Do managers learn more about successful project management methods from articles in high impact factor journals?

Abstract

BACKGROUND:

In recent years, the need to develop performance-based measurement systems to improve project management outcomes has dramatically increased. Managers still take various risks during the course of managing projects which lead to ineffective decision making. A range of theories discuss such behaviors. These theories demonstrate that the discussion of risk embedded in non-optimal decision-making processes is based on theory rather than practical knowledge. However, various components of project management can be derived from academic best practices for decision making.

OBJECTIVE:

The study aims to explore whether articles in high impact journals tend to embody practical, rather than theoretical, knowledge thus closing the gap between academia and industry. The study is based on SEM and various machine learning classification methods.

METHOD:

The study was conducted using an NLP analysis of 1461 academic journals in the field of project management.

RESULTS:

Results show a significant positive relationship between the success of projects and the impact of new practical procedures. In contrast, a negative correlation was found between theories that use non-practical processes of effective project management.

CONCLUSION:

Managers can learn about new methods for project management from articles in high impact factor journals.

Keywords

Project management project procedures prospect theory project management decision making machine learning

Moti Zwilling is a Senior Lecturer in the Department of Economics and Business Administration at Ariel University, Israel. He serves as an advisory editor of the journal, Quality & Quantity, and as a reviewer for various journals, such as the Journal of Human Systems Management. He is an acknowledged expert worldwide in Data Science and Cybersecurity. He is a veteran of R&D and project management positions in the Israeli High-Tech industry.

Eyal Eckhaus is a Senior Lecturer in the Department of Economics and Business Administration at Ariel University, Israel. He received his PhD in management from Bar-Ilan University. His main research interests include management, leadership, technology and digital culture.

1 Introduction

In recent years, the need to develop performance-based measurement systems to improve project management outcomes has dramatically increased. As such, various methods and guidelines for project management exist for different types of industries [1, 2]. These methods and guidelines are presented in the scientific literature, such as academic journals, which are assessed by their impact factor. In addition, they are evaluated by their practical vs. theoretical contribution to the success of projects by practitioners.

For example, one researcher [3] developed a benchmarking framework for pharmaceutical projects. Another [4], who proposed a multidimensional performance measure for international joint venture projects, found that project evaluation based on collected data is often performed after project completion and not during project execution. Accordingly, another article [5, p.1615] cites the following statement [6]: “Most KPI (key performance indicators) are used for review purposes. As such, there are few opportunities to apply PMS (project management systems) results during the project because the projects that are their subject tend to have already been completed.” Indeed, it is an accepted fact that projects differ in their attributes with respect to location, method, level of required management experience, and project objectives affecting success. Therefore, managers are required to adopt new methodologies for project management, applying them to most of a project’s activities. Such methodologies are described in academic journals in which the theoretical methodologies are meant to contribute to the success of managers in leading projects to optimal results. To understand to what extent project managers can learn new project management methods from articles which are found to be suited to high impact journals, we offer a model which empirically demonstrates that journals with a higher IF (impact factor) illustrate the importance of practical over theoretical knowledge, thus closing the gap between academia and industry, an issue which is the subject of criticism by many commercial companies [7].

Project management methods for evaluating planning and control processes date back to the late 19th century [8]. These became standard during the 20th century in the evaluation of manufacturing processes. At the beginning of 1950s, project management methods emerged such as, total quality management (TQM) [9], which is applied in a range of fields including human resources management, marketing and leadership. Since that time many others approaches appeared. However, it soon became apparent that some methods are more effective for project management. In 1996, two researchers [10] provided a general performance model to apply for projects managed by a limited number of project managers, at times by only one individual. They emphasized the need to link project outputs to the process of decision making and suggested using a management decision tool to aid in better execution of project delivery methods. Prospect theory, as discussed in the literature, also supports their approach, particularly with reference to how issues relevant to behavioral finance with respect to the project (in terms of budget) influence managerial decision making. In prospect theory, individual decision making processes with their considerations of loss aversion, mental accounting, and regret aversion affect financial decisions. At the core of this theory is the claim that people do not ascribe the same level of happiness and regret to the same effects. That is, the average individual tends to be more sensitive to loss of money or negative outcomes than to gains, such as a positive outcome or recognition as a result of their decision making process. In a similar context, planned behavior theory focuses on the influence of psychology on the behavior of financial practitioners and the subsequent effect of their decisions, especially those considered inefficient for the firm and market outcomes. This theory highlights how irrationality and sub-optimal decision making processes, including managerial beliefs and preferences, can lead to biases, producing undesirable financial risk [11].

