Perceived organizational performance and trust in project manager and top management in project-based organizations: Comparative analysis using statistical and grey systems methods

Saad Ahmed Javed (College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China) (Department of Management Sciences, COMSATS Institute of Information Technology, Islamabad, Pakistan)
Ali Murad Syed (College of Business Administration, Imam Abdulrahman Bin Faisal University, Dammam, Kingdom of Saudi Arabia)
Sara Javed (Institute for Grey Systems and Decision Sciences, GreySys Foundation, Lahore, Pakistan)

Grey Systems: Theory and Application

ISSN: 2043-9377

Publication date: 2 July 2018

Abstract

Purpose

The purpose of this paper is to empirically analyze the effect of the relationship between trust in top management (TTM) and trust in immediate supervisor (TIS), who was organizational project manager in our case, on perceived organizational performance in Pakistani public and private project-based organizations (PBOs).

Design/methodology/approach

The survey (N=108) was done using a questionnaire that was sent to project managers in the selected PBOs in Pakistan with a request to forward it to their immediate subordinates. Later, established statistical techniques (correlation and regression analyses) and gray incidence analysis models were applied to test the hypotheses.

Findings

The results from both methods reveal that TTM was more strongly correlated to perceived organizational performance of PBOs and, in general, public sector employees are more trusted than private sector employees. The gray method revealed that in both private and public PBOs, trust in project manager is greater predictor of perceived organizational performance, while statistical analysis confirmed this only for private sector PBOs. According to statistical analysis, the public sector employees who trust their top management are more likely to have good perception of the organizational performance. Later, the study argues that because of the proven superiority of gray methods over statistics on small samples, the results obtained from gray method should be used for decision making and implications.

Originality/value

The study is pioneer in evaluating the association between TIS and TTM in PBOs using both statistical and gray systems methods.

Keywords

Citation

Javed, S., Syed, A. and Javed, S. (2018), "Perceived organizational performance and trust in project manager and top management in project-based organizations", Grey Systems: Theory and Application, Vol. 8 No. 3, pp. 230-245. https://doi.org/10.1108/GS-01-2018-0009

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Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


1. Introduction

It has been observed that studies on the elements of success or failure of projects are usually impotent to analyze “intra-project dynamics” and overlook human-related “soft” dimensions of project evolution which are so essential for the project outcomes (Hobday, 2000). Most organizations tend to stress more on the process to achieve project deliverables and less on their employees’ interaction with the project and organization (Hardy-Vallee, 2012). For instance, Levasseur (2010) concluded that 65 percent of the reasons of project failures in the IT sector are due to people-related issues and rest due to technical reasons. If even in a “technical sector” like IT, the project failures due to “people issue” can amount to more than 60 percent, then what could be the situation in non-technical sectors encompassing NGO management, construction management, disaster management, etc.? Perhaps, the figure could be quite alarming. A research involving more than 10,000 projects from 30 countries, cited in Hardy-Vallee (2012), reveals that just 2.5 percent of the firms effectively finished all of their projects. Thus, it can be argued that project managers are too obsessed with the “tangible deliverables” (physical outcomes) of the projects that they even sometimes oversee the human aspect that is directly responsible for the smooth functioning of the project management processes and organizational performance/productivity. As Weber et al. (2008) demonstrated, a lack of trust can result in increase in cost of doing business; hence, it can be argued that fields like project management where people-related issues significantly impact project failures or successes and achieving project deliverables within cost is the most important objective of almost all project managers, ignoring the role of trust is no more affordable by responsible organizations. Further, in underdeveloped countries like Pakistan where recourses are scarce and clients are becoming more and more conscious and demanding with the passage of time, effective and efficient utilization of resources to minimize project failures is of extremely significant importance.

