Organizational Behaviors (OB) have been an area of great interest for the researchers for a long time (Andre, 2008). Many positive and negative organizational behaviors like organizational citizenship behaviors (OCB), destructive deviant behaviors (DDB), constructive deviant behaviors (CDB) and workaholism (WA) (Galperin & Burke, 2006a) have been explored to have positive and negative effects on the performance of organizations (Podsakoff, MacKenzie, Paine, & Bachrach, 2000) as a whole or individuals’ performance, working in those organizations (Berry et al., 2007). Most of the research on OCB, DDB, CDB, and WA deals with either finding or measuring their antecedents or their outcomes and affects. Researchers in the field of organizational behaviors (OBs) have recommended research on the inter-relationship of these behaviors, as this area has not been well researched (Dalal, Lam, Weiss, Welch, & Hulin, 2009) and (ur Rehman et al., n.d.). The present study is focused on the relationships between OCB-DDB (Dunlop & Lee, 2004), OCB-CDB (Dalal, 2005), OCB-WA, WA-DDB, WA-CDB (Galperin & Burke, 2006a) as well as their effects on the productivity. The wholesome framework of studying the relationship of all these behaviors, which exist independently and distinctly among people, was missing. These behaviors in universities’ context were previously focused on the faculty members and administrative staff. Students were treated either as the customers or the product of engineering universities. Recently few researchers studied OCB among students (Yam et al., 2014), however, mostly, such studies do not encompass capturing these OBs amongst students in real-life context; rather, students’ behaviors are studied in controlled experimental environments (Khalid et al., 2010). A review of the literature also reveals that the majority of the studies on organizational behaviors are based on quantitative data, and qualitative studies are few. “The advantages of qualitative methods include the use of the focal unit’s terms to describe itself, the intensive and in-depth information that can be obtained about a unit, and the amenability of the method for exploratory research on issues and processes about which little information exists” (Cooke & Rousseau, 1988). This paper is an attempt to explore relationships of various OBs and productivity through qualitative research.

We contest that the engineering students, especially the undergraduate (UG) students, spend a long duration of 4 to 5 years in engineering universities, they act as co-workers and exhibit organizational behaviors (LeBlanc, 2014) like other members of the universities (faculty and administrative staff), and researchers now find it an interesting area of research to explore (Allison et al., 2001); whether these behaviors are exhibited by the engineering students similar to university employees. The research to explore the relationships of these positive and negative behaviors among engineering students, along with their effects on students’ productivity will help increase the students’ productivity.

To fulfill the purpose of this research, we have used the codebook thematic analysis (Braun et al., 2019). In our research, an inductive-deductive qualitative approach was used to develop a new theory as well as to explore relationships of the behaviors among students and perceived effects on their productivity.

The Presenting Problem: Relationship of OCB, DDB, CDB, Workaholism, and Productivity among Engineering Students.

Students of engineering universities in developing countries like Pakistan can play a pivotal role in the development of their nations (Jowi et al., 2013) and (Habib et al., 2018). While the “antecedents”, “outcomes” and “relationships” of various positive and negative organizational behaviors and their effects on the individual as well as organizational productivity are an area of great interest for researchers for past many decades (Dunlop & Lee, 2004), the study of the prevalence of OCB, DDB, CDB and WA among engineering students and relationships of these behaviors and their effects on students’ productivity, especially in developing countries like Pakistan, has not been in focus. Research on these behaviors and their effects on productivity can enhance understanding of planners, practitioners, and researchers on “how these behaviors can help to increase engineering students’ productivity?” which will further lead to the rapid industrial growth of developing nations.

Relationships of Organizational Citizenship Behaviors (OCB), Deviant Behaviors (DDB and CDB), Workaholism (WA), and Productivity.

Organizational citizenship behavior (OCB) refers to employee’s extra-role behavior, that promotes organizational effectiveness, and that is not explicitly recognized by an organization’s reward system (Organ, 1990). Workplace deviance and misbehavior has also become an important concern for organizations (Bennett & Robinson, 2003). More than four decades of research on OCB has mostly considered it as a positive behavior which adds to the well-being of the organization (Skarlicki & Latham, 1995), but from last two decades, researchers have focused on finding some dark sides of this behavior (Koopman et al., 2016) due to various phenomena such as “too much of a good thing”, “moral licensing” (Klotz & Bolino, 2013; Bolino & Klotz, 2015), “maintaining moral equilibrium”, “compulsory behaviors”, “impression management”, “work-family conflicts and workaholic behaviors”, thus having some negative implications on individuals and organizations. The relationship of OCB with performance also has two views; positive effects of OCB on performance (Ozer, 2011); and negative effects(28)(28)(28).

