Transversal competences and employability: comparing in-person learning and distance education

Abstract

The demand for distance education has been increasing over the years, and this has gained momentum especially in the post-pandemic period. This development has caused a debate among the academicians as to whether distance education mode serves the real purpose of education as In-person learning does. Even though the method of schooling is not a consideration in most firms’ hiring decisions, the level of employability skills and competency are critical to landing a job in any sector. The primary goal of this study was to determine whether the mode of education caused a substantial difference in the competency and employability skills of the graduates/students. For this purpose, a sample of 350 In-person learning course students and 112 distance education students from Karnataka, India, was used in the study. Employability skills were measured using standard instrument with 10 dimensions and competency levels were measured using standard SLC Self-Evaluation Measurement with eight dimensions. This study found that the education method significantly impacts students’ competency and employability skills. Further, it demonstrates that distance education students have a higher competency level than In-person learning students. Conversely, students with In-person learning showed a higher level of employability skills. Distance education, however, is lacking in developing employability skills for various reasons. Nevertheless, using the right ed-Tech tools and software in distance education could boost effectiveness and keep the learners engaged and motivated.

Keywords:

• Distance education
• In-person learning course
• employability skill
• competence level
• transversal competencies

1. Introduction

The level of literacy is predominant for the development of any country (Gupta & Singh, 2013). However, some financial as well as non-financial factors may cause difficulties in accessing education through in-person learning mode for some sections of people (Babović, 2017; Hasani et al., 2020). Thus, distance education came as a boon to people who were otherwise deprived of in-person learning. The main distinction is that while distance education is education where students may not always be physically present at a college or where the learner and the teacher are separated in both time and distance (Tarchi et al., 2022), education through in-person learning mode is offered in a traditional classroom setup where a teacher could continuously monitor the progress made by the students (Yekta, 2017). The superiority of the two modes of education has been a debatable topic among academicians, researchers, policymakers and the public at large. This can be evaluated by comparing the level of competence and employability skills among the students of both the modes. Lately, the literacy rate of countries across the globe is increasing in a swift manner (Jafri et al., 2011; Maureen et al., 2020). However, there is a dubious disparity between the needs of the labour market and the employability of graduates seeking jobs (Mainga et al., 2022). This means merely acquiring education to get a qualification never guarantees the survival of the graduates in the job market. Therefore, any mode of education should inculcate the aspects which certainly enhance the transversal employability and competencies of graduates and thereby their survival can be ensured.

There are many underlying factors which influence the choice regarding the mode of education by a student (Bailey et al., 2014; Colorado & Eberle, 2012). They may be personal, accessibility, instructional, social, campus, environmental and demographic factors. Personal factors may be related to the purpose of education, confidence and learning ability of the students. Accessibility factors may consist of cost of education, geographical distance, and flexibility in time, work-life-study balance and so on. Instructional factors such as accessibility to educational materials, collaborative learning with other students, level of interaction with academic staff, access to IT services and career advice (Ali & Elfessi, 2004). Socializing and campus environmental factors include access to co-curricular facilities such as gym, swimming pool, club, etc., and socializing with student counterparts. Demographic factors may be gender, age, course, family income and so on (Gul & Bhat, 2019; Santibañez & Guarino, 2021; Sarkar et al., 2021).

Regardless of the mode of education chosen by the student, the education obtained should enhance the sustainability of students (Kim, 2021). In In-person learning mode of education, a learner gets accessibility to the learning environment physically and a clear-cut accountability can be observed. Whereas in distance mode a learner gets indirect accessibility to the learning environment and there will be no clear-cut accountability, as observed in previous studies (Broskoske & Harvey, 2000; Ohene & Essuman, 2014). It means that there are many pitfalls in distance mode of education such as less or no physical interaction, only self-motivational, limited chance of building oral and social interaction skills and so on (Spooner et al., 1998). However, such shortcomings can be avoided or regulated to a certain extent, if not completely, in In-person learning method of education. Further, studies have observed that nowadays both In-person learning and distance mode of education are more curriculum based than skill and value based (Garrison & Vaughan, 2008). In addition to this, there is a serious accusation on distance education which notes that the students use this mode just to secure a certificate, thus defeating the very purpose of education which is acquiring knowledge (Abrantes et al., 2022; Spooner et al., 1998). Therefore, there is a notion that in-person learning method of education is better over distance one. To prove or disprove this notion, no study has been conducted especially on the ground of competences and employability. In this study, an attempt has been made to examine the transversal competencies and employability by comparing in-person learning and distance education mode. This is a unique study, and the outcome of this study would assist the educational institutions, offering either in-person learning or distance learning, to redesign the curriculum. This aids in enhancing the transversal competencies and employability skills among the graduates. With this at the backdrop, the researchers have developed the following research questions and discussed them in the analysis part.

1.1. Research questions

Research gap was identified through the critical evaluation of related literature and following research questions formulated to address the gap.

RQ1:

Is there any difference in the competence level and employability skills of students of In-person learning and distance education modes?

RQ2:

How does the demographic profile of the students influence their Competence level and employability skills?

RQ3:

Is there any relationship between Competence and Employability Skills?

RQ4:

Does the mode of education impact the Competence and employability skills?

2 Literature review

2.1. Competences

To perform any given task efficiently and in a satisfactory manner, an individual is required to possess certain skills and abilities (De Vos et al., 2011). The term “Competence” has different meanings depending on the purpose for which it is used (Boahin & Hofman, 2013; Hoffmann, 1999). In the words of Woodruffe (1993) “Competence is a dimension of overt, manifest behaviour that allows a person to perform competently”. Therefore, Competence requires a person to have the ability as well as the desire to be competent. Mulder et al. (2007) noted competence as the ability to perform the required tasks and roles to the expected standards. Boahin and Hofman (2013) and Calero López and Rodríguez-López (2020) have defined competencies in two ways. According to them, competencies can be expressed as “behaviours that an individual needs to demonstrate” or “the minimum standards of performance”. Boahin and Hofman (2013) and Hoffmann (1999) observed competence as a dynamic process where an individual is not only able to perform a given task with skills but is also capable of developing, learning, and transferring skills and knowledge in different contexts too.

Transversal competences, also referred to as soft skills, relate to the ability of individuals to interact with others, like working effectively in teams, efficient communication and problem-solving (Belchior-Rocha et al., 2022). Employers in the knowledge economy give greater importance to graduates’ transversal competencies due to its notable benefits in terms of business performance, effectiveness in diverse teams and drive to innovation (García-Álvarez et al., 2022).Teijeiro et al. (2013) noted that generic competencies involve knowledge, skills and attitudes which are transferable and can be learnt, developed and applied in various contexts. They are critical in gauging the employability of an individual in the job market. According to UNESCO’s definition, competence is a combination of knowledge, skills and attitudes appropriate to the context and applying the learning outcomes in the personal as well as the professional contexts (Rosén et al., 2019). These skills may include problem-solving, critical thinking, interpersonal ability, global citizenship and ICT literacy (Abrantes et al., 2022). Therefore, the skills needed to perform a given task, called as competences, are acquired, or learnt by individuals and are applied where necessary. In fact, the focal of contemporary education and learning lies in equipping the students with competences which can be transferred across contexts (Belchior-Rocha et al., 2022).

