Internship in the year of COVID-19: what has changed in internship dynamics?

Автор: Patil A., Sharma P.

Рубрика: Report

Статья в выпуске: 22, 2022 года.

Бесплатный доступ

The purpose of this research is to understand the changes in internship dynamics of MBA students in 2020 who undertook virtual full-time internships as compared to previous years when interns were required to be present physically. Sample was collected from two set of interns; one set interns were working through virtual internship and the second set were working on traditional model of on- site internship. For data collection, structured questionnaire was used. Data was collected on various parameters of internship processes and experience. The result from the current findings suggests that the experience of both set of interns differ significantly. This paper will essentially evaluate whether virtual full-time internships have been able to contribute, create & shape internship experience effectively for interns & understand which dimensions such as learning, productivity, communication, etc have seen changes due to the nature of work being changed. Research material in this area is limited, mainly due to students experiencing a remote working style in their internships for the first time which is why this research will be quite valuable for various stakeholders.

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Internship, mba, dimensions, experience, stakeholders

Короткий адрес: https://sciup.org/148324606

IDR: 148324606 | DOI: 10.18137/cardiometry.2022.22.277289

Текст научной статьи Internship in the year of COVID-19: what has changed in internship dynamics?

Advait Patil, Pooja Sharma. Internship in the year of Covid-19: what has changed in internship dynamics? Cardiometry; Issue 22; May 2022; p. 277-289; DOI: 10.18137/cardiome-try.2022.22.277289; Available from: http://www.cardiometry. net/issues/no22-may-2022/internship_year_Covid-19

The foremost elements of this study are the MBA students who have to compulsorily undertake internships as a part of the MBA program. A survey was conducted where the sample were the students from the MBA batches of 2021, 2020, 2019 and 2018 and the changes that have happened for the Batch of 2021 during the course of their internship in terms of various internship dynamics were analysed [4].

2 Literature review

There is a need to evaluate the gap between the perceived expectations from the internship as compared to its actual experience by using the expectation confirmation theory. (Neelam et al., 2019) The aim was to evaluate the perceived internship experience through the expectation confirmation theory. The study revealed that there is positive expectation disconfirmation with there being a significant impact of supervisor-intern exchange on perceived internship value. The utility of existing technologies to enable students (interns) to gain international work experience through virtual internships can be analysed [10].

3 Materials and methods 3.1 Objective

The goal was to understand the internship dynamics for MBA students especially with respect to interactions with the Organisation, with peers, other employees and also the manager-mentee dynamics. The goal was to understand how these have changed in 2020 due to the virtual/ remote mode of working being implemented for the first time as a result of the COVID-19 pandemic spreading across the whole world as compared to the internship dynamics of the previous few batches of MBA students [11].

3.2 Sampling

3.3 Survey

A survey of the 271 MBA students was taken to understand their insights on a variety internship dy- namic ranging from their interactions with their managers, Organisation representatives to the learning curve during the course of their internships with the Organisation. Out of the total 271 MBA students surveyed, 4 were from the Batch of 2018, 33 were from the Batch of 2019, 98 were from the Batch of 2020 and 136 were from the Batch of 2021. The 135 students from the Batches of 2018, 2019 and 2020 were grouped under the Pre-COVID Scenario category and the 136 students from the Batch of 2021 were grouped under the Post-COVID Scenario.

4 Analyses

In this study the LMX theory has been used to understand the changes in the dynamics between the intern and the manager in terms of professional abilities, domain knowledge and mutual respect and how these dimensions have changed due to the workplace becoming a virtual construct. The Cronbach’s Alpha for the set of data which has 16 items is 0.857 which means that the data set is reliable.

The number of reporting managers can also add on to the difficulty of the project at hand for the intern as it can complicate their work especially while working remotely and may even cause some delays in the output. The number of interns who had 2 manag- ers has decreased from 50 to 30 by almost 45% in the post-COVID scenario as compared to the pre-COVID scenario. However there has been an increase of 60% from 6 to 10 in terms of the number of interns who had to report to more than 2 managers.