Galperin and Burke (2006a) defined deviance as “behaviors that cause harm to the organization”. Whereas many other types of research define deviance as behaviors of violating norms (Erkutlu & Chafra, 2018; Rock, 2014) which leads to both positive and negative directions” (Cameron, 2003; Galperin, 2012). Galperin & Burke (2006b) found through exploratory research that employees’ deviance could be functional and constructive as well. They also found out that WA is significantly related to both CDB as well as DDB. Since most of the writings are anecdotal, researchers have called for more scienti?c research attention on workaholism (McMillan et al., 2002). This encouraged us to assume that engineering students’ productivity can also be linked with their positive behaviors. Researchers have found the relationship of problems like stress, depression, and sleep disorders with students’ productivity (Hysenbegasi et al., 2005; Gaultney, 2010), but the prevalence of organizational behaviors among engineering students and the relationship of these behaviors with their productivity needs to be further explored.

Overall Principles of Design

We developed the following research questions from the theoretical framework: (1) in the perception of the faculty members (supervisors), how do engineering students exhibit positive and negative organizational behaviors in engineering universities in a developing country? (2) What is the relationship between OCB, DDB, CDB, and workaholism (WA) among engineering students? (3) How do faculty members (supervisors) perceive the role of students’ gender in demonstrating positive and negative organizational behaviors (OCB, CDB, DDB, WA) amongst engineering students? (4) In the perception of engineering faculty, what is the relationship between these positive and negative behaviors and workaholism with the productivity of engineering students?

A rigorous method of sample selection, followed by the standardized open-ended interviews, transcription, coding, and codebook thematic analysis was used to formulate propositions and refine hypotheses for further quantitative studies (not reported in this article) for doctoral research work.

Selection of Codebook Thematic Analysis (TA) Qualitative Method

(Braun et al., 2019) explain codebook thematic analysis(TA) as a school of TA between “coding reliability TA” and “reflexive TA”, sharing the structured approach of coding from “coding reliability TA” (often without the use of coding reliability measures like Kohen’s Kappa) with the broadly qualitative underlying philosophy of “reflexive TA”. An inductive approach was required to get an in-depth understanding of relationships of behaviors of engineering students, and then developing propositions so that hypotheses may be tested. We, thus, decided to use an eight (8) stage codebook TA approach. We were encouraged to use this approach by researchers like Ferlie, Fitzgerald, Wood, & Hawkins (2005) and Langley (1999), who in interpretive qualitative studies, where the partial theory was already available and hypothesis-testing was to be combined with the inductive exploratory research, to find new insights or to develop new theories, used looser designs by balancing pure induction against early structure to avoid the peril of “drowning in data”. The innovative eight-stage process was: -

· Stage-1 Developing the codebook

· Stage-2 Testing the reliability of the codebook

· Stage-3 Transcription and initial coding from interviews’ data

· Stage-4 Validating initial coding through follow-up questions/interviews

· Stage-5 Validating codes/themes by different perspectives to reduce researcher’s bias

· Stage-6 Applying codebook to map/identify themes in data

· Stage-7 Corroborating and legitimating coded themes to identify second-order themes

· Stage-8 Producing report

Sampling for Interviews

We used a qualitative approach; to get rich data of perceptions of engineering faculty about the relationship of various positive and negative behaviors demonstrated by their students, qualitative standardized open-ended interviews (Turner III, 2010) of 22 faculty members were conducted by the first author. The sample size is very important to ensure the richness of data and to get an in-depth understanding of phenomena under study. Morrow (2005) suggests a magic number of 12 and also recommends a number between “20-30” for qualitative studies. She considers that a sampling procedure and a variety of evidence are more important for maintaining the quality of data. As the understanding of behaviors of students was under consideration in this research, hence 22 faculty members with teaching experience of five years or above were selected purposively; their experiences ranged from 5 to 25 years in teaching the engineering students. Similarly, variety in their disciplines was also considered; several faculty members from various engineering disciplines was: Electrical Engineering (3), Mechanical Engineering (3), Mechatronics Engineering (3), Computer Sciences (3), Software Engineering (3), Telecommunication Engineering (4) and Engineering Management (3). These 22 faculty members were from 3 different universities. All participants agreed for interviews willingly. 12 of the faculty members were male and 10 were female. The age bracket was 33 years to 57 years.