Since education plays a key role in acquiring these skills, the mode through which the students obtain education can also play a crucial role. Past studies have found that both distance and in-person learning modes are effective in teaching skills to the individuals (Abrantes et al., 2022; Davidson & Palermo, 2015; Sarkar et al., 2021). McCutcheon et al. (2015) found that online learning for teaching clinical skills is as effective as traditional means. However, the study revealed that a wide variation was found in the type of online and blended learning approaches used to support students in developing clinical skills. In another study, Pei and Wu (2019) found a statistically significant difference between online and offline learning for knowledge and skill outcomes, however, they found no evidence to prove the superiority of offline learning over online learning. There are many students who get education through distance modes. There lies a difference in the facilities that they can access relative to the students who attend In-person learning colleges. The present study aims to find the influence of teaching-learning mode, that is, in-person learning and distance education modes, on the competence acquired by the students and the gap between the two groups based on their Competence levels.

2.2. Employability skills

The quality of human capital is critical in determining the well-being of any nation. The economic challenges across the world and globalisation are forcing employers to look for competent human resources (Jeswani, 2016). Many studies have stressed on the vitality of graduate employability and the social significance of acquiring employability skills (Barrie & Prosser, 2004; Calero López & Rodríguez-López, 2020; Harvey & Bowers-Brown, 2004; Yorke & Knight, 2007). Sinkovics et al. (2015) noted that the labour market policies and corporate strategies across the world have emphasized on employability and also a considerable amount of funding is being provided to educational institutions to prioritize employability outcomes in the curriculum. Yorke and Knight (2007) have defined employability as the suitability of graduates for appropriate employment. In a study by De Vos et al. (2011), it is noted that employability requires continuous fulfilling, acquiring or creating of work through the optimal use of competences. Civelli (1998) noted that possessing a degree does not assure that an individual is able to accomplish the related tasks successfully. This necessitates gauging the suitability of graduates to perform in the job market. Therefore, efficient application of the knowledge, skills and abilities to carry out the tasks and responsibilities of a given job as well as adaptability to changes is very important.

One of the barriers faced by the graduates trying to enter the job market is the gap between the skills acquired by them and the skills required in the workplace. In a market which is driven by technological advancements and constant change, occupation-specific skills alone are not sufficient for the graduates to meet the demands of job markets (Suarta et al., 2017). The stakeholders also expect the universities to ensure that their graduates possess soft or transferable skills suitable for the job market beyond possessing the technical competence (Samkin & Stainbank, 2016). Jackson (2014) emphasizes the crucial role of certain skills which can help the graduates in the application of their disciplinary knowledge in the workplace. The author noted that though many of the graduates have satisfactory disciplinary qualification, they lack in the “soft skills” which are key to attain success in the contemporary customer focussed market. Therefore, in addition to the subject specific skills and knowledge, the students have to acquire an additional set of skills called the employability skills to meet the evolving needs of the work environment (Suarta et al., 2017). A study by Martína (2014) found that the most sought-after skills in the job market are related to interpersonal relations, adaptability and negotiation skills. The author emphasized that communicative competence can greatly assist graduates in enhancing their social and occupational potentials alike. Wickramasinghe and Perera (2010) highlighted problem-solving, self-confidence, and working as a team member as the most important employability skills. In another study by Teijeiro et al. (2013) a mismatch was noted between the competencies acquired by the graduates and those required by the employers and the difference was most evident in case of problem-solving, application of knowledge to practical situations, the ability to work independently and interpersonal abilities. Suarta et al. (2017) have also emphasized on communication skills, decision-making and problem-solving skills and teamwork skills as the important employability skill attributes to be possessed by the graduates to enter and succeed in the workplace. Tymon (2013) noted that communication or interpersonal skills and teamwork are significantly linked with employability along with problem-solving, result orientation, adaptability, willingness to accept responsibility, creativity and self-confidence among others. Martína (2014) also suggested the significance of communicative competencies to be included in the new educational context in order to enhance the employability and productive force of graduates.

As noted by several authors (Calero López & Rodríguez-López, 2020; Jackson, 2014; Sinkovics et al., 2015; Tymon, 2013), educational institutions across the world have a strategic agenda of enhancing graduate employability as there is a considerable shift in industry expectations of graduates from exhibiting academic expertise to the ability to apply the acquired skills and knowledge and adapt to the changes in the workplace. Also, it is the responsibility of universities to respond to the labour market demands and prepare the students for future jobs (Martína, 2014). Calero López and Rodríguez-López (2020) noted that vocational education and training programmes focus on transversal competences in their curricula to enhance employability.

The method/mode of education can play a significant role in the acquisition of employability skills by the graduates. For instance, Kyaw et al. (2019) evaluated the effectiveness of digital education in developing the communication skills of medical students. They found low-quality evidence supporting digital education to be as effective as traditional learning in developing communication skills among medical students. In fact, they found blended digital education to be more effective than traditional learning for communication skills and knowledge.

2.3. Distance learning, competences and employability

In a study by Abrantes et al. (2022) involving graduates from the distance learning mode, the majority revealed a positive perception regarding competence development. More than 70% of graduates reported to have developed Competence relating to “fundamental knowledge of the field of study”, “analysis and synthesis”, “critical thinking, planning and innovation”, “communication”, “problem solving”, “ICT literacy” and “professional work methods’’. However, a lower value (<60%) was observed relating to “teamwork”, “job searching” and “ICT skills”. Further, most respondents reported that their degree improved their employability. Ofosuhene (2022) examining the state of employability among graduates of distance education found that though the employability of graduates has enhanced post training, most graduates depend on existing jobs in the market. They do not employ the skills they obtained through training in creating new jobs. However, in another study by De Vos et al. (2011), it has been suggested that employability also requires creating new jobs through optimal use of competences.

2.4. Competences, employability skills and demographic profile

The demographic factors such as age, gender and area of residence can have a significant influence on the employability skills of the graduates. Abd Majid et al. (2020) have, however, found no significant difference in the employability skills based on gender of the respondents. However, the authors found that the employability skill score of females was slightly higher than that of males. Interestingly, they found that the graduates from urban areas have higher employability skills than their rural counterparts. A possible reason for this could be that the students from urban areas can get exposure to certain facilities which students from rural areas cannot access. A study by Wickramasinghe and Perera (2010) noted that employability skills are influenced by gender of the graduates and there is significant difference in the importance given to various employability skills by male and female graduates. However, a study by Omar (2012) found no significant difference in employability skills based neither on gender nor on work experience. Therefore, the present study aims at understanding whether employability skills of graduates are influenced by the demographic variables.

As discussed earlier, Competence is a skill-set relating to the ability of individuals to interact with others, like working effectively in teams, efficient communication and problem-solving which can be learnt, developed and transferred. Therefore, the demographic variables can have its influence over an individual’s level of competence. In a study by Colwell and Gianesini (2011), demographic variables like age, gender, number of years of service and educational qualification were found to have a strong influence on different dimensions of Competence mapping, namely, productiveness, analytical ability, technical expertise, ability of planning and organizing and interpersonal skills. Similarly, Gujral and Saxena (2020) had found a significant correlation between competencies of mid-level disaster management professionals and demographic factors.

It has been believed that competencies gained through education or training would help enhance employability and competencies are pivotal in gauging the employability of graduates (Jayasingam et al., 2018; Teijeiro et al., 2013). Blokker et al. (2019) found a positive association between career competencies and perceived employability. However, Abrantes et al. (2022) noted a “double-direction relationship” between the two variables. According to the authors, higher employability perception by individuals could enhance the confidence of individuals about their competences. The present study also aims at revealing the relationship between competence and employability of the students.