While considering aforementioned context, this research was undertaken to bridge the gap in project management literature concerning human side that is relatively ignored as compare to non-human (or technical) side that deals with management of tangible deliverables (time, cost and quality). Considering the fact that project management literature is more obsessed by the hard issues concerning project performance and less by the soft issues that involve people-related aspects, the study identified two soft areas relating to management, i.e. the trust in top management (TTM) and trust in immediate supervisor (TIS), and how they affect perceived organizational performance. The strength of their relationship was gauged in the environment surrounding project-based organizations (PBOs) with a view to fill the gap in the literature as the review of literature suggested that examination of the strength of the relationship of perceived organizational performance with these two factors in the PBO context is almost nonexistent. For the general readers, it should be noted that “PBO is an organizational form whereby projects are the primary units for coordinating and integrating production and innovation” (Wei and Miraglia, 2017). Further, the review of literature (see Ellis and Shockley‐Zalabak, 2001) suggested that compared to TIS, TTM is more strongly linked to perceived effectiveness of organizations but in PBOs where immediate supervisor of the workforce (project manager) is more visible than top management to most of the employees, the existing research was almost silent. Also, even though the studies report that public sector employees tend to be more satisfied and more trusted than private sector employees (Zeffane and Melhem, 2017) but the relative importance of the TIS and the TTM within public and private systems is not fully explored.

More precisely, the research aims to look at the association of perception of organizational performance with TIS and TTM in Pakistan’s PBOs through two alternative data analysis methods.

2. Literature review

There are quite abundant studies available on managerial trust and organizational trust in various contexts; however, when one desires to study the relationship of TIS and TTM with the organizational performance in PBOs, the studies are scarce.

The possible reason could be that initially project studies were obsessed with technical and engineering issues, with contexts built around the environment of construction, development and engineering industries, whose performance was mostly “concrete” and tangible and the nature of their work was less operational and they mostly worked on time-bound assignments or projects. For such industries, the management of time and resources (cost) and meeting deliverables (quality) were of prime concern and their concern for workforce was less apparent. However, the focus of project studies is now moving from more technical and engineering topics to a broader organizational perspective (Geraldi and Söderlund, 2018). With the evolution and development of human resource management philosophy, the other fields, including project management, too evolved and organizations, both local and global, began seeing their workforce, not the machines and tools, as most essential organizational asset (see e.g. Keegan et al., 2018; Sparrow et al., 2016; Fulmer and Ployhart, 2014). Today, project-based methods of working have become necessary in managing really unique, novel and transient operations and activities (Turner and Keegan, 2001) and thus managing projects in non-conventional way is a need of time. Also, as project management, or project organizing, is at crossroad of different fields, it would continue benefiting from concepts and ideas from different fields, further enriching its body of knowledge and practices.

2.1 Trust

Trust is a very divisive concept in literature. Literature suggests that there exists scant agreement on what trust actually means (Khan, 2012), leading to absence of its universally recognized scholarly definition (Rousseau et al., 1998) and difficulty in conceptualizing trust (Colquitt et al., 2007; Warren, 2012). Different scholars have conceptualized trust differently in their studies. Some identify trust having two forms, “reliability/dependability and emotional trust/faith,” some categorize it into “cognitive and affective trust” (Khan et al., 2015) and for some, trust is a distinct phenomenon from trustworthiness and trust propensity (Colquitt et al., 2007).

Chow and Chan (2008) and Milliken et al. (2003) have included trust in social capital, as it is not a product of personal attributes of employees but an upshot of their interpersonal relationships. Based on a previous study, Mayer and Gavin (2005) concluded that “trust in management is important for organizational performance.” Weber et al. (2008) have demonstrated that a lack of trust can result in an increase in cost of doing business; hence, it can be argued that fields like project management where people-related issues significantly impact project failures or successes and achieving project deliverables within cost is the most important objective of most of the project managers, ignoring the role of trust is no longer affordable by responsible organizations. Adler (2001) has summarized different dimensions and components of trust, as shown in Table I.

The concept of trust is probably as primitive as the oldest forms of human relationship (Watson, 2005). Although trust remained a vital concept since ages, it received comparatively modest recognition in research until a little while back (Watson, 2005). In the most recent decade, trust has turn into an important study area within the field of organizational studies (Matzler and Renzl, 2006). Interest in the study of trust has been shown by researchers from different fields of knowledge like psychology, management, marketing, organizational behavior, public relations (Watson, 2005), entrepreneurship (Khan et al., 2014, 2015), sociology (Matzler and Renzl, 2006), ethics (Colquitt et al., 2007; Muller et al., 2013), economics (Falk and Kosfeld, 2004; Weber et al., 2008), education (Swain, 2007; Basch, 2012) and science (Haerlin and Parr, 1999; Gauchat, 2012). Even though this multi-disciplinary attention has enriched the concept, a variety of studies has complicated incorporation of a range of views on trust into a concurrent viewpoint (Watson, 2005).