Stages Demonstrating the Research Process of Codebook Thematic Analysis (TA)

Stage-1 Developing the Codebook

The apriori codebook was prepared to facilitate collating segments of related text to find themes, and providing a trail of evidence for the credibility of the study. Codes were developed from the literature review; essential factors of the constructs under study and their relationships found by eminent scholars in the field were included in the codebook, so that, during interviews and interpretation, relevant data is mapped and collated to find themes in data. The codebook is given at Table-1: -

Table 1. A sample from Codebook Developed from the Theoretical Framework on Relationships of OCB, DDB, CDB, WA and Productivity of Engineering Students

Stage-2 Testing the Reliability of the Codes

The codebook was thoroughly checked and discussed with the co-author and other team members. To further validate, a senior expert (a professor of organizational behaviors) from another university was requested to further review the codebook. A detailed discussion resulted in adding and deleting many codes. The expert suggested deleting OCB from Warren’s list of CDB (Code 1.3) due to two reasons. First, it is already studied as a separate construct in this study; and second, basing on Galperin’s (2012) argument that OCB is passive behaviors, whereas CDB is demonstrated by pro-active individuals and risk-takers. In code 1.2 (DDB), dishonesty was replaced by “academic dishonesty” as this study’s focused population was students. In Code 1.4 (WA), the constructs “Work Engagement”, “Obsessive Passion (OP)” and “Harmonious Passion (HP)” (Birkeland & Buch, 2015), were dropped as these were considered making study too complicated, hence, were recommended for future research. Besides, a 9^th code i.e. “Relationship of Gender with OB (OCB, DDB, CDB, WA) and productivity” was added and was also included in the theoretical framework section.

Stage-3 Transcription and Initial Coding from Interviews’ Data

Transcripts were prepared very carefully and initial coding was carried out by the researchers. English is the official language being used at all universities; hence, translation was not required, as all interviews were conducted in the English language. As a measure to maintain rigor, the write-up must provide sufficient evidence of themes within the data- i.e. enough data extracts to demonstrate the prevalence of the themes (Braun & Clarke, 2006). Due to space and word limits for this article, the extracts with varied perceptions or opinions are presented in Table-2. 22 Interviewees have been labeled from “A” to “V” and interviewee’s label has been indicated in parenthesis (), along with “status” [Prof for the professor, AsP for associate professor, AP for assistant professor, and Lec for lecturer], and teaching experience in years. Example: (AP-C, 15) means assistant professor C with 15 years of teaching experience.

Stage-4 Validating initial coding through follow-up questions/interviews

Participants’ checks and follow-up interviews are the recommended process for ensuring that we capture the true perceptions of the interviewees. Examples from a follow-up interview from an assistant professor (AP-C, 15) are presented in Table-3. This follow-up interview not only confirmed the initial codes/theme of “OCB is positively related to the productivity of engineering students” but also helped in finding some new codes/themes which are underlined: -

Table 3. Validating codes Through Follow-Up Questions

Stage-5 Validating Codes/Themes by Different Perspectives to reduce Researcher’s Bias

The initial data coding and thematic analysis were carried out by one person, hence compromising the principles of rigor and quality. The co-author, 3 doctoral students (working on diverse research areas) and 1 Ph.D. qualified faculty members were requested to provide multiple perspectives. The codes and themes were then discussed as a team and were finalized after detailed discussion.

Stage-6 Applying Codebook to Map/Identify themes in Data

All the codes and templates were then mapped with the a-priori codebook to draw propositions and find relationships of various behaviors with the productivity of students. Examples are at Table – 4:-

Table 4. Mapping of codes Derived from data with A-Priori Codebook

Stage-7 Corroborating and Legitimating coded Themes to Identify second-order Themes

An iterative corroboration process was used to ensure that no unconscious “seeing” of data by researchers occur. To do this, a to and fro analysis of initial codes/themes, transcripts, and the codebook was carried out so that overarching themes are clustered to reach second level themes. The iterative process is essential to capture the perceived relationship of behaviors and productivity, to form a comprehensive framework of relations between studied behaviors, and to phrase the propositions for further quantitative studies. Examples are given at Table-5 and a summary of propositions is narrated in the discussion section:-

Stage-8 Producing Report

In writing the report, a continuous to and fro interpretive and reflexive approach was followed as the

overarching principle of quality and rigor (Braun & Clarke, 2006; Tobin & Begley, 2004).