3. Conceptual model and hypotheses

The impact of the method of education on the employability skills gained and Competence acquired by the students is unresearched. The extent and nature of this impact varied based on their demographic background like gender, economic status, residential status and learning interest (Abd Majid et al., 2020; Wickramasinghe and Perera, 2010; Colwell and Gianesini, 2011). On this assumption, researchers developed a working conceptual model to answer the research questions. In Figure 1, the method of education is an independent variable (IV) with two categories, that is, (a) In-person learning and (b) Distance education, whereas on the other side two dependent variables (DV), (a) Employability Skills and (b) Competences. Demographic variable is considered as Moderating Variable (MV) to understand the differences in Employability skills and level of competences among the parameters of demographic profile. The research is also aimed at knowing whether there is any relationship between employability skills and Competence level. Finally, this model helps to measure the impact of the method of education on level of employability skills and competence. By considering the conceptual model, we framed three hypotheses as follows,

Figure 1. Conceptual model.

First, students of In-person learning have higher level of employability skills and level of competences than students of distance education because they get more facilities, face-to-face interaction and live knowledge building activities (H₁ — RQ1).

Second, literature proved that demographic factors have a significant influence on the employability skills and competence level. Abrantes et al. (2022) and García-Álvarez et al. (2022) in their respective studies have identified that the male students have higher levels of employability skills. Similarly, Calero López and Rodríguez-López (2020) in his study showed that the economic status of students can cause variation in employability skills and Competence level (H₂ –RQ2).

Third, though not much study available to prove the relationship between employability skills and level of competences, the study carried out by Boahin and Hofman (2013) and Tymon (2013) showed positive correlation between employability skills and competences and it revealed that higher the competence higher employability skills (H₃—RQ3).

Fourth, having different levels of competence and employability skills between In-person learning and distance education does not imply that method of education has significant influence on competence and employability skills (H₄—RQ4). This hypothesis helps to identify the extent of impact on outcome variables if they exist. All these hypotheses are well depicted in Figure 1.

Alt text for Figure 1: There are total four variables written inside the blue box. First, the method of education as independent variable placed on the left side of the image. Employability and competencies as dependent variables in the middle of the image. Demographic variable as a moderating variable placed on the right side of the image. One-sided Arrows drawn from method of education to employability skill and competencies. Two-sided arrows drawn between employability and competencies. A one-sided arrow drawn from demographic variable indicating as moderating between employability and competencies.

4. Research methods

The researcher adopted the triangulation approach, where both qualitative and quantitative data were collected to answer the research question (Cooper et al., 2006). According to Tashakkori and Creswell (2007), the triangulation approach helps to better understand the situation compared to any single method.

4.1. Participants

Students who were pursuing their under-graduation and post-graduation courses (final semester) in different parts of Karnataka (India) were invited to provide their opinions on employability skills and competencies. A total of 500 students were invited randomly, and 462 students agreed to give information with a 92.4% response rate. Table 1 exhibits the summary of the demographic profile of the participating students and it depicts that 350 (75.8%) students are pursuing their course in In-person learning method of education and 112 (24.2%) students are getting their degree from Distance education mode. Among them, 60.6% are female students and 39.4% male students participated. The age profile of the respondents depicts that the majority (60.37%) of the participants lie in the age group of 21–25. The participants’ demographic profile showed that the majority of the students in Karnataka (India) are showing interest in pursuing education through in-person learning method rather than distance method of education.

Table 1. Sample profile

Demographic parameter	Categories	Frequencies (N)	Percentage (%)
Gender	Male	182	39.4
	Female	280	60.6
Age	Below 20 years	32	7.60
	21–25	282	60.37
	26–30	90	19.48
	Above 31 years	58	12.55
Course of the Study	Commerce and Management	278	60.17
	Science and Technology	94	20.35
	Arts and Humanities	66	14.29
	Professional/Career oriented/Any other	24	5.19
Annual Family Income	Less than 2 lakhs	220	47.62
	2 lakhs-5 lakhs	130	28.14
	Above 5 lakhs	112	24.24
Method of Education	In-person learning	350	75.8
	Distance	112	24.2

4.2. Instrumentation

The present study collected both primary data and secondary data to address the objectives. Primary data was collected using Questionnaire, which consisted of three sections A) Demographic Profile, B) Employability Skills and C) Competencies. Demographic details like gender, age, Course and Method of education were obtained in the first section, and the second section was devoted only to the students of distance education and asked them about the challenges faced by them during academic life. More importantly, Employability skills and Competencies were measured using standard questionnaires with minor modifications to suit the Indian context. Employability Skills framework adapted from Jackson and Chapman (2012) with Ten dimensions such as Working effectively with others (5 items), communicating effectively (6 items), Self-awareness (3 items), Thinking Critically (2 items), Analyzing data and using technology (3 items), Problem Solving (3 items), Developing initiative and enterprise (4 items), Self-management (4 items), Developing Professionalism (5 items) and Social responsibility and accountability (3 items). Similarly, Competencies were measured with the SLC Self-Evaluation Measurements by Seemiller (2016) with eight dimensions such as Learning and Reasoning (10 items), Self-Development (6 items), Interpersonal Interaction (11 items), Group Dynamics (4 items), Civic Responsibility (6 items), Personal Communication (7 items), Strategic Planning (6 items) and Personal Behavior (11 items). Employability skills measured by asking to rate for each statement from 5 (Very High) to 1 (very low) and Competencies measured by asking level of engagement “I did not”, “I did to some extent” and “I did”. The sample of items from both the constructs is depicted in Tables 2 and 3.

Table 2. The sample questions for employability skills

No.	Items	5	4	3	2	1
	Working with others
1	Solves problems without getting assistance from others
2	Enjoys the ‘give and take’ policy or working in a group.

Table 3. The sample questions for competences

No.	Items	I did not	I did to some extent	I did
	Learning and Reasoning
1	Reflect on the past to apply learning in the future.
2	Engage in systems thinking.

4.3. Procedure

An online survey conducted for students who are pursuing undergraduate and postgraduate programmes from the 10 State Government universities of Karnataka, India (University of Mysore, Karnataka University, Bangalore University, Mangalore University, Gulbarga University, Kuvempu University, Karnataka State Open University, Tumkur University, Davangere University and Karnataka State Akkamahadevi Women’s University). The name list and contact details of students were personally collected from the administrative offices of the universities and later used to prepare the sample frame. We identified more than one lakh students who were pursuing graduation through In-person learning mode and approximately 50,000 students pursuing their degree through Distance Education. Sample size is identified for In-person learning (n = 350) and Distance Education (n = 112) using Taro Yamane (1967) formula at 5% chance of error. Google Form was designed and distributed to the students over e-mail, WhatsApp and Telegram. With the Sampling Frame, researchers were able to follow Systematic Sampling technique to get the desired sample size. The collected responses from Google forms were exported into MS Excel, and the variables were coded accordingly (e.g.: Male = 1, Female = 2). After data purification (removing missing data and biased responses), the data were imported into SPSS 26 software for analysis and PLS-Smart 4 for Structural Equation Modelling.

5. Measurement model

Quality of the constructs in the study is assessed based on the evaluation of the measurement model. The assessment of the quality criteria starts with evaluation of the factor loadings which is followed by establishing the construct reliability and construct.

5.1. Factor loadings

Factor loadings indicate the relation of each item on the principal component, and it ranges between +1 and −1. Item factor loadings close to +1 indicates higher representation of the construct (Pett et al., 2003). Table 4 shows the factor loadings of all items representing sub-constructs of Employability Skills and Competencies. All items are significantly representing the constructs as the factor loadings of all items are higher than 0.5 as recommended by Hair et al. (2017) except items SRA1, SRA4 of Social responsibility and accountability, IP10 of Interpersonal Interaction, COMN2 of Communication effectively and PB1 of Personal Behaviour. Item loadings lesser than 0.5 were removed from further analysis.