Drawing on prospect theory, it was noted that non-optimal heuristics can lead to sub-optimal investment decisions [12]. More recently, a pair of researchers [13] applied planned behavior theory to explain risk response decisions in information technology project management. Their claim is that managers generate risk during project management as a result of non-effective decision-making processes. They also track how these processes may result from additional factors that influence managerial risk management, which, in turn, ultimately impacts project outcomes. These risks may involve manager focus on project targets and not on effective measurement of its processes [14].

This observation supports the need for more effective measurements of project management processes provided by scientific resources, such as academic journals. With that, it is expected that publication of manuscripts in high quality journals with correspondingly high impact factors (IFs) would be correlated with better management practices for managers. Since each project holds its unique characteristics, it is important for project managers to know how different project management methodologies can assist them in managing their projects successfully. To answer this question, researchers have proposed new methodologies and best practices for effective project management processes which are suitable to changing environments in the manufacturing, research and development, and service sectors.

More recently the gap between how managers and employees are educated to perform a job in the field of software management and their actual performance was shown by two researchers [15]. By evaluating publications in the field of software management, they showed that there is a lack of suitable academic-level software management teaching methods. According to the authors, most of the current practices deal only with the theoretical dimension of software management and not its practical application. For the actual skills in demand for future software engineers and managers, they suggested a new model called “MOSAICS” to serve as a practical guide for software project management.

Software project management is just one example of projects that involve complexity. Other project types are also characterized by well-defined procedures for the creation of software applications with a multiplicity of processes, ranging from requirements and design to implementation and testing, as well as the final product launch stage. Project constraints are mainly concentrated among three factors: time, quality, and cost. Therefore, the success of a software development process is crucially dependent on the management approach selected. In recent years, new methodologies for project management have emerged, with the “agile” method being increasingly popular in industry for improving coordination between the multiple parties involved in a project (e.g., team members, product owner and stakeholders). Yet, it has been shown [16 –18] that there is still a gap in how project management is studied and delivered in the classroom, especially in the field of software engineering, and how it is implemented in real-world industrial scenarios. As noted, [15] highlighted the need to improve IT student training outcomes by exposing them to different methods and providing them with practical rather than theoretical knowledge. They proposed a model of education focused on several practical elements such as: project theme, evaluation criteria, milestones, and team structure. They also argued that training should be focused on synergy, along with independence, collaboration, as well as additional mixed methods that involve both individual and team work. They conclude that students fail to receive enough skills for 1) practical collaboration scenarios and 2) implementation of theoretical knowledge into “real management scenarios.” As with the PIMIS (project issues monitoring information systems) method, which aims to assist students in developing their skills in software project management, additional qualitative and quantitative perspectives to improve skills in a competitive environment are urgently required, including the use of e-learning platforms and social networks [19, 20].

It can thus be deduced that there is a strong demand to evaluate critical project management processes and methodologies, especially in software and IT technology ecosystems. In addition, the effectiveness of existing methods delivered by academic institutions should be scrutinized, including the impact on the success of project-affiliated factors, as informed by theories popularized in these pedagogical environments. Therefore, the contribution of this study to academic and industrial practitioners and stakeholders is embedded in the study’s findings that indicate the need for the inspection and adoption of new processes and methods by project managers, which will be used to manage projects effectively. These new and innovative methods (which, in most cases, are available in high impact journals) are considered one of the significant factors for the success of projects.