Discussion and Development of Propositions

Relationship of OCB, CDB, DDB, WA, and Productivity of Engineering Students

There is a consensus amongst the faculty members, as interpreted from the transcripts and follow-ups (examples statements at Tables 2, 3, 4, and 5), that engineering students do demonstrate OCB, CDB, DDB, and WA in universities. And these behaviors are related to students’ productivity as well. This finding is in line with the previous research in the area of organizational behaviors (OB) (Allison et al., 2001; Khalid et al., 2010; Skarlicki & Latham, 1995), where researchers have found a positive relationship of OCB and WA with individuals’ performance, and negative relationship between DDB and performance (Steffgen, 2009).

The propositions’ developed are: “OCB among engineering students is positively related to CDB, WA, and Productivity; and negatively related to DDB”. Our work here contests the findings of (Lanzo et al., 2016) to some extent. “There is no significant relation between DDB and CDB; and between DDB and WA; however, there is a negative relation between DDB and productivity”. “There is no relationship between CDB and WA, whereas, CDB has a mild positive relationship with the productivity of engineering students”. “WA has a positive relation with productivity (CGPA), but this relation is moderated by OCB and gender of students”. Here our work contests the findings of Peiperl & Jones (2001). “Gender has been found to have a relationship with various behaviors as Female students demonstrate less OCB, less CDB and less DDB, however, they exhibit more WA; the WA in female students has a positive relation with productivity (CGPA), but this relationship is moderated by their OCB”. These propositions on gender’s role in exhibiting OBs partially contrasts the previous work of Ng, Lam, & Feldman (2016), in the context of various cultures, however, it is in line with their research in the context of developing countries like Pakistan (Nawaz et al., n.d.).

The rigorous iterative analysis helped us find not only the themes at the semantic level which helped us to find the relationship of various behaviors and productivity of engineering students but also helped us to find innovative themes at latent levels; the need for an instrument to measure the construct “studyaholism”; which opens new avenues for researchers to find its dimensions and to design separate instrument for measuring it among engineering students. Second, the measure of the productivity of students, as perceived by some faculty members, differs from the existing concept of CGPA only. And there is a need to device a reliable and valid instrument for “students’ productivity measurement”.

An interesting finding is OCB among students leading to leader-member exchange (LMX) phenomenon, between teachers and students; which ultimately leads to deviant behaviors; “nepotism, favoritism, and distributive justice” amongst teachers and “perceived procedural and organizational justice” among fellow students (colleagues), in developing countries’ cultural context. This is in line with previous research of Pillai, Scandura, & Williams (1999) and Farrell & Finkelstein (2011). It is worth noting that only 3 out of 22 respondents expressed this teacher-student LMX relation, however, its negative effects were glaring and hence noted as an important theme, as suggested by Braun & Clarke (2006) to capture themes basing on importance rather than on frequency in data.

This study was carried out in a time-constrained environment. The initial data coding and thematic analysis were carried out by one person, hence compromising the principles of rigor and quality. The co-author thus involved 3 doctoral students (working on diverse research areas) and 1 Ph.D. qualified faculty member to provide multiple perspectives. Time availability with the participants was another constraint due to which representation or checking back with participants, as suggested by many qualitative research experts (Morrow, 2005), was possible for only 15 participants out of 22 interviewees.

The students of social sciences, sometimes, consider qualitative methods more difficult and time-consuming in research, and under the pressure of submitting dissertations in time-constrained environments tend to incline more towards quantitative methods. This tendency affects the creation of new knowledge. The issues of rigor and quality in qualitative studies also usually haunt the researchers. Our study is an effort to present a systematic approach to codebook thematic analysis. It is concluded from the study that in-depth analysis of OBs can help universities and teachers to enhance the productivity of students. The paper can help a holistic understanding of the organizational behaviors of engineering students in developing countries, to bring improvements in the overall development of the nations. Our findings have provided first-hand knowledge, of effects of behaviors on the productivity of engineering students to the planners, practitioners, and faculty members at engineering universities; and have also provided a base to scholars for exploring this neglected area of research.