Table 4. Factor loadings

Items	Sub-constructs of employability skills										Sub-constructs of competencies
	Working effectively with others	Communi-cating effectively	Self-awareness	Thinking critically	Analysing data and using technology	Problem solving	Developing initiative and enterprise	Self-manage-ment	Social responsibility and accountability	Developing profess-ionalism	Learning and reasoning	Self-development	Interpersonal interaction	Group dynamics	Civic responsibility	Communi-cation	Strategic planning	Personal behaviour
WE1	.844
WE2	.835
WE3	.880
WE4	.853
WE5	.783
COMN1		.786
COMN2		.428
COMN3		.854
COMN4		.822
COMN5		.912
COMN6		.766
SA1			.687
SA2			.853
SA3			.887
TC1				.906
TC2				.906
ADT1					.901
ADT2					.911
ADT3					.924
PS1						.792
PS2						.809
PS3						.864
DIE1							.864
DIE2							.859
DIE3							.829
DIE4							.816
SM1								.911
SM2								.874
SM3								.905
SM4								.830
SRA1									−.101
SRA2									.914
SRA3									.914
SRA4									−.013
DP1										.783
DP2										.822
DP3										.844
DP4										.821
DP5										.752
DP6										.802
LR1											.425
LR2											.679
LR3											.581
LR4											.719
LR5											.581
LR6											.714
LR7											.742
LR8											.732
LR9											.542
LR10											.744
SD1												.675
SD2												.680
SD3												.811
SD4												.854
SD5												.779
SD6												.716
IP1													.753
IP2													.657
IP3													.695
IP4													.700
IP5													.730
IP6													.736
IP7													.759
IP8													.822
IP9													.801
IP10													.358
IP11													.502
GD1														.622
GD2														.827
GD3														.741
GD4														.786
CR1															.807
CR2															.800
CR3															.627
CR4															.774
CR5															.747
CR6															.541
PC1																.773
PC2																.803
PC3																.764
PC4																.766
PC5																.714
PC6																.659
PC7																.649
SP1																	.753
SP2																	.688
SP3																	.764
SP4																	.762
SP5																	.787
SP6																	.753
PB1																		−.047
PB2																		.724
PB3																		.546
PB4																		.834
PB5																		.808
PB6																		.707
PB7																		.666
PB8																		.749
PB9																		.850
PB10																		.624
PB11																		.800

Note: Items in red colour denotes lower factor loading (<0.5).

5.2. Indicator multicollinearity

Test of multicollinearity helps to identify whether two or more independent variables are correlated with each other. If a high correlation exists, there is a problem. Therefore, Variance Inflation Factor (VIF) statistics helps to identify the multicollinearity among independent variables (Fornell, 1983). Since the VIF value of all indicators is not greater than threshold value of 5 as recommended by Hair et al. (2017), therefore multicollinearity is not an issue.

5.3. Reliability analysis

Reliability analysis helps to identify the consistency of the instrument. It indicates that if researchers collect information repeatedly using the same instrument at different situations, it provides the same result (Ruzafa-Martinez et al., 2013). The Cronbach's alpha and Composite Reliability test were used to check the reliability. Table 5 shows the Cronbach's alpha values ranging from 0.804 to 0.930, and composite reliability values ranging from 0.819 to 0.935. Since both the values are greater than required threshold level (0.7) as recommended by Hair et al. (2012), the reliability exists in the instrument.

Table 5. Cronbach's alpha and composite reliability

	Cronbach’s alpha	Composite reliability
Analysing Data	0.899	0.935
Civic Responsibility	0.836	0.879
Communication	0.858	0.894
Developing Professionalism	0.804	0.828
Developing initiative and Enterprise	0.863	0.906
Group Dynamics	0.811	0.897
Interaction	0.860	0.891
Interpersonal Interaction	0.887	0.908
Learning and Reasoning	0.846	0.876
Personal Behaviour	0.867	0.911
Problem Solving	0.859	0.861
Self-Awareness	0.839	0.819
Self-Development	0.847	0.887
Self-Management	0.903	0.932
Social Responsibility and Accountability	0.930	0.910
Strategic Planning	0.856	0.890
Thinking Critically	0.881	0.901
Work with Others	0.895	0.921

5.4. Construct validity

Convergent Validity and Discriminant validity evaluated under construct validity using PLS-SEM.

5.4.1. Convergent validity

Convergent validity helps to identify the degree of which items converge together to measure the construct. If they covary highly together, they validly measure the concept (Stöber, 2001). According to Fornell (1983), convergent validity will be established if Average Variance Extracted (AVE) is greater than or equal to 0.5. Table 6 exhibits AVE value of each item. AVE of all sub-constructs is greater than 0.5; therefore, convergent validity is not an issue.

Table 6. Convergent validity (AVE)

	Average variance extracted (AVE)
Analysing Data	0.828
Civic Responsibility	0.865
Communication	0.689
Developing Professionalism	0.884
Developing initiative and Enterprise	0.706
Group Dynamics	0.757
Interaction	0.903
Interpersonal Interaction	0.608
Learning and Reasoning	0.723
Personal Behaviour	0.548
Problem Solving	0.672
Self-Awareness	0.608
Self-Development	0.578
Self-Management	0.774
Social Responsibility and Accountability	0.689
Strategic Planning	0.576
Thinking Critically	0.828
Work with Others	0.706

5.4.2. Discriminant validity

Discriminant Validity helps to identify the degree to which each concept is distinct. If two concepts are distinct from each other it should not highly correlate with each other (Zaiţ & Bertea, 2011). There are different methods to check discriminant validity and in this study the researchers used Fornell and Larcker criterion. As per this method, the square root of AVE (Bold in italic) should be greater than correlation with all other concepts. All conditions are satisfied as mentioned in the Fornell and Larcker Criterion (Table 7), which proves Discriminant validity.

Table 7. Discriminant validity—Fornell and Larcker criterion

	Analysing data	Civic respon-sibility	Communication	Developing profess-ionalism	Developing initiative and enterprise	Group dynamics	Interaction	Interpersonal interaction	Learning and reasoning	Personal behaviour	Problem solving	Self-awareness	Self-development	Self-manage-ment	Social responsibility and accountability	Strategic planning	Thinking critically	Work with others
Analysing Data	0.91
Civic Responsibility	0.18	0.93
Communication	0.57	0.31	0.83
Developing Professionalism	0.46	0.33	0.62	0.94
Developing initiative and Enterprise	0.62	0.29	0.80	0.69	0.84
Group Dynamics	0.15	0.64	0.35	0.42	0.40	0.87
Interaction	0.07	0.72	0.14	0.20	0.17	0.49	0.95
Interpersonal Interaction	0.15	0.76	0.25	0.29	0.23	0.62	0.73	0.78
Learning and Reasoning	0.20	0.74	0.37	0.42	0.38	0.71	0.67	0.77	0.85
Personal Behaviour	−0.23	−0.49	−0.24	−0.34	−0.25	−0.39	−0.46	−0.63	−0.50	0.74
Problem Solving	0.80	0.24	0.71	0.58	0.80	0.28	0.13	0.22	0.30	−0.26	0.82
Self-Awareness	0.64	0.19	0.72	0.47	0.65	0.17	0.14	0.24	0.22	−0.27	0.65	0.78
Self-Development	0.05	0.72	0.10	0.21	0.17	0.49	0.85	0.71	0.66	−0.38	0.13	0.10	0.76
Self-Management	0.53	0.34	0.78	0.70	0.85	0.37	0.17	0.26	0.40	−0.27	0.68	0.69	0.17	0.88
Social Responsibility and Accountability	0.40	0.29	0.62	0.50	0.61	0.30	0.06	0.20	0.34	−0.20	0.58	0.46	0.08	0.63	0.83
Strategic Planning	0.14	0.73	0.24	0.28	0.22	0.59	0.80	0.97	0.73	−0.57	0.19	0.21	0.72	0.25	0.20	0.76
Thinking Critically	0.76	0.26	0.76	0.64	0.78	0.29	0.16	0.23	0.31	−0.26	0.78	0.70	0.13	0.69	0.51	0.21	0.91
Work with Others	0.60	0.33	0.79	0.52	0.75	0.35	0.18	0.27	0.37	−0.24	0.67	0.68	0.15	0.75	0.65	0.28	0.67	0.84