Trust is often recognized as a key concept in understanding managerial relationships, and nourishing and keeping trust is regarded as crucial to productivity of organization and management (Watson, 2005). Studies reveal that trust influences individual performance (Colquitt et al., 2007; Naber et al., 2015), team performance (Dirks, 1999; Khan et al., 2015) and organizational performance (Watson, 2005; Salamon and Robinson, 2008). It has been reported that public sector employees tend to be more satisfied and more trusted than private sector employees (Zeffane and Melhem, 2017). In an environment where interpersonal trust is abundant, fewer resources are available for investigating trustworthiness of others, and thus greater resources are available for productive activities (Lim et al., 2018). Literature demonstrates that in relationships surrounded by superior level of trust, parties involved often sincerely try to help each other on long-run basis (Wang et al., 2015). Not only in comprehending individual behavior, but trust is also equally significant in comprehending economic and financial behavior of an organization (Weber et al., 2008). Its relation to organizational performance is no more an unfamiliar concept within management literature; thus, overlooking the role of trust in an organization can augment distrust, resulting in adverse consequences for organization and its management.

In this study, managerial trust and trust in management are used synonymously and are the sum of TTM and TIS, as defined by Ellis and Shockley‐Zalabak (2001). This is a relatively new concept of seeing managerial trust in light of TIS and top bosses and is quite relevant to project management field as in projects, where project manager is immediate and direct supervisor of key personnel and functions, whereas top management of the company is believe to be more visible to clients than to its workforce on project sites. Thus, the study will facilitate how these two trusts vary for the project managers working on the projects of PBOs and how this trust in management affects perceived performance of the organizations.

2.1.1 TTM and TIS

Trust is a widely studied concept in the social psychology literature and is gaining widespread attention in the HR/management literature. Interpersonal trust, support and respect are manifestations of high group cohesion in a team. Trust is frequently supposed to be the “lubrication” that keeps an organization’s social systems intact and advances its effectiveness and efficiency (Gould-Williams, 2003). Trust in management infuses more power and potency among the subordinates (Bahrami et al., 2012) and is important for organizational performance (Mayer and Gavin, 2005). McCauley and Kuhnert (1992) stressed that “an employee may trust his peers but not his supervisor,” thus the level of trust varies. Cook and Wall (1980) too have demonstrated in their research that there is nothing like one trust for all! Most of the works on trust and workplace relationships involve comprehension of managers and their immediate subordinates, while TTM received very little attention in literature (Ellis and Shockley‐Zalabak, 2001). Employees believe that top managers have power over most of the attributes of organizations, so if employees’ perception concerning organizational attributes, like rules and regulations, is positive then their views concerning top managers are supposedly equally be positive and they are less likely to distrust their top management (McCauley and Kuhnert, 1992). Thus, without gauging trusts in management (top and immediate), it is not appropriate to comprehend how employees see their management and performance of their organization.

2.2 Perceived organizational performance

In this study, the terms “perceived organizational performance,” “perceived firm performance” and “perception of organization’s performance” are used interchangeably and are abbreviated as “POP.”

An important factor related to trust is perceived organizational performance. Trust in an organization has been known as the “feature of a thriving organization” (Bahrami et al., 2012). Prusak and Cohen (2001) signified the importance of trust and strong relationships as a social capital that glues an organization together. Bakieve (2013) also considered interpersonal trust an important attribute of organizational social capital associated to perceived organizational performance and then revealed the influence of interpersonal trust on the performance of police.

Allen et al. (2007) highlighted a brilliant discussion in performance measurement studies concerning two different measures to gauge organizational productivity, each with its own pros and cons. They say “objective measures” are more absolute but are narrow in scope, while “subjective measures” lack absoluteness or reproducibility but afford affluent depiction of a firm’s productivity relative to its competitors and strengthen the generalizability of the research outcomes (Allen et al., 2007). Further, it has been proven that “subjective measures” involve “perceptual” attribute of investigation (Allen et al., 2007) and measures of POP are positively related to organization’s “objective measures” (Delaney and Huselid, 1996; Carmeli and Schaubroeck, 2008; Singh et al., 2016). It has also been observed that POP is also dependent on the performance of other organizations in the same industry (Herman and Renz, 2008). In the study by Delaney and Huselid (1996), “perceptual measures” of organizational performance are found to be dependent on “organizational performance” and “market performance.” Thus, using subjective measures to gauge organizational performance is not an inappropriate move and carefully planning subjective measures can successfully access organizational performance (Singh et al., 2016).

