Faculty members play a vital role in distribution of knowledge.

The faculty salaries is defined as the cash

emolument paid by the academic institutions to the faculty member against their services of teaching,
administrative activities, research etc. (The University of Arizona, 2019). The salary of the faculty is the
reflection of titles associated in the form of experience, publications, gender, type of faculty, name of the
university etc. The prior mentioned determinants can further be bifurcated into profiles named
professional characteristics and publication issues.

Numerous past researches have confound the impact of professional attributes on to the salary of the
faculty. One of the study conducted by Prakhov and Rudakov (2018) identified that the incentive
mechanism in universities in Russia. As per the research, the faculty salary showed a positive relationship
with the publication activity, specifically for research oriented universities. Moreover, administrative
duties, seniority, males also showed positive relationship with the faculty salaries i.e. high salary in case
of presence of all prior mentioned determinants.

Personally, we have also experienced a huge difference in salaries with the position occupied by faculty,
gender and number of research papers held by the teacher.

If one is planning to enter into the profession of teaching, the research conducted by Stock and Siegfried
(2001) assisted in determining that how much an Economic professor can make. For this matter, the
research stated that the School of origin or the institute determines securing a full time position and
emolument is based on the specialization or department of study. Similarly, Kirk (1996) identified that
four factors are majorly used to calculate or estimate the differences in the salary profiles of the faculty
named experience, rank/position, reward structure of the institute, PhD degree status.

Research indicates that individual attributes of faculty is the leading factor of salary determination, but
those faculty members who are salary driven must keep an eye on those low tier institutes who are not
research oriented and are planning to enter in the race of publication. Such institutes provide higher
salaries for the position linked with the individual attributes that is equal to the same as offered by the
upper tier universities (Neely et al., 2008).

Siegfried and White(1973) identified the reward structure at the University of Wisconsin where they
identified that experience, productivity of the teaching, administrative duties, publications are directly
linked with the salaries, and major variations can be seen due to the difference in the same.
Another research conducted in University of Northern Iowa, College of Business Administration
identified that other than the external factor of labor market, the internal factor of seniority, department of
faculty, position had significant impact on the salary of the faculty (Bergeth, 2007). Economic professors
have been proved to receive higher salaries than social sciences colleges leading to business school
premium, hence, it can be dictated that type of university also determines the faculty salary (Hoover and
Formby, 2000).

In order to find the certainty in above-mentioned literature and extracted assumptions, we conducted
interviews, which is a Primary data collection method, of 786 participants. The interview is a qualitative
approach where the responses received have been transformed in quantitative via coding.

The rationale behind this research design is this study will be analyzing the variation caused in Salary of
the faculty members (effect) due to variation in independent variables i.e. causes which in our case will be
ranking of university, gender of the faculty, experience, position of the faculty, number of publications,
PhD and years after PhD etc.
Publication Index

Metric for productivity and citation impact of the publication of the faculty (Gomez et al., 2016).
Similarly, Siegfried and White (1973) identified the reward structure at the University of Wisconsin
where they identified that experience, productivity of the teaching, administrative duties, publications are
directly linked with the salaries, and major variations can be seen due to the difference in the same

Independent Variable Measurement

Number of most cited papers of the faculty as well as the number of citation

PUBLICATION received by the faculty in other publications.

Total Number of pages in all of the faculty citations

Position Dichotomous – 0 for Not applicable, 1 for applicable

Gender Dichotomous – 0 for Male, 1 for Female

Dichotomous – 0 for Not applicable, 1 for applicable

PhD
Year

9 universities and their impact on the salary, does the ranking as identified
Universities
in literature review has a positive impact on the salary

Experience Total Numbers of teaching experience since 1st job

This research is conducted in accordance to all the ethical requirements of study. This research makes
sure that it does not disturb anyone’s confidentiality as well as it has not used any derogatory terms and
material

Another research conducted in Russian universities identified the determinants participating in the
fluctuation of the salaries of the faculty members. It was identified that salary has a positive relationship
with the number of publications, administrative duties or position held in the academic institution, male
faculty members, seniority (Prakhov and Rudakov, 2018).
ollowing website has ranked the universities in USA and as per the data, our research universities can be
ranked as following:

https://www.topuniversities.com/where-to-study/north-america/united-states/ranked-top-100-us-
universities

Table 1

Rank as per Website Name of University Research Ranking

#18 University of Michigan #1

#36 University of Illinois, Chicago (UIC) #2

#50 Ohio State University, Columbus #3

#53 Purdue University #4

#54 University of Minnesota #5

#55 Michigan State University #6

#58 University of Wisconsin-Madison #7

#75 Indiana University Bloomington #8

#99 University of Iowa #9

XP3 shows that the female gender and faculty salary are negatively associated and any female faculty
receives less salary than the male faculties. It is similar to the finding in which researchers found out that
male faculty members earn more than their female peers (Prakhov and Rudakov, 2018).