5.4.3. Validating higher order construct

Employability skills and Competencies are Higher-Order Constructs measured by other lower-order constructs. After validating the lower-order constructs, it is significant to assess the validity and reliability of Higher-order Constructs (Sarstedt et al., 2019). Higher-Order Construct developed by considering latent variable scores of lower-order construct. The Cronbach’s alpha value and Composite reliability value of both the constructs are greater than 0.7 and AVE is greater than 0.5, which establishes the reliability and convergent validity (Table 8). Further, the discriminant validity test was conducted using Fornell and Larcker Criterion and the result showed that the square root of AVE is higher than the correlation between the higher-order constructs (Table 9). Hence, discriminant analysis is established between the constructs.

Table 8. Higher-order construct reliability and convergent validity

	Cronbach’s alpha	Composite reliability	Average variance extracted (AVE)
Competencies	0.881	0.862	0.512
Employability	0.931	0.975	0.566

Table 9. Fornell and Larcker criterion—higher-order discriminant validity

	Competencies	Employability
Competencies	0.716
Employability	0.231	0.753

5.5. Normality test

A null hypothesis was developed stating that normality exists in the data at 5% significance level. Kolmogorov–Smirnov Test and Shapiro–Wilk Test were conducted to test the hypothesis. The test result showed (Table 10) that there is no significant deviation in the data from normal distribution (p > 0.05) for all sub-constructs. Therefore, researchers can use parametric statistical tools to test the hypotheses.

Table 10. Normality test result

	Kolmogorov–Smirnov^a			Shapiro–Wilk
	Statistic	df	Sig.	Statistic	df	Sig.
Analysing Data	.263	462	.120	.807	462	.335
Civic Responsibility	.248	462	.321	.845	462	.089
Communication	.246	462	.658	.806	462	.100
Developing Professionalism	.263	462	.078	.800	462	.210
Developing initiative and Enterprise	.223	462	.258	.829	462	.325
Group Dynamics	.290	462	.226	.791	462	.582
Interaction	.239	462	.421	.863	462	.452
Interpersonal Interaction	.243	462	.222	.795	462	.149
Learning and Reasoning	.264	462	.145	.803	462	.258
Personal Behaviour	.294	462	.458	.742	462	.478
Problem Solving	.262	462	.322	.759	462	.365
Self-Awareness	.300	462	.100	.742	462	.562
Self-Development	.278	462	.056	.808	462	.852
Self-Management	.257	462	.089	.801	462	.452
Social Responsibility and Accountability	.292	462	.147	.800	462	.562
Strategic Planning	.236	462	.785	.818	462	.582
Thinking Critically	.247	462	.253	.853	462	.729
Work with Others	.264	462	.192	.843	462	.092

^aLilliefors significance correction.

5.6. Data analysis and interpretation

SPSS 26 and Smart PLS-4 were used to analyse the data. Missing values were replaced with mean values in the data cleaning process and later descriptive analysis was conducted to assess the “Employability Skills” and “Competence” along with the t-test to check the significance difference.

Table 11 presents the employability skill level of respondents and it depicts overall Mean Score of “Working effectively with others” showed significantly (t = 0.982, p < .01) higher among the students of In-person learning (M = 4.06) than Distance education (M = 3.97). Similarly, the overall mean scores of other sub-constructs such as “Communicating effectively” (M = 4.16 > 3.92, t = 3.11, p < 0.01), Problem Solving (M = 4.03, t = 1.26, p < .000), Developing initiative and enterprise (M = 4.21 > 4.07, t = 1.35, p < 0.01), Thinking Critically (M = 4.12 > 3.93, t = 2.13, p < 0.01) and Developing Professionalism (M = 4.12 > 4.04, t = 1.56, p < 0.01) are also statistically significant and is higher among students of In-person learning than distance education. But, the “Self-awareness” showed significantly (t = 2.38, p < .05) higher among students of Distance education (M = 4.29) than In-person learning (M = 4.07). Self-management (M = 4.165 > 4.14, t = 0.92, p > 0.05) and Analyzing data and using technology (M = 3.93 > 3.74, t = 3.07, p > 0.05) showed insignificant mean difference between students of In-person learning and distance education. The overall mean score was calculated for main construct (Employability Skill) by considering all sub-constructs. The overall mean score showed higher among students of In-person learning (M = 4.10) than students of distance education (M = 3.99). The result of independent sample t-test indicated significant difference in the Employability Skills between students of In-person learning and Distance education (t = 1.79, p < 0.01). While analyzing the overall mean score, it was found that students of In-person learning have very high level of employability skills (Mean score above 4) and students of distance education have high level of employability skills (Mean score between 3.1 and 4).