2.3 Grey system theory (GST)

GST is a theory first proposed by a Chinese Professor Julong Deng with the publication of his two papers in 1982 (Mahmoudi et al., 2018; Liu and Forrest, 2007; Julong, 1982). Julong (1982) defined a gray system as “a system containing knowns and unknowns,” i.e. a system with incomplete information. Today, the GST has a well-developed body of knowledge that can be manifested in its framework, which can be found in Liu et al. (2016, Chapter 2). The theory is in the phase of continuous evolution and has been successfully applied in various fields of natural and social sciences including engineering, economics, management and project management. One of the key advantages of this theory is that it can produce valid results even when sample size is small (Javed and Liu, 2017, 2018; Liu and Forrest, 2007). This is one of the distinct features of this theory that make it different from statistics (Ng and Deng, 1995). The theory proposes an alternative method to statistical data analysis and studies have found that in many cases, the predictions made through gray systems methods were superior to the predictions made through statistical methods (see e.g. Javed and Liu, 2018; Lin and Wu, 2011; Chen and Xie, 2005). Gray incidence analysis (GIA) models constitute important part of the framework of GST. GIA models are also known as gray relational analysis (GRA) models. First, GIA model was proposed by Julong Deng and is now known as Deng’s GIA model. Later, Professor Sifeng Liu proposed Absolute Degree GIA model, also called absolute GRA model. In a nutshell, Deng’s GIA model gives the measure of influence that one variable represented by a data sequence exerts on the other and Absolute Degree GIA model gives the measure of association between them.

The foundation of Deng’s GIA model rests upon Deng’s degree of gray incidence, which is also known as gray relational grade. This degree is represented as γ0i or γ(X0, Xi) and is given by:

γ ( X 0 , X i ) = 1 n k = 1 n γ ( x 0 ( k ) , x i ( k ) ) ,
where:
γ ( x 0 ( k ) , x i ( k ) ) = min i min k | x 0 ( k ) x i ( k ) | + ξ max i max k | x 0 ( k ) x i ( k ) | | x 0 ( k ) x i ( k ) | + ξ max i max k | x 0 ( k ) x i ( k ) | .

Here, X0=(x0(1), x0(2), …, x0(n)) and Xi=(xi(1), xi(2), …, xi(n)) are the sequences representing a gray system for ξ∈(0, 1), the distinguishing coefficient (Liu et al., 2016, 2017). Usually, the scholars suppose the value of ξ to be 0.5 even though the rationale behind this supposition is not yet universally established. The computing steps to calculate Deng’s Degree of Gray Incidence for two data sequences X0 and X1 are shown in Liu et al. (2016, p. 74) and have been reproduced below:

  • Step I: calculating the initial image (or average image) of X0 and Xi, i=1, 2, …, m, where:

    X i = X i / x i ( 1 ) = ( x i ( 1 ) , x i ( 2 ) , , x i ( n ) ) ; i = 0 , 1 , 2 , .. , m .

  • Step II: computing the difference sequences of X 0 and X i , i=1, 2, …, m, as:

    Δ i ( k ) = | x 0 ( k ) x i ( k ) | , Δ = ( Δ i ( 1 ) , Δ i ( 2 ) , , Δ i ( n ) ) , i = 1 , 2 , , m .

  • Step III: finding the maximum and minimum differences:

    M = max i max k Δ i ( k ) ,
    m = min i min k Δ i ( k ) .

  • Step IV: calculating incidence coefficients:

    γ 0 i ( k ) = m + ξ M Δ i ( k ) + ξ M .

  • Step V: computing the Deng’s degree of gray incidence:

    γ 0 i = 1 n k = 1 n γ 0 i ( k ) , i = 1 , 2 , , m .

