sectors (Grigg & Walls, 2007).
Studies (Goomas, 2012) suggest the benefits of adopting CIT towards improving service performance of education institutions are overwhelming. In the sense that, the techniques help to save students" valuable time and yields satisfactory service delivery. Most institutions often adopt improvement techniques like barcodes and iClickers to overcome unpleasant and hazardous experiences students face during On-line registration by (Meedzan & Fisher, 2009). Research (Brown & Marshall, 2008;Harvey & Eisner, 2011) show that HEIs experienced better students" advisement and satisfied learning outcomes when they inculcated Quality Enhancement Plans (QEP) into their program curriculum or respective courses.

Improvement Techniques: Lean Synchronization
Lean synchronization is a management technique which originated from the ideas of Total Quality Management (TQM) the philosophy that laid the foundation for achieving maximum results through continuous improvements. It was developed by the Japanese automobile industry Toyota but has entered every facet of the business world. The debate on whether lean differs from TQM is still an ongoing one that is being fostered by various management authors. Even though both concepts are change-oriented, there is contextual difference between them with respect to their specific operationalization (Dahlgaard-Park et al., 2006;Dahlgaard-Park & Pettersen, 2009). Dahlgaard-Park and Pettersen (2009) view lean as an elusive concept devoid of a precise definition, but essentially, it refers to the process of controlling organizations" resources based on customers" needs while at the same time reducing unwanted activities (like waste) from production processes. The concept is one of the most widely spread ideas among the various quality management concepts proposed by management scientists in the early 90"s.
The authors stated that lean differs from total quality management in the dimensions of quality, viewing organization as a system, employee, and quality of work, analyzing customers' needs, measuring improvement and performance, learning and continuous improvement. Unlike lean (whose emphasis is waste reduction), the main focus of total quality management is quality (Dahlgaard-Park et al., 2006). With respect to viewing organizations as systems, total quality management views organizations as systems, having different sections and departments integrated to best serve the customer.

Total Quality Management
Total Quality Management (TQM) incorporates the internal network or units in the organization, lean focuses on the market logistics down to the various channels of distribution. Thus, it pays close attention to the effective management of firms" supply chain and the internal workings of the organization. On the dimension of employees and quality of work, TQM emphasizes organizations show concern for employees, whereas lean maintain a very passive attitude towards employees. Lean tends to undermine the importance of human capital in organizations by focusing more on jidoka and heijunka. Jidoka is the process of "humanizing the interface between operator and machine" (Slack et al., 2010, p. 247), Heijunka, on the other hand, enhances the smooth flow of production. In terms of analyzing customers" needs total quality management pursues quality improvement based on customers" requirements, lean does not promote this ideology. Regarding performance measurement, TQM measures performance to identify problems likely to occur in the production process, lean proponents believe performance measurement is necessary only for planning purposes. On the dimensions of learning and continuous improvement Dahlgaard-Park and Pettersen (2009) argued that, unlike total quality management, lean does not place learning as a vital ingredient for the survival of organizations.

Methods
This research adopts a quantitative research approach to obtain survey data by randomly sampling the population of international students of post-92 university located in the city of Liverpool. The students were drawn from different departments of the school using quota sampling technique to arrive at a sample size of one hundred and twenty (120) students. Data was obtained using a structured questionnaire and analysed using tables, averages, and simple percentages. Six hypotheses were formulated and tested using the Statistical Package for the Social Sciences (SPSS), to conduct a Two-Way Analysis of Variance (ANOVA). The 120 students constitute those who duly answered and returned the survey questions posted on social media and the student union webpage, and they come from five different countries across two continents namely Africa and Asia. The students include only newly admitted undergraduate and postgraduate full-time students because they are more prone to the complexities of the registration process than old students.

Research Design and Instrumentation
A quantitative research approach was employed in this study. A structured and self-designed questionnaire titled "International Students" Perception of the New Students" Registration Process (ISPNSRP)" was used to obtain data for this study. It was divided into two sections namely "section A" and "section B". Section A contains the biodata (personal data) of the students (respondents) which includes questions of facts about them. While section B contains questions of the students" opinions about the issues of this research which were drawn from the literature review to specifically address the research questions and the hypothesis of this study. Interview schedules will also be used to explain attitudinal variables to respondents for empathy and clarity. It was flexibly designed to include both structured (closed-ended) and the unstructured (or open-ended) questions. Next, the paper addresses the acceptable sample size for the study.