Table 11. Employability skill level

Employability skill	In-person learning			Distance education			t (Sig.)
	N	Mean score	S. D	N	Mean score	S. D
(A) Working effectively with others
Task collaboration	350	4.07	.880	112	4.07	1.11	.028 (.978)
Team working	350	3.90	.925	112	3.71	1.04	−1.82 (.009)
Social intelligence	350	4.07	1.03	112	4.21	.776	1.38 (.007)
Cultural and diversity awareness	350	4.14	.974	112	3.93	.887	−2.07 (.009)
Influencing others	350	4.13	.867	112	3.93	.802	−2.13 (.004)
		[4.06]			[3.97]		0.982(0.007)
(B) Communicating effectively
Conflict resolution	350	4.17	.802	112	4.00	.759	1.93 (.054)
Verbal communication	350	3.89	.920	112	3.86	.746	0.29 (.765)
Giving and receiving feedback	350	4.18	1.022	112	3.93	.802	2.41 (.016)
Public speaking	350	4.14	1.018	112	3.50	1.245	5.45 (.000)
Meeting participation	350	4.34	.955	112	4.07	.887	2.61 (.009)
Written communication	350	4.23	1.001	112	4.14	.837	0.82 (.413)
		[4.16]			[3.92]		3.11 (.000)
(C) Self-awareness
Meta-cognition	350	4.23	.944	112	4.36	1.047	1.17 (.004)
Lifelong learning	350	3.98	.957	112	4.21	1.017	2.25 (.005)
Career management	350	3.99	1.035	112	4.29	.799	2.73 (.007)
		[4.07]			[4.29]		2.38 (0.03)
(D) Thinking Critically
Conceptualization	350	4.10	.946	112	3.93	.802	−1.76 (.079)
Evaluation	350	4.13	.943	112	3.93	1.037	2.35 (.002)
		[4.12]			[3.93]		2.13 (0.007)
(E) Analysing data and using technology
Numeracy	350	3.99	.918	112	3.79	.944	−1.88 (.061)
Technology	350	3.99	.955	112	3.79	.944	−2.08 (.038)
Information management	350	3.82	1.003	112	3.65	.842	−2.018 (.044)
		[3.93]			[3.74]		3.07 (0.321)
(F) Problem Solving
Reasoning	350	3.95	.865	112	3.71	1.035	0.99 (.323)
Analysing and diagnosing	350	3.97	.942	112	3.71	.963	2.46 (.006)
Decision making	350	4.18	.903	112	3.86	.746	1.17 (.002)
		[4.03]			[3.76]		1.26 (.000)
(G) Developing initiative and enterprise
Entrepreneurship–intrapreneurship	350	4.30	.983	112	4.07	.887	−1.14 (.254)
Lateral thinking–creativity	350	4.22	.998	112	3.93	1.105	−3.35 (.001)
Initiative	350	4.16	.919	112	4.36	.613	1.40 (.162)
Change management	350	4.15	.928	112	3.93	.802	−2.38 (.017)
		[4.21]			[4.07]		1.35 (.000)
(H) Self-management
Self-efficacy	350	4.20	.921	112	4.07	.596	−0.83 (.409)
Stress tolerance	350	4.24	.969	112	4.14	.642	−0.61 (.542)
Work–life balance	350	4.15	.942	112	4.14	.746	−0.97 (.331)
Self-regulation	350	4.07	.990	112	4.21	.560	0.64 (.523)
		[4.165]			[4.14]		0.92 (.256)
(I) Social responsibility and accountability
Social responsibility	350	3.98	.945	112	3.86	.642	−2.12 (0.035)
Accountability	350	4.10	.923	112	4.00	.849	0.23 (.820)
Personal ethics	350	4.11	.923	112	3.99	.885	−1.18 (.237)
Organizational awareness	350	4.09	1.01	112	4.08	.954	−0.19 (.849)
		[4.07]			[3.99]		1.89(.000)
(J) Developing Professionalism
Efficiency	350	4.09	1.023	112	4.00	.977	2.84 (.001)
Multitasking	350	4.11	.989	112	4.16	.822	2.504 (.004)
Autonomy	350	4.13	.928	112	4.04	.871	3.543 (.000)
Time management	350	4.19	.897	112	3.99	.864	2.01 (.000)
Drive	350	4.05	.933	112	3.99	.905	.571 (.568)
Goal and task management	350	4.16	.979	112	4.02	.905	1.25 (.003)
		[4.12]			[4.04]		1.56 (.000)
Overall Employability	350	{4.10}	.598	112	{3.99}	.399	1.79 (.005)

Notes: (1) Mean Score calculated for each item as well as for sub-constructs of employability skill. Mean Score calculated with the range of 1–5 and if Mean Score is less than 2 which means students have very low level of skills in that construct; Mean Score between 2.1 to 3 indicates low level of skills; Mean score between 3.1 to 4 indicates high level of skills and Mean Score above 4 indicates very high level of skills.

(2) The values within the [] shows the overall Mean score of Sub-constructs of Employability Skill and the value within the {} shows the overall Mean Score of Employability Skill.

(3) The value within the () shows the significance value of t statistics resulted from Independent Sample t test.

Table 12 exhibits higher “Learning and Reasoning” (M = 2.41 > 2.35, t = 1.02, p < 0.01) and Group Dynamics (M = 2.48 > 2.31, t = 1.26, p < .01) among students of In-person learning compared to those of distance education. Similarly, students of In-person learning revealed a higher civic responsibility (M = 2.44 > 2.36, t = 1.30, p < 0.01) and Communication (M = 2.51 > 2.37, t = 2.89, p < 0.01) than students of distance education. On the other hand, students of distance education have higher “Interpersonal Interaction” (M = 2.63 > 2.45, t = 1.26, p < .01), Strategic Planning (M = 2.52 > 2.47, t = 1.812, p < .01) and Personal behaviour (M = 2.48 > 2.42, t = 1.13, p < 0.01) than students of In-person learning. The overall Competence score indicated a high level of competence for both the group of respondents (Mean score lies between 2.1 and 3). However, students of distance education have higher competencies than the students of In-person learning (M = 2.50 > 2.41, t = 3.65, p < 0.01). The result of independent sample t-test indicated significant difference in the Competence between students of In-person learning and distance education. The results of independent test (Tables 3& 4) showed difference in Employability skill and Competencies, and thus, H₁ is accepted at 1% level of significance.

Table 12. Competence profile

Competencies	In-person learning			Distance education			t (Sig.)
	N	Mean	S. D	N	Mean	S. D
(A) Learning and Reasoning
Research	350	2.23	.724	112	2.36	.613	1.620 (.106)
Other Perspectives	350	2.33	.619	112	2.29	.703	−.658 (.511)
Reflection and Application	350	2.27	.689	112	2.07	.802	−2.603(.010)
Systems Thinking	350	2.54	.584	112	2.36	.613	2.895 (.004)
Analysis	350	2.50	.545	112	2.50	.502	−.049 (.961)
Synthesis	350	2.59	.578	112	2.36	.481	−3.831(.000)
Evaluation	350	2.43	.638	112	2.29	.592	−2.183(.024)
Idea Generation	350	2.40	.596	112	2.57	.625	.012 (.000)
Problem Solving	350	2.35	.604	112	2.29	.703	−.920 (.396)
Decision Making	350	2.46	.622	112	2.43	.625	−.507 (.614)
		[2.41]			[2.35]		1.023(0.159)
(B) Self-Development
Self-Understanding	350	2.61	.684	112	2.21	.776	−5.173(.000)
Personal Values	350	2.39	.650	112	2.29	.592	−1.57 (.101)
Personal Contributions	350	2.47	.622	112	2.29	.592	−2.739(.005)
Scope of Competence	350	2.48	.659	112	2.21	.677	−3.692(.000)
Receiving Feedback	350	2.41	.635	112	2.43	.732	.239 (.824)
Self-Development	350	2.60	.596	112	2.43	.625	−2.617(.012)
		[2.49]			[2.31]		3.29 (0.008)
(C) Interpersonal Interaction
Productive Relationships	350	2.47	.622	112	2.43	.625	−.591 (.556)
Appropriate Interaction	350	2.62	.553	112	2.50	.502	−1.994(.037)
Helping Others	350	2.52	.604	112	2.64	.481	1.962 (.029)
Empathy	350	2.50	.545	112	2.50	.630	−.046 (.966)
Mentoring	350	2.31	.593	112	2.43	.625	1.839 (.075)
Motivation	350	2.47	.585	112	2.57	.497	1.585 (.087)
Others’ Contributions	350	2.50	.623	112	2.43	.625	−1.013(.313)
Empowerment	350	2.54	.622	112	2.57	.497	.531 (.552)
Providing Feedback	350	2.46	.640	112	2.50	.630	.619 (.533)
Supervision	350	2.23	.724	112	3.96	.510	1.620 (.079)
Collaboration	350	2.33	.619	112	2.36	.613	−.658 (.539)
		[2.45]			[2.63]		1.262(0.11)
(D) Group Dynamics
Organizational Behavior	350	2.27	.689	112	2.29	.703	−2.603(.017)
Power Dynamics	350	2.54	.584	112	2.07	.802	−2.895(.005)
Group Development	350	2.50	.545	112	2.36	.613	−.049 (.959)
Creating Change	350	2.59	.578	112	2.50	.502	−3.831 (.000)
		[2.48]			[2.31]		1.488 (0.094)
(E) Civic Responsibility
Diversity	350	2.43	.638	112	2.36	.481	−2.183 (.024)
Others’ Circumstances	350	2.40	.596	112	2.29	.592	2.617 (012)
Inclusion	350	2.3486	.604	112	2.57	.625	−.920 (.396)
Social Justice	350	2.4629	.62	112	2.2857	.703	−.507 (.614)
Social Responsibility	350	2.6114	.683	112	2.4286	.625	−5.173 (.000)
Service	350	2.3943	.650	112	2.2143	.776	−1.571 (.005)
		[2.44]			[2.36]		1.30(0.111)
(F) Personal Communication
Verbal Communication	350	2.4686	.622	112	2.2	.591	−2.739 (.000)
Nonverbal Communication	350	2.4800	.658	112	2.28	.591	−3.692 (.824)
Listening	350	2.41	.635	112	2.21	.676	.239 (.012)
Writing	350	2.60	.596	112	2.43	.732	−2.617 (.556)
Facilitation	350	2.47	.622	112	2.43	.625	−.591 (.037)
Conflict Negotiation	350	2.62	.553	112	2.43	.625	−1.994 (.966)
Advocating for a Point of View	350	2.52	.604	112	2.50	.502	1.962 (.075)
		[2.51]			[2.37]		2.89(0.006)
(G) Strategic Planning
Mission	350	2.52	.604	112	2.64	.481	−.046 (.087)
Vision	350	2.50	.545	112	2.50	.630	1.839 (.313)
Goals	350	2.31	.593	112	2.43	.625	1.585 (.552)
Plan	350	2.47	.584	112	2.57	.497	−1.013 (.533)
Organization	350	2.49	.623	112	2.42	.620	.531 (.000)
Mission	350	2.53	.621	112	2.57	.497	.619 (.000)
		[2.47]			[2.52]		1.812 (0.322)
(H) Personal Behavior
Initiative	350	2.46	.639	112	2.50	.629	.619 (536)
Functioning Independently	350	2.21	.865	112	2.42	.496	3.08(.002)
Follow-Through	350	2.33	.650	112	2.43	.490	2.11 (.035)
Responsibility for Personal Behavior	350	2.29	.697	112	2.2	.560	.871 (.384)
Ethics	350	2.24	.765	112	2.37	.615	2.23 (.026)
Responding to Ambiguity	350	2.43	.808	112	2.36	.481	.418 (.676)
Responding to Change	350	2.49	.69	112	2.56	.498	1.688 (.092)
Resiliency	350	2.47	.732	112	2.57	.497	2.152 (.032)
Positive Attitude	350	2.52	.589	112	2.71	.457	4.19 (.000)
Confidence	350	2.68	.549	112	2.64	.481	.055 (.956)
Excellence	350	2.45	.670	112	2.56	.626	1.63 (.104)
		[2.42]			[2.48]		1.13 (0.135)
Overall Competence	350	{2.41}	.341	112	{2.50}	.629	3.65 (.000)