    On the other hand, for a system represented by the sequences Xi and Xj, the absolute degree of GIA model is represented as:

    ε i j = 1 + | s i | + | s j | 1 + | s i | + | s j | + | s i s j | ,
    where:
    s i = 1 n X i 0 d t , s j = 1 n X j 0 d t , s i s j = 1 n ( X i 0 X j 0 ) d t .
    Here, ɛij is absolute degree of gray incidence (Liu et al., 2016, 2017). The computational steps of the model are listed below:

  • Step I: calculate the zero-starting point images of two sequences X0 and X1.

  • Step II: calculate |s0|, |s1| and |s1s0|.

  • Step III: calculate absolute degree of gray incidence between the sequence X0 and X1:

ε 01 = 1 + | s 0 | + | s 1 | 1 + | s 0 | + | s 1 | + | s 1 s 0 | .

For the detailed discussion on the Deng’s and Absolute degree GIA models, their properties and calculation steps, Liu et al. (2016) can be consulted.

3. Framework and methodology

3.1 Framework and hypotheses

Trust leads to enhanced organizational performance (Gould-Williams, 2003). Studies (Delaney and Huselid, 1996; Carmeli and Schaubroeck, 2008; Singh et al., 2016) suggest that measures of POP are positively related to organization’s “objective measures,” i.e. a firm’s real performance. In the work by Ellis and Shockley‐Zalabak (2001), POP was found to be more strongly linked to TTM than to TIS. But how this relationship can vary in the PBOs where immediate supervisor (project manager) is more visible than top management (senior managers) to most of the employees needed exploration. In light of these questions, the following hypotheses were proposed:

H1.

TTM is positively linked to perceived organizational performance (POP).

H2.

TIS/trust in project manager is positively linked to perceived organizational performance (POP).

3.2 Instrument development and its testing

Trust in management was considered as the sum of TTM and TIS in the study. A total of 6 research items to gauge TTM (first independent variable) and 14 research items to gauge TIS (second independent variable) were adapted from Ellis and Shockley‐Zalabak (2001) on a five-point Likert scale with range from 1 (very little) to 5 (very great). Perception of organization’s performance (POP), the dependent variable, was measured using the 11 research items adapted from Delaney and Huselid (1996) which considered perception of a firm’s performance (POP) as the sum of perceived organizational performance (seven research items) and perceived market performance (four research items), on a five-point Likert scale with range from 1 (much worse than others) to 5 (much better than others).

To gauge the reliability of research instrument, Cronbach’s α value of 11 items associated with POP was calculated that turned out to be 0.877. Cronbach’s α value of the six items associated with TTM turned out to be 0.867. Cronbach’s α value of the 14 items associated with TIS turned out to be 0.911. Therefore, the reliability of the research instrument was very good. However, the response rate was about 57 percent (112/197). The reasons for low response rate could be attributed to the possibility that several respondents did not either check their e-mails containing the questionnaire or the e-mails sent to them went into their spam folder. Another possibility that cannot be ruled out is that may be many project managers whom the authors sent e-mails for forwarding them to their immediate subordinates failed to do so for the reasons best known to them.

3.3 Data collection and analysis

The respondents were interviewed using a questionnaire containing close-ended questions against a five-point Likert scale. An online questionnaire was created for this purpose. There were two parts of the questionnaire. One part contained demographic variables and the other part contained close-ended questions to gauge TTM, TIS and POP. Thus, the study involves respondents working in PBOs operating in six industries as defined in Kerzner (2009, p. 26).

Difficulty in finding sufficient number of employees who directly report to project managers leads the researcher to seek the help of project managers themselves to approach their immediate subordinates. Two project managers from two different high-tech project management firms were contacted who provided to the researchers the e-mail addresses of 224 project management practitioners working in Pakistan’s PBOs, on the condition of being anonymity. An online questionnaire was created and a link to it was e-mailed to all of them who were requested to forward it to their immediate subordinates. However, the e-mail containing the link to questionnaire was successfully delivered to only 197 project managers of different PBOs. After several reminders and continuous follow-up, at the end we found 112 responses out of which 108 (=N) responses were properly filled and thus were selected for data analysis. Out of them, 43 were from public sector PBOs and 65 were from private sector PBOs.