XP4& XI14- XI15 shows that experience has a positive impact on the salary, which was confounded
by Webster (1995), stating that experience along with publication rate and long-term affiliation
with the institution significantly impact the salary of the faculty.

XP5 andXP6represents the year of PhD and the PhD department. It shows that both have a negative impact
of 0.019 and 0.001 on the salaries of the faculty indicating that smaller the number of PhD year, higher
will be the salary wherein no relationship was identified with the department of PhD however research by
Claypool et al. (2017) shows that there is a correlation between department and salary of the faculty.

Innovation and design in organizations are the engine of success for both organization and economy of a
country. Those countries who have been relying on innovation have shown rapid growth and development
as compared to those countries who have been lethargic to adapt the changes in the system.UK is
considered the holder of world’s most innovative companies including Coca-Cola, Facebook, Amazon.
Approximately 590,000 new businesses started in UK in the year 2017 indicating the innovation and
business motivated environment (Great Britain Northern Island, 2022).
Innovation is defined as the idea, invention that is transformed into a good or service with a value that a
customer is willing to pay. Some of the examples of innovation is the introduction of touch screens,
electric cars, rockets taking to you mars, batteries with longer ranges etc. (sinnaps, 2015).
The innovation in the organizations depends on multiple determinants including size of the organization,
market structure, profit / net income of the firm, and growth where the intensity of impact depends on the
nature of the firm. As per one of the study conducted in Australian manufacturing businesses,
determinants of R&D, size, profit and market structure show high impact on the innovation in high-tech
firm wherein the same variables show less significance in low-tech firms (Bhattacharya and Bloch, 2004).
As long as the size of the firm is concerned, there is significant impact of innovation on larger firms than
firms with lower production (Hadhri, Arvanitis and M’Henni, 2016).

The innovation in logistics is the enhancement in resources and technology that can be used in managing
the movement or logistics process so to reduce the cost, human error and time associated with each of the
process of logistics. Some of the example of innovation in logistics is the automation of the inventory,
3PLs automate shipping allow the logistics to find and opt the most short and optimize shipping route
(Zamora, 2016).

Innovation in support allow the companies to get the project elevate from the ground with the help of
contacts. It is also known as innovation brokering, the enhancement allows the individuals to start with a
successful project. It includes innovative teams, brainstorming, communications, etc. (NASRALLA,
2016).
Hadhri, Arvanitis and M’Henni (2016) stated that the size of the firm has a greater impact on the
innovation for firms that are process innovative than those which are product innovative. Moreover, the
concentration of the industry which was also confirmed by Bhattacharya and Bloch (2004) where they
also added some additional determinants along with the impact of size of firm(in terms of employees) on
the innovation.
Cooperation arrangement, lastly determine the impact of the innovation linkages of the company on the
innovation outputs and inputs. The results shows that cooperation in the non-R&D activities positively
influence the introduction of new product. The developing countries company rely on coexistence with
companies who invest heavily on the innovation. The cooperation can either be in the form of learning the
innovation tactics and passing on to the employees via training, or using the capabilities of the partner
regarding the innovation and measuring the impact on the products and services developed by it
(Fernández Sastre and Vaca Vera, 2017).

The above-mentioned figure depicts that within a manufacturing organization, there are primary and
secondary value chain. Within this, innovation is required in each of the value to minimize the cost and to
gain the competitive advantage over the others (Zamora, 2016). It takes into account the innovation in
logistics, services, manufacturing and other support functions. The innovation in product / manufacturing
can be in the form of product development or any other way on Ansoff’s Matrix etc. Innovation in matrix
can be in any form; it can either be in the form of new technology, change in supply chain, and
enhancement in process. It can be the use of analytics, robotics, 3D printing, cloud computing, smart
sensors etc. (Wilson, 2021).
The R square value, also known as the coefficient of determination can also be interpreted as the total
variation in the dependent variable caused by independent variables. It can also be seen in the last
column, Durbin Watson has a value of 0.887. As stated by Kenton (2021), the DW test helps to analyze
the autocorrelation in the residual of the regression model and has a range from 0 to 4 wherein value
below 2 shows positive correlation, 2 shows no and above 2 shows negative correlation. In our case, we
can suggest that our residual has a positive autocorrelation.