Notes: 1) Mean Score calculated for each item as well as for sub-constructs of competence level. Mean Score calculated with the range of 1–3 and if Mean Score is less than 2 which means students have low level of mentioned Competence; Mean Score between 2.1 to 3 indicates students have high level of mentioned Competence.

2) The values within the [] shows the overall Mean score of Sub-constructs of Competence and the value within the {} shows the overall Mean Score of Competence.

3) The value within the (.) shows the significance value of t-statistics resulted from Independent Sample t-test.

A two-way multivariate analysis of variance (MANOVA) was conducted to investigate if there is a significant difference in Employability skills and Competencies among the demographic parameters of the students as hypothesized (H₂). Two sub-null hypotheses were also evaluated: a) There is a significant difference in the Employability Skills among the demographic parameters of the students and b) There is a significant difference in the Competencies among the demographic parameters of the students. Table 13 includes the descriptive statistics for the dependent variables disaggregated by the independent variable.

Table 13. Two-way MANOVA result

Source	Dependent variable	Type III sum of squares	df	Mean square	F	Sig.
Corrected Model	TEM	30.613^a	25	1.225	4.717	.000
	TCM	13.139^b	25	.526	5.229	.000
Intercept	TEM	1690.043	1	1690.043	6509.684	.000
	TCM	543.285	1	543.285	5405.411	.000
Gender	TEM	.115	1	.115	3.444	.005
	TCM	.256	1	.256	2.544	.001
Age	TEM	3.631	3	1.210	4.662	.003
	TCM	1.662	3	.554	5.513	.001
Course of the Study	TEM	2.506	3	.835	3.218	.023
	TCM	2.992	3	.997	9.924	.000
Annual Family Income	TEM	.474	2	.237	.914	.402
	TCM	.387	2	.193	1.924	.147
Gender * Age	TEM	3.370	2	1.685	6.490	.002
	TCM	.204	2	.102	1.017	.362
Gender * Annual Family Income	TEM	.717	2	.358	1.380	.253
	TCM	1.256	2	.628	6.247	.002
Age * Course of the Study	TEM	.749	2	.374	1.442	.238
	TCM	2.945	2	1.472	14.650	.000
Age * Annual Family Income	TEM	1.864	2	.932	3.589	.028
	TCM	.572	2	.286	2.845	.059
Course of the Study * Annual Family Income	TEM	.664	1	.664	2.558	.110
	TCM	.743	1	.743	7.393	.007
Gender * Age * Annual Family Income	TEM	.444	1	.444	1.709	.192
	TCM	.000	1	.000	.002	.969
Error	TEM	113.194	436	.260
	TCM	43.821	436	.101
Total	TEM	7820.496	462
	TCM	2862.550	462
Corrected Total	TEM	143.807	461
	TCM	56.961	461
a. R Squared = .213 (Adjusted R Squared = .168)
b. R Squared = .231 (Adjusted R Squared = .187)

Note: TEM—Total Employability Score, TCM—Total Competence Score.

Using the Bonferroni method, each ANOVA was tested at 0.025 (.05/2) alpha level. Result demonstrated that there was sufficient evidence to reject the null hypothesis at 5% significance level. This showed that there is significant difference in employability skills and competencies among the parameters of demographic profile (p < 0.01) except Annual family Income. Further, the study identified the interaction effect of demographic parameters on employability skills and Competencies. Post hoc comparisons (Tukey HSD) were conducted to evaluate pairwise the differences among group means for independent variables (more than two categories) and independent sample test results for Gender (two categories). Further, results revealed a higher employability and Competence among male respondents than their female counterparts (M = 2.48 > 2.45). The post hoc result showed that students in the age group less than 20 years have more employability skills (M.D> other age groups) compared to other age groups. Likewise, students between the age group of 26–30 have greater competencies (M = 2.82). According to the survey, students with science backgrounds had better employability skills (M = 4.12) than those with backgrounds in business and management (M = 4.05) or the humanities (M = 4.25). The interaction results of gender and age showed significant difference in employability skills (F = 6.49, p < .01) and descriptive results showed male students in the age groups of between 26 and 30 years have significantly higher employability skills (M = 4.25) than other combinations of gender and age. The interaction of Gender and Annual Income showed significant difference in competencies (F = 6.247, p < 0.01) and post hoc result showed male students with Annual family income of less than 2 lakhs have higher level of competencies (M = 2.82) than other combination in the interaction of Gender and Annual family income. Further, interaction of age and course of the study showed significant difference in competencies (F = 14.650, p < .01) and comparison result showed that students from arts and humanity in the age group of 26–30 have higher level of competencies (M = 2.76) than other combination in the interaction of age and course of the study. Interaction of Course of the study and Annual family income showed significant difference is the competencies (F = 7.39, p < 0.01) and comparison result showed that students from commerce and management with family income of 2–5 lakhs have higher level of competencies (M = 2.59) than other combinations of the interaction of course of the study and Annual family income. From the above analysis, it can be concluded that there is a significant difference in the employability skills and Competence level among the demographic variables. Thus, the alternative hypothesis (H₂) is accepted at 1% significance level.