For gray data analysis, since the data were primary therefore first of all the geometric mean of responses obtained against each research item was calculated. Later, using these geometric means the data sequences as required for conducting GIAs were developed and the tests were executed at the Gray Modeling Software (v6.0), developed by Nanjing University of Aeronautics and Astronautics, China. For statistical data analysis, IBM SPSS (version 20) was used.

4. Results and discussion

The sample comprises the employees who were directly reporting to project managers overseeing projects of PBOs from two sectors and five industries. The detail of demographics is presented in Table II.

4.1 Statistical analysis

Correlation and regression analyses were conducted through IBM SPSS (version 20) and the results have been shown in Tables IIIVI. Missing values, outlying and improperly filled questionnaires were removed from the scene before the data analysis using the software.

According to correlation analysis, as shown in Table III, in both private and public PBOs, perceived organizational performance is most strongly associated to TTM. However, in public sector PBOs, this association is relatively much stronger which implies that for the employees working under the organizational project managers in public sector PBOs, their level of TTM is significantly connected to their perception of overall organizational performance. Relationship between the two trusts was observed relatively stronger in private PBOs.

The regression analyses, as shown in Tables IVVI, show that in private PBOs, TIS (trust in the project manager of the private PBOs) is relatively greater predictor of the perceived performance of the PBOs. However, in public PBOs, TTM is much greater predictor of the perceived performance of the PBOs. In general, the results are indicating that public sector employees are more trusted.

4.2 Grey incidence analyses

According to Absolute Degree GIA model, as shown in Table VII, in both private and public PBOs, perceived organizational performance is most strongly associated to TTM. However, in private sector PBOs, this association is relatively stronger which implies that for the employees working under the organizational project managers in private sector PBOs, their level of TTM is more significantly connected to their perception of overall organizational performance. Relationship between the two trusts was observed relatively stronger in public PBOs.

According to Deng’s GIA model, as shown in Table VIII, in both private and public PBOs, TIS (trust in the project manager of the PBOs) is the strongest predictor of the perceived performance of the PBOs.

The Deng’s degree-based gray data analysis reveals that, on average, employees in public sector are more trusted (average=0.6530) than the employees in private sector (average=0.62065). The result is consistent with the already reported facts (e.g. as reported by Zeffane and Melhem, 2017).

Overall, the results from both statistical and gray incidence analyses of data produced not entirely consistent results. Even though according to both approaches, perceived organizational performance is more strongly associated to TTM than to trust in project manager, according to statistical correlational analysis this association is much stronger for public PBOs (0.648>0.481) and according to absolute degree GIA this association is stronger for private PBOs (0.5530>0.5454). According to statistical regression analyses, in private PBOs, the trust in project manager and in public PBOs, the TTM is greater predictors of the perceived performance of the PBOs. However, according to Deng’s GIA, in both private and public PBOs, the trust in project manager of the PBOs is greater predictor of the perceived performance of the PBOs. Now question arises the results from which method should be trusted? The solution lies in the analysis of the roots of both methods: “Probability theory and statistics deal with the data nexus or relation of the systems encompassing large samples embodied in statistic history laws, regression models or probability relations, and subject to laws, such as the law of large samples” (Ng and Deng, 1995), whereas “Grey systems theory […] focuses on the study of problems involving small samples and poor information […] [by] […] generating, excavating, and extracting useful information from what is available” (Liu et al., 2016). Also, the superiority of gray methods on statistical methods is not unknown in literature (see e.g. Javed and Liu, 2018). Since GST is capable of making sound decision making through small samples as well wherever the predictions through statistics are dependent on larger samples, therefore one can argue that the sample size involved in this study was insufficient to make sound judgment concerning the problem under study. Therefore, the results obtained through GIA are more likely to be acceptable.