Table 1

Average interaction time with buddy

Timeline	Average Interaction Time with Buddy
Timeline	0 (No Interaction)	Upto 10 Minutes	10-15 Minutes	15-30 Minutes	More than 30 Minutes	Grand Total
Post-COVID	10	30	34	34	28	136
Pre-COVID	18	27	50	34	6	135
Grand Total	28	57	84	68	34	271

Table 2

Average interaction time with manager

Timeline	Average Interaction Time with Manager
Timeline	0-10 Minutes	10-15 Minutes	15-30 Minutes	More than 30 Minutes	Grand Total
Post-COVID	18	24	60	34	136
Pre-COVID	27	44	45	19	135
Grand Total	45	68	105	53	271

Table 3

Post hoc analysis (LMX Theory)

Multiple Comparisons
Bonferroni
Dependent Variable	(I) Batch (MBA)	(J) Batch (MBA)	Mean Difference (I-J)	Sig.
[I like my mentor very much as a person]	2018	2019	-0.167	1
		2020	0.143	1
		2021	-0.559	1

Multiple Comparisons
Bonferroni
Dependent Variable	(I) Batch (MBA)	(J) Batch (MBA)	Mean Difference (I-J)	Sig.
	2019	2018	0.167	1
		2020	0.31	0.937
		2021	-0.392	0.377
	2020	2018	-0.143	1
		2019	-0.31	0.937
		2021	-.702*	0
	2021	2018	0.559	1
		2019	0.392	0.377
		2020	.702*	0
[My mentor is the kind of person one would like to have as a friend.]	2018	2019	-0.106	1
		2020	0.48	1
		2021	-0.338	1
	2019	2018	0.106	1
		2020	.586*	0.05
		2021	-0.232	1
	2020	2018	-0.48	1
		2019	-.586*	0.05
		2021	-.818*	0
	2021	2018	0.338	1
		2019	0.232	1
		2020	.818*	0
[My mentor is a lot of fun to work with]	2018	2019	-0.061	1
		2020	-0.153	1
		2021	-0.559	1
	2019	2018	0.061	1
		2020	-0.092	1
		2021	-0.498	0.137
	2020	2018	0.153	1
		2019	0.092	1
		2021	-.406*	0.041
	2021	2018	0.559	1
		2019	0.498	0.137
		2020	.406*	0.041
[My mentor defends my work actions to a superior, even without complete knowledge of the issue in question]	2018	2019	-0.121	1
		2020	-0.286	1
		2021	-0.221	1
	2019	2018	0.121	1
		2020	-0.165	1
		2021	-0.099	1
	2020	2018	0.286	1
		2019	0.165	1
		2021	0.065	1
	2021	2018	0.221	1
		2019	0.099	1
		2020	-0.065	1

Multiple Comparisons
Bonferroni
Dependent Variable	(I) Batch (MBA)	(J) Batch (MBA)	Mean Difference (I-J)	Sig.
[My mentor would come to my defence if I were “attacked” by others]	2018	2019	-0.424	1
		2020	-0.388	1
		2021	-0.618	1
	2019	2018	0.424	1
		2020	0.036	1
		2021	-0.193	1
	2020	2018	0.388	1
		2019	-0.036	1
		2021	-0.23	0.601
	2021	2018	0.618	1
		2019	0.193	1
		2020	0.23	0.601
[My mentor would defend me to others in the organisation if I made an honest mistake]	2018	2019	-0.045	1
		2020	-0.041	1
		2021	-0.147	1
	2019	2018	0.045	1
		2020	0.005	1
		2021	-0.102	1
	2020	2018	0.041	1
		2019	-0.005	1
		2021	-0.106	1
	2021	2018	0.147	1
		2019	0.102	1
		2020	0.106	1
[I do work for my mentor that goes beyond what is specified in my work description]	2018	2019	0.212	1
		2020	0.633	1
		2021	0.221	1
	2019	2018	-0.212	1
		2020	0.421	0.227
		2021	0.008	1
	2020	2018	-0.633	1
		2019	-0.421	0.227
		2021	-.412*	0.013
	2021	2018	-0.221	1
		2019	-0.008	1
		2020	.412*	0.013
[I am willing to apply extra efforts, beyond those normally required, to further the interests of my work group]	2018	2019	1	0.269
		2020	1.286*	0.046
		2021	0.838	0.474
	2019	2018	-1	0.269
		2020	0.286	0.786
		2021	-0.162	1
	2020	2018	-1.286*	0.046
		2019	-0.286	0.786
		2021	-.447*	0.002