Determining Adequacy of the Sample Size
To validate the suitability of the sample size, I conducted Factor Analysis (FA), which allowed me to determine whether the sample size was reasonably sufficient for the study. Outcome of the analysis show a test result of 0.756 using Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy (see Table 1.1). The outcome suggests that the sample size is relatively sufficient because according to the Kaiser-Meyer-Olkin Measure of Adequacy, a sample is considered suitable for a study if the resultant KMO value is greater than 0.6 (Pallant, 2011(Pallant, , 2020Pallant & Manual, 2007). A summary of the KMO analysis as a measure of sampling adequacy is presented below (see Table 1).

Test of Reliability
The reliability of the instrument was established by conducting a test re-test method. First, twenty questionnaires were distributed to the sample and retrieved later upon completion. Two weeks later I carefully administered the same questionnaires with the same set of questions to the same group of students. This was done to determine the level of consistency of their responses to the proposed sets of questions. Using; Reliability = (test score) ÷ (retest), a ratio of 0.98 was obtained indicating that the instrument was reliable. According to Kumar (2018) the closer the value of the calculated ratio is to 1, the greater the reliability of the research instrument. Similarly, the farther the value of the ratio is to 1, the less reliable the instrument tend to be. He added that reliability can also be calculated by finding the difference between test scores and the retest scores; thus, (Test score) -(retest) = 0. He stated that when the difference between test scores and retest scores is zero (0), it means that the instrument is completely (or 100%) reliable and accurate. Likewise, if the difference is one (1), then it means that the instrument is totally (or 100%) unreliable and inaccurate.

Descriptive Statistics Analysis
Among the 120 questionnaires distributed to the students, only one hundred and nine (109) duly completed copies were retrieved thus, the analysis was based solely on 109 copies. The distributions of the students based on their continents are presented below (see Table 2).  Table 2 shows that 60% of the sample constitutes students from Asian countries while 40% came from Africa, whereas Table 3 below shows the distribution of the students based on their countries of origin.  Table 3 reveals that out of the one hundred and nine (109) questionnaires that were retrieved, 70% came from students from Nigerian origin, making this the highest response rate. Followed by Ghanaian students whose response rate was 9% out of the total sample of study and Bangladesh students with a response rate of 8%. India represents 12% of the entire sample of study while, the lowest number of 1% came from a student from Myanmar. Next, I looked at the distribution of the sample based on their age range (see Table 4).  Table 4 reveals that a larger percentage of the students used for the study were between the age of 20-29 years, representing about 50.5% of the working sample size, followed by those whose ages fell within 30-39 years and again; this group represents 33.9% of the sample size. It shows that more than half of the sample was within the ages of 20-29 years. The next section addresses the gender characteristics of the sample of study (see Table 5).  Table 5 reveals the distribution of male students among the five countries was more in number compared to female students, indicating that 59% of the sample were male students while only 41% were female students. Thus, more than half of the sample for the study represents male students signifying that there are more male international students than females. Finally, I analysed the students" biodata based on their program of study, that is whether studying for an undergraduate, postgraduate (taught), or postgraduate research program. This information is presented in Table 6.  Table 6 shows that 56% of the respondents were postgraduate (taught) students while 42% were offering courses at undergraduate level. Thus, more than half of the study sample was postgraduate students while 2% of the study sample was a postgraduate (research) student. Next, the paper evaluates students" perception of the registration process in general. Excerpts of the questions drawn from the questionnaire are presented in Table 7 below.

Hypotheses Testing
The six hypotheses for this study were tested with respect to the independent variables namely students" age -A, gender -G, continent -C, and program of study -P, and the independent variables (i.e., the statements describing students" perception) using ANOVA. The dependent variables are further categorized as q1, q2, q3, q4, q5, and q6, where "q" denotes the statements presented in Table 7 respectively. Each statement is analysed separately.