5. Conclusion and recommendations

Studies show that human resource issues influence perception of firm performance (Brierley and Gwilliam, 2017; Perry-Smith and Blum, 2000; Delaney and Huselid, 1996). Also, the effect of trust on perception is not unknown in literature. Trust has repeatedly been related to variety of perceptions associated with contentment (Ellis and Shockley‐Zalabak, 2001). Building upon earlier research works, Matzler and Renzl (2006) concluded that trust in senior management increases job contentment and that shared trust between management and subordinates increases latter’s job contentment. Thus, the managers who are seen as trustworthy are in better position to negotiate and compel their subordinates to work on new assignments or new project. The teams of such managers can perform better under non-routine tasks. It has been widely acknowledged that trust is a decisive factor resulting in the increased firm performance (Gould-Williams, 2003). Distrust by superiors results in the absence of motivation to perform well in subordinates (Falk and Kosfeld, 2004) and the resulting lack of commitment may lead to even project failure (Levasseur, 2010). Further, if trust is either incomplete or entirely missing, employees often add to the cost to alleviate their predicaments (Weber et al., 2008). Studying trust in PBOs is necessary because interpersonal trust is one of the factors that not only affect centralization and decentralization in organizations but are also crucial to comprehend the economic necessity of decision making (Malone, 1997).

According to statistical correlation analysis and absolute-degree-based gray data analysis, in both private and public TTM was more strongly correlated to perceived organizational performance of Pakistani PBOs. Both methods reveal that, in general, public sector employees are more trusted than private sector employees. There is an agreement between the two data analyses methodologies as far as these findings are concerned. The regression analysis showed that trust in the project manager is relatively greater predictor of the perceived performance of the private sector PBOs in Pakistan, whereas in public PBOs, the TTM is much greater predictor of the perceived performance of the PBOs. Considering the views of the people who are directly reporting to the project managers in PBOs, according to statistical analysis (and Deng’s degree-based gray data analysis as well) for the private sector employees who trust their immediate supervisors (project managers) are more likely to have good perception of the performance of their PBO. Again, there is an agreement between the two methodologies. According to statistical analysis, the public sector employees who trust their top management are more likely to have good perception of the performance of their PBO. However, Deng’s degree-based gray data analysis reveals that not only in private PBOs, in public PBOs as well, trust in project manager is greater predictor of perceived performance of the PBOs. Here, the two methods are producing contradictory findings. Thus, the results from which methodology should be used for policy implications? As highlighted in the aforementioned section and considering the association between statistics and probability theory, it can be argued that statistical analysis tries to make predictions from the data that exhibit probability distribution and require large sample size to make reliable predictions, while on the other hand GST makes predictions by excavating maximum information from the incomplete or small data sets and is known for making better predictions under uncertainty, thus one can conclude that in the given scenario the findings from the gray data analysis can be used for policy implications and future decision making, as the sample size in the study was small.

The study shows if sample size is not properly selected, then statistical analyses can produce misleading results. The study suggests that reliability and validity of research instrument and soundness of framework alone cannot guarantee the validity of the results, if sample size is not appropriately selected using the standard methods of sample selection as proposed in statistical literature. On the other hand, since GST and its models work well on small samples as well, therefore, the results produced in this study through gray incidence analyses are more likely to be acceptable. These results imply that in both private and public sectors PBOs, trust in project managers, working on the projects of these PBOs, is more significant predictor of the perceived performance of these PBOs, i.e. the immediate subordinates of the project managers who see their immediate supervisor (project manager) as trustworthy are more likely to perceive their organization (not project, strictly speaking!) as well performing. These results have particular significance and implications. For the decision makers (who are usually top managers) of the PBOs, the organizations for whom organizational success primarily depends on the success of their projects, it is important to nourish such an environment in their organizational projects which can foster a trustworthy relationship between their appointed project managers and their workforce working under those project managers. If their project managers’ subordinates do not see him reliable, they are more likely to be suspicious about their organizational performance as well. This can reduce their motivation level and citizenship behavior and can cause different problems in long run. Also, the results and literature revealed that public sector employees are more trusted than private sector employees so the public and private nature of the two systems also seems influencing the variation in trust level. This nature of a system/organization is usually beyond the control of the management/controllers of the organization/system. Thus, the management of private sector can devise some other methods and incentives to boost up the overall trust level. For example, for private sector PBOs, easier way is to promote the good and trusting image of the project manager (rather than the top management) among the employees as it is a stronger predictor of the perceived organizational performance.