Multiple Comparisons
Bonferroni
Dependent Variable	(I) Batch (MBA)	(J) Batch (MBA)	Mean Difference (I-J)	Sig.
	2021	2018	-0.838	0.474
		2019	0.162	1
		2020	.447*	0.002
[I do not mind working hardest for my mentor]	2018	2019	0.833	0.499
		2020	0.857	0.387
		2021	0.485	1
	2019	2018	-0.833	0.499
		2020	0.024	1
		2021	-0.348	0.292
	2020	2018	-0.857	0.387
		2019	-0.024	1
		2021	-.372*	0.013
	2021	2018	-0.485	1
		2019	0.348	0.292
		2020	.372*	0.013
[I am impressed with my mentor’s knowledge of his/ her job]	2018	2019	0.955	0.598
		2020	0.735	0.557
		2021	0.426	1
	2019	2018	-0.955	0.598
		2020	-0.22	1
		2021	-0.528	0.08
	2020	2018	-0.735	1
		2019	0.22	1
		2021	-0.308	0.204
	2021	2018	-0.426	1
		2019	0.528	0.08
		2020	0.308	0.204
[I respect my mentor’s knowledge of and competence on the job]	2018	2019	0.682	0.86
		2020	0.408	1
		2021	0.279	1
	2019	2018	-0.682	0.86
		2020	-0.274	0.734
		2021	-0.402	0.113
	2020	2018	-0.408	1
		2019	0.274	0.734
		2021	-0.129	1
	2021	2018	-0.279	1
		2019	0.402	0.113
		2020	0.129	1
[I admire my mentor’s professional skills]	2018	2019	0.833	0.676
		2020	1.031	0.252
		2021	0.338	1
	2019	2018	-0.833	0.676
		2020	0.197	1
		2021	-0.495	0.063

Multiple Comparisons

Bonferroni

Dependent Variable	(I) Batch (MBA)	(J) Batch (MBA)	Mean Difference (I-J)	Sig.
	2020	2018	-1.031	0.252
		2019	-0.197	1
		2021	-.692*	0
	2021	2018	-0.338	1
		2019	0.495	0.063
		2020	.692*	0

Table 4

Post hoc analysis (post-internship impact on skills)

Multiple Comparisons
Bonferroni
Dependent Variable	(I) Batch (MBA)	(J) Batch (MBA)	Mean Difference (I-J)	Std. Error	Sig.
[Leadership]	2018	2019	0.5	0.483	1
		2020	0.765	0.465	0.606
		2021	0.471	0.463	1
	2019	2018	-0.5	0.483	1
		2020	0.265	0.184	0.897
		2021	-0.029	0.177	1
	2020	2018	-0.765	0.465	0.606
		2019	-0.265	0.184	0.897
		2021	-0.295	0.121	0.092
	2021	2018	-0.471	0.463	1
		2019	0.029	0.177	1
		2020	0.295	0.121	0.092
[Ability to Work in a Team]	2018	2019	0.106	0.479	1
		2020	0.561	0.461	1
		2021	0.029	0.459	1

Multiple Comparisons
Bonferroni
Dependent Variable	(I) Batch (MBA)	(J) Batch (MBA)	Mean Difference (I-J)	Std. Error	Sig.
	2019	2018	-0.106	0.479	1
		2020	0.455	0.182	0.078
		2021	-0.077	0.176	1
	2020	2018	-0.561	0.461	1
		2019	-0.455	0.182	0.078
		2021	-.532*	0.12	0
	2021	2018	-0.029	0.459	1
		2019	0.077	0.176	1
		2020	.532*	0.12	0
[Writing Effectively]	2018	2019	0.318	0.513	1
		2020	0.214	0.494	1
		2021	0.235	0.491	1
	2019	2018	-0.318	0.513	1
		2020	-0.104	0.195	1
		2021	-0.083	0.188	1
	2020	2018	-0.214	0.494	1
		2019	0.104	0.195	1
		2021	0.021	0.128	1
	2021	2018	-0.235	0.491	1
		2019	0.083	0.188	1
		2020	-0.021	0.128	1
[Speaking Effectively]	2018	2019	0.47	0.458	1
		2020	0.286	0.441	1
		2021	0.074	0.439	1
	2019	2018	-0.47	0.458	1
		2020	-0.184	0.174	1
		2021	-0.396	0.168	0.114
	2020	2018	-0.286	0.441	1
		2019	0.184	0.174	1
		2021	-0.212	0.115	0.391
	2021	2018	-0.074	0.439	1
		2019	0.396	0.168	0.114
		2020	0.212	0.115	0.391
[Problem solving]	2018	2019	-0.091	0.425	1
		2020	-0.357	0.41	1
		2021	-0.191	0.407	1
	2019	2018	0.091	0.425	1
		2020	-0.266	0.162	0.605
		2021	-0.1	0.156	1
	2020	2018	0.357	0.41	1
		2019	0.266	0.162	0.605
		2021	0.166	0.106	0.72
	2021	2018	0.191	0.407	1
		2019	0.1	0.156	1
		2020	-0.166	0.106	0.72

The impact of the internship experience on the learning outcomes of students after completing their

internships has also been compared is shown in Table 4. The factors in Table 3 are derived from the factors of the LMX theory and interns were asked to rate them from 1 to 5 based on the internship experience and perception, with 1 being lowest and 5 being the highest. We can clearly derive from the tables that variation for most of the factors is between the 2021 and the rest of the Pre-COVID Batches. (2018, 2019 and 2020). The data collected from the selected samples was measured systematically and segmented. Analysis was done using various standard tools to arrive at the conclusion [16].