The studies of Jayasingam et al. (2018) and Teijeiro et al. (2013) showed positive relation between competence and employability skill. Pearson’s Multi-group correlation was conducted to test this hypothesis and the result showed (Table 14) no significant relationship (p > 0.05) between employability skill and competence among students of distance education. However, a “very low level” (r < 0.25 as per Kalaycı et al., 2007 criteria) of positive correlation (r = +0.212). Likewise, it showed a very low level of positive correlation between employability and competence (r = +0.241).

Table 14. Multi-group correlation coefficient

	Distance education	In-person learning	Total sample
Correlation coefficient between Employability Skill and Competencies (r)	0.505	0.212	0.241
Sig. (2-tailed)	.065	.048	.015
N	112	350	462

Therefore, from the correlation analysis, it can be inferred that there is a very low level yet significant correlation between competence and employability skills of graduates except in case of distance education students. Since p-value is less than 0.05, H₃ is accepted.

5.7. Structural model assessment

Structural model developed and tested to know the impact of method of education on employability skills and competencies. H4 was developed for this purpose, and it was tested using multiple regression analysis in Smart-PLS 4.

5.7.1. Explanatory power and model fit

According to Shmueli and Koppius (2011), R² is the indicator of explanatory power and it indicates the proportion of variance in dependent variables explained by independent variables. Table 15 indicates that method of education explains 56.6% of variance of Employability skills and 51.2% of variance in Competencies, which are substantial according to the guidelines of Cohen (1988). Similarly, F² explains the changes in R² if the predictor variable is omitted and the result showed satisfactory (<0.35) as recommended by Cohen (1988). In addition to this, the value of Q² is greater than zero. Hence, predictive relevance is established. SRMR <0.10 and NFI >0.90 considered as good model fit (Hu & Bentler, 1999). Since the present model has SRMR >0.10 and NFI >0.90, this establishes a good model fit.

Table 15. Explanatory power and model fit indices

Predictor	Outcomes	R^2	F^2	Q^2
Method of Education	Employability Skills	0.566	0.54	0.06
	Competencies	0.512	0.38	0.048
SRMR = 0.056 NFI = 0.971

H₄ states that the method of education has significant influence on Employability Skills and Competencies. The result (Table 16) that Method of education has a significant impact on Employability Skills (β = 0.446, p < 0.01) and Competencies (β = -0.527, p < 0.01). Therefore, the method of education is a categorical variable and coded (1 = In-person learning and 0 = Distance Education) for the purpose of linear regression. Here, Distance education is taken as a reference variable. If the result shows positive which means In-person learning has more impact on outcome variable than distance education and vice versa. Further, the beta coefficient of first path (Method of education Competence) showed negative which means Distance education has more positive impact on competence than In-person learning. Conversely, it showed positive beta coefficient for second path (Method of education Employability) which indicates that In-person learning has more significant impact on employability skills than distance education. Overall, the method of education has a significant impact on competence and employability skills. Therefore, H₄ was accepted at 1% significance level.

Table 16. Direct effect/total effect

	Beta coefficients	Standard deviation	T statistics	P values
Method of Education -> Competence	−0.527	0.104	5.082	.000
Method of Education -> Employability	0.446	0.126	3.545	.000

6. Discussion and concluding remark

The present study explored whether the method of education has an influence in indoctrinating competence and employability skills in students. The measurement scale developed by Seemiller (2016) was used to measure Competence of graduates, and the scale developed by Jackson & Chapman (2012) was used in measuring employability skills. However, the scales were used after incorporating some modifications to suit the Indian context after referring to the related literature (Jeswani, 2016; Kulal & Nayak, 2020; Bakar & Hanafi, 2007). The findings showed that the percentage of students opting distance education (only 24.2%) is much less than in-person learning which supported the findings of Gul and Bhat (2019) survey on Higher education. As the significance of Competence and employability skills is indisputable and is also emphasized by many (Abas & Imam, 2016; García-Álvarez et al., 2022; Jackson, 2014; Kenayathulla et al., 2019 etc.), the present study laid emphasis on measuring the level of Competence and employability skills possessed by the students of In-person learning and distance education alike. The result revealed that students of In-person learning have higher employability skills compared to students of distance education, but the Competence profile showed higher Competence level among students of distance education. These findings made us conclude that students in higher education are competent to develop employability skills but not able to achieve due to lack of face-to-face interaction, practical demonstration and classroom discussions. This revealed that Competence can be developed without the assistance of a tutor but employability skills can be developed with proper guidance of instructor.

According to Kara et al. (2019) financial problems and family dependability are the major reasons for pursuing distance education in India despite having interest in In-person learning. So, it is fair to assume that the students would prefer In-person learning mode over distance mode if education is made at less cost. This is because they could get hands-on experience, classroom interactions, industry visits and internship opportunities (Many Universities in India give Internship as optional for students of distance education). The disparity is not just based on the method of education but is scattered across the varying demographics of students (García-Álvarez et al., 2022; Tymon, 2013). The study revealed that there is significant difference in the employability skills and competence among the parameters of demographic profile as found in literature (Sinkovics et al., 2015). Technology oriented nations (like US, China and Japan) have much more comparative advantage in distance education than developing nations (like India, Africa and Pakistan). Gul and Bhat (2019), study revealed that age, field of study and gender of the study have influence on the level of competence and employability skills. Students from science backgrounds have a higher level of employability skills compared to other course students. Therefore, science students have daily practical classes in the lab, and it may be caused by the difference. In cross-tab analysis, researchers found that science students opt In-person learning rather than distance education because most of the science subjects require a well-equipped lab which is difficult to get in distance education.

The present study found a very low level of positive correlation between competence and employability skill which is consistent with the result of Blokker et al. (2019), Jayasingam et al. (2018) and Teijeiro et al. (2013). This result concludes that a high level of competence among students enables them to achieve a higher level of employability skills. Therefore, colleges must bring in such curriculum which enhances the competence level of students. Finally, the study found that this method of education has a significant impact on the level of competence and employability skills. As there were no studies to cross-refer this finding, a discussion with the subject experts approved the results. They commented that though distance education is growing expeditiously in the post-pandemic era, it can hardly match the traditional classroom experience. However, using the right Ed-tech tools and software accompanied by innovation, distance education can meet the true objectives of education. It may be concluded that though online learning can promote Competence skills in learners, a lot needs to be done to develop employability skills.

7. Practical implications

The outcomes of the present study may help the educational institutions and policymakers to bridge the gap between In-person learning mode and Distance mode of education. Thereby, the transversal competencies of students will enhance and their suitability to job market requirement can be ensured, and also it will help to make youngsters more employable and to reduce the unemployment level in an economy. This in turn indirectly helps in resolving socio-economic problems such as poverty, low family income, illiteracy and so on.

8. Limitations and future directions

The present study was conducted taking only 10 universities of Karnataka; therefore, the result of the study can be generalized to the Indian context only. Further, the response bias of students may reduce the accuracy of perceived employability skill and competences of the students. A future study can be conducted on competence and employability skills by taking employees, and a comparative study can be conducted to know the difference in the level of competence and employability skill in the pre-employment stage and post-employment stage.

Disclosure statement

No potential conflict of interest was reported by the authors.