q1: The registration process was quite flexible, and it was designed to meet the needs of international students.
The test result is shown in Table 8. Tests the null hypothesis that the error variance of the dependent variable is equal across groups.  .000 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a. Design: Intercept + Age + Gender + Continent + Programme + Age * Gender + Age * Continent + Age * Programme + Gender * Continent + Gender * Programme + Continent * Programme + Age * Gender * Continent + Age * Gender * Programme + Age * Continent * Programme + Gender * Continent * Programme + Age * Gender * Continent * Programme  .000 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a. Design: Intercept + Age + Continent + Gender + Programme + Age * Continent + Age * Gender + Age * Programme + Continent * Gender + Continent * Programme + Gender * Programme + Age * Continent * Gender + Age * Continent * Programme + Age * Gender * Programme + Continent * Gender * Programme + Age * Continent * Gender * Programme  Table 20. .000 Tests the null hypothesis that the error variance of the dependent variable is equal across groups a. Design: Intercept + Age + Gender + Continent + Programme + Age * Gender + Age * Continent + Age * Programme + Gender * Continent + Gender * Programme + Continent * Programme + Age * Gender * Continent + Age * Gender * Programme + Age * Continent * Programme + Gender * Continent * Programme + Age * Gender * Continent * Programme .000 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a. Design: Intercept + Age + Gender + Continent + Programme + Age * Gender + Age * Continent + Age * Programme + Gender * Continent + Gender * Programme + Continent * Programme + Age * Gender * Continent + Age * Gender * Programme + Age * Continent * Programme + Gender * Continent * Programme + Age * Gender * Continent * Programme  .000 Tests the null hypothesis that the error variance of the dependent variable is equal across groups a. Design: Intercept + Age + Gender + Continent + Programme + Age * Gender + Age * Continent + Age * Programme + Gender * Continent + Gender * Programme + Continent * Programme + Age * Gender * Continent + Age * Gender * Programme + Age * Continent * Programme + Gender * Continent * Programme + Age * Gender * Continent * Programme Decision Rule: If the significant p value is less than or equal to 0.05 then there is significant effect among a single dependent variable and the other independent variables. This means that the null hypothesis is refuted if there is a significant difference, and the null hypothesis is supported if there is no significant difference.

Discussion of Findings
The ANOVA test results are summarized and presented in Table 3. This was done with respect to the six hypotheses for the study, as expressed with respecgt to the age, gender, continent of origin, and program of study of each student who took part in the survey. Findings from hypothesis 1 revealed that the null hypotheses (H0) with respect to age, gender and program of study were supported. This suggests no differences exist among students" perception of the registration process based on the three independent factors of age, gender, and program of study. It means that there is no significant difference between the flexibility of the registration process and the age, gender, and program of study of students. Although, the null hypothesis with respect to continent (which is Ho1c) was refuted (see Table 3) so, the alternative (H1) was accepted. This means there is a significant difference between Asian and African students" perception on whether the registration process was flexible or not. Results from hypothesis 2 shows that no significant differences exist concerning whether the registration process was time consuming or not based on students" gender and program of study. This means that H02G and H02P were supported (see Table 3). Whereas H02A and H02C were refuted indicating that there is a significant difference between students" view on whether the registration process was time consuming or not and their age and continents of origin.
In hypothesis 3 there was no significant difference between the students" age, gender and program with regards to their registering within the stipulated time of registration. This means that H03A, H03G and H03P were supported (see Table 3) while H03C was refuted thus, the alternative (H1) was supported. This means that there is a significant difference with respect to students" continent of origin and whether or not they were able to register within the specified time for registration. Hypotheses 4 revealed that there were significant differences in students" age, continent and program of study and their general perception of the registration process. This means that H04A, H04C and H04P were refuted (see Table 3) while that of gender (H04G) was supported indicating that there was no significant difference concerning students" gender and their general perception of the registration process.
The results from hypothesis 5 reveals no significant difference among age, gender, and students" ability to effectively use computers and self-serviced machines. This means that H 0 5A and H 0 5G were supported, while H 0 5C and H 0 5P were refuted signifying the fact that there were significant differences with regards to students" ability to use computers and self-serviced machine and their continents and program of study. Thus, H 0 was refuted while H 1 was rather supported. Hypothesis 6 test results showed that there were no significant differences between students" age, gender, and program of study and whether or not they gained prompt assistance from staff. Hence, H 0 6A, H 0 6G and H 0 6P were supported whereas, H 0 6C was refuted. H 0 6C indicates that there is a significant difference between Asian and African students and whether they got assistance from staff. From the analyses it could be observed that continent seemed to have a re-occurring major interactive effect on how students perceive the registration process in general.

Limitations and Key Assumptions
Data for this research was obtained from international students who agreed to take part in the survey. This means the outcome might not be a true reflection of the views of all international students in the school. Furthermore, while only two gender orientations were represented in the study, the research did not deliberately exclude other types of gender orientations. It was left for each student to identify their preferences; hence the analysis is based on the exact representations of the survey data collected from the students. Therefore, it is assumed that only two gender types took part in the study.