Multiple Comparisons
Bonferroni
Dependent Variable	(I) Batch (MBA)	(J) Batch (MBA)	Mean Difference (I-J)	Std. Error	Sig.
[Strong work ethic]	2018	2019	0.045	0.474	1
		2020	0.49	0.457	1
		2021	0.176	0.454	1
	2019	2018	-0.045	0.474	1
		2020	0.444	0.18	0.086
		2021	0.131	0.174	1
	2020	2018	-0.49	0.457	1
		2019	-0.444	0.18	0.086
		2021	-0.313	0.119	0.052
	2021	2018	-0.176	0.454	1
		2019	-0.131	0.174	1
		2020	0.313	0.119	0.052

5 Findings

However, the number of interns who have been conveyed a rough scope of their internship topic has almost doubled. The number of interns who had a buddy/ mentor allocated to them for the duration of the internship has also seen a rise by slightly less than 10%. In terms of average interaction times with buddy, the number of longer interactions seems to be on the rise (by almost 47%) and number of shorter interactions has declined (by almost 25%). In terms of av- 286 | Cardiometry | Issue 22. May 2022

erage interaction times with manager, the number of longer interactions seems to be on the rise (by almost 47%) and number of shorter interactions has declined (by almost 25%) [18].

We have also looked at the mentor-mentee dynamics using the leader member exchange theory. After using Post-Hoc Analysis for the data collected for various internship factors on the basis of the LMX Theory, we can infer various aspects of changes in the internship dynamics. Most of the differences found are between the perception and experiences of the Post-COVID batch (2021) and the Pre-COVID batches (2020, 2019 and 2018). In terms of the Affect, Loyalty, Contribution and Professional Respect factors, there are major differences between the 2020 and 2021 Batches which can be seen from the low significance in the Post-Hoc Analysis [19].

6 Discussions

We need to look at changes in both the positive factors (metrics which should be high) and the negative factors (metrics that should be low), analyse them and understand how we can make the most of the changes that are occurring [25].

7 Conclusions

However, the time spent on interactions with the buddy/mentor as well as the manager have increased. Also, there has been a rise in the number of reporting managers per intern as well which has in a way resulted in an increased complexity for tasks to be performed during the course of the internship. During initial ED management, respiratory viral load assessment on the first nasopharyngeal swab (by RTPCR) is neither a predictor of magnitude nor a predictor of mortality in SARSCoV2 infection. The host’s reaction to the virus, as well as the severity of pre-existing comorbidities, may be more predictive of disease severity than the virus itself.

There has also been an increase in the compared dynamics between the mentor (manager) and the mentee (intern) in terms of likeability, loyalty, contribution and professional respect as seen using the LMX theory.

8 Managerial Implications

This study brings into view the dynamics of an internship and the changes over the years. Especially now that working remotely is being considered as a permanent model of work, these dynamics need to be looked at closely to ensure that efficiency of the work being performed by interns while working remotely does not decline at all. From the organization’s perspective the intern should be enabled to contribute effectively to the organization through their project, while at the same time the intern should be able to go through a learning experience.

The internship programs have to change and evolve according to the changing dynamics in order to still be effective. From the perspective of educational institutions, they have to assist students to gain skills to work effectively remotely to match the changing job roles. This will contribute in a huge way to increase the employability of these students and make it easier to managers to work remotely with interns who in some cases may be individuals with no prior work experience.

From a managerial perspective not only do we need to consider the logistical and performance issues bit also how other functions/ activities like employee engagement are going to evolve in the future.

9 Limitations

Another major issue were lack of previous research studies on this specific topic. Most of the studies that have been conducted on this topic were done before the coronavirus pandemic. Due to this there is actually a good understanding on the different aspects or dimensions of metrics during the internships both in terms of the performance of interns as well as their experience.

Acknowledgement

The authors wish to acknowledge Symbiosis Centre for Management and Human Resource Development for providing constant support.

Conflict of Interest

There is no conflict of interest among the authors.

Funding

Self-funded

Ethical approval

SI Report Attached

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