The study suggests to future researchers when a sample size seems small or insufficient, then gray data analysis should be preferred over statistical analysis. Here, it is worth noting that the current study does not negate the importance of statistics as its successes are well known to the data analysts and the scientific community; however, since GST is grounded on different sets of principles and hypotheses, therefore its strengths and limitations are different from that of statistics. Also, future researchers can re-test the three-variable framework presented in this study by enlarging the sample or by retesting it in different environments to confirm (or disconfirm!) the findings of the current study. Further, the possibility of another interpretation of the difference between the results obtained from the two approaches cannot be ruled out.

Dimensions and components of trust

Dimensions Components
Sources Familiarity through repeated interaction
Calculation based on interests
Norms that create predictability and trustworthiness
Mechanisms Direct interpersonal contact
Reputation
Institutional context
Objects Individuals
Systems
Collectivities
Bases Consistency, contractual trust
Competence
Benevolence, loyalty, concern, goodwill, fiduciary trust
Honesty, integrity
Openness

Source: Alder (2001)

Demographics

Descriptions Frequency Percentage
PBOs’ industry
In-house R&D 40 37.0
Small construction 11 10.2
Large construction 11 10.2
Aerospace/Defense 12 11.1
MIS 21 19.4
Engineering 13 12.0
Sector
Public sector 43 39.8
Private sector 65 60.2

Note: n=108

Correlations

Private sector PBOs (N=65) Public sector PBOs (N=43)
POP TTM TIS POP TTM TIS
POP
Pearson correlation 1 0.481** 0.476** 1 0.648** 0.409**
Sig. (2-tailed) 0.000 0.000 0.000 0.006
TTM
Pearson correlation 0.481** 1 0.565** 0.648** 1 0.227
Sig. (2-tailed) 0.000 0.000 0.000 0.143
TIS
Pearson correlation 0.476** 0.565** 1 0.409** 0.227 1
Sig. (2-tailed) 0.000 0.000 0.006 0.143

Note: **Significant at the 0.01 level (two-tailed)

Model summary

Model R R2 Adjusted R2 SE of the estimate
1: Public PBOs 0.541a 0.293 0.27 0.63712
2: Private PBOs 0.702a 0.492 0.467 0.46573

Note: aPredictors: (constant), TIS, TTM

ANOVAa

Model Sum of squares df Mean square F Sig.
1: Private PBOs
Regression 10.417 2 5.209 12.832 0.000b
Residual 25.167 62 0.406
Total 35.585 64
2: Public PBOs
Regression 8.41 2 4.205 19.386 0.000b
Residual 8.676 40 0.217
Total 17.086 42

Notes: aDependent variable: POP; bpredictors: (constant), TIS, TTM

Coefficientsa

Unstandardized coefficients Standardized coefficients
Model B SE β t Sig.
1: Private PBOs
(Constant) 1.797 0.335 5.360 0.000
TTM 0.249 0.104 0.311 2.405 0.019
TIS 0.258 0.111 0.300 2.322 0.024
2: Public PBOs
(Constant) 0.859 0.403 2.131 0.039
TTM 0.453 0.09 0.585 5.059 0.000
TIS 0.285 0.119 0.276 2.387 0.022

Note: aDependent variable: POP

Absolute degrees of gray incidence

ɛ POP TTM TIS
POP 1
TTM 0.5530a 1
0.5454b
TIS 0.5022a 0.5015a 1
0.5124b 0.5033b

Notes: aPrivate sector PBOs; bPublic sector PBOs

Deng’s degrees of gray incidencea

γ TTM TIS
Private PBOs 0.5832 0.6581
Public PBOs 0.6092 0.6968

Note: aResponse/dependent variable: POP

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Supplementary materials

GS_8_3.pdf (13.7 MB)

Acknowledgements

The work on this paper was started when S.A. Javed, the corresponding author, was writing his Masters in Project Management thesis, under the supervision of Dr A.M. Syed, which was submitted to COMSATS Institute of Information Technology (CIIT), Pakistan, as a requirement for graduation. Therefore, he wants to thank CIIT. Later, after joining PhD program at Nanjing University of Aeronautics and Astronautics (NUAA), China, he revised the work. During the revision phase, S. Javed from the GreySys Foundation, Pakistan, significantly helped. The authors also want to thank the anonymous reviewers for their comments on the earlier version of this paper.

Corresponding author

Saad Ahmed Javed can be contacted at: saad.ahmed.javed@live.com