This study is constructed as follows: In section 1, we introduce the research problem and its motivation. In section 2, we support our study through a literature review, where we emphasize the existing gap between methodologies prevalent in the literature and project success. Section 3 provides our methodology, while section 4 presents our results. In section 5, we discuss research implications, followed by section 6 on future perspectives and recommendations for improvement to the current study.

2 Literature review and hypotheses

In general, the literature defines a project as a temporary effort with a defined beginning, middle and end. Carried out with the aim of producing a specific product or providing a service, projects are generally executed under certain limitations such as time, cost, budget and other organizational, social, or legal constraints [21]. For example, a group of researchers [22] demonstrated the manner in which enterprises can enhance their stakeholder relations and their sales performance. The study’s authors emphasized the need for careful environmental sustainability considerations throughout the process of managing employees and customer relationships in business enterprises in order to overcome organizational constraints. Another constraint, from a social perspective, was shown. In their study the authors demonstrate how expatriation affects knowledge transfer as a driver for innovation and performance. The authors found that the adoption of human resource development practices are crucial to support knowledge transfer for various industrial purposes.

Above-mentioned researchers [21] described project elements: 1) At the end of the project, a product or service has been created in response to a pre-determined need; 2) Effort must be fairly complex, in accordance with parameters of execution duration, total costs, and technical complexity; 3) A relatively high level of uncertainty exists; and 4) Teamwork, which includes workers from different organizational fields, is an important factor.

The project management domain contains several important terms: 1) A successful project is defined as one that is delivered to the client on time, regarding all components and as defined by the client. This means that the project in its entirety has been fully implemented, all worker instructions have been carried out, and the client is already using the new system with all budgetary conditions having been met. 2) A failed project is defined as a project that has failed to meet one or more of the above conditions and has not been completed. 3) A challenged project is a project that has been completed and handed over to the client - it may or may not already have been implemented or the client may already be using a different system.

Projects may encounter one or more problems such as: deviating from the planned budget, failure to adhere to the timeline, failure to submit work content (failure to submit system content in its entirety, as per the project’s defined conditions), failure to complete worker instructions regarding project implementation, failure to integrate the new system into the client’s existing systems and in the worst-case scenario, a situation where the new system has been abandoned and the client has reverted to working with the old one. Two sets of researchers [24, 25, p.337] defined the success of a project as a “set of iron triangle components of cost, time and quality rather than the impacts or long-run benefits that are obtained from the resulting change.”

Another group of scholars [26], for example, emphasized the need to identify problems which they define as risks by a structured and multilayered framework so as to manage projects successfully where human resources and work environment attributes are involved. Such frameworks for process management are mentioned in published academic practical articles in the field of project management.

Today, many projects are still analyzed according to the critical path method (CPM) [27, 28] and advanced project methods such as critical chain project management (CCPM) [29]. The CCPM method is focused on resolving problems that arise from multitasking in human resources, which reduces productivity and detracts from project success.

Nevertheless, due to diverse practices and projects in the private and public sectors, the influence of practitioner characteristics on the optimal choice of project management practices has emerged as a salient issue in project management research [30] who show that the most widely-used project management practice is the tool set. There is serious concern as to whether methodologies delivered as theory, known as “best practice” guides, are sufficient for providing the required knowledge managers need to successfully implement projects. Moreover, project management practices are gaining increased visibility and importance in organizations [31 –33]. To emphasize this, researchers mentioned above [26] noted the need to mitigate projects risks and improve their success in industrial firms by the adoption of new frameworks to manage processes and actions, by the inclusion of both new working models and regulations. Even so, the problem of improving project success rates remains unresolved [34]. For example, successful project management should deliver tangible as well as intangible benefits to organizations, such as a better ratio of return on investment [35], which is considered tangible, along with intangible benefits for culture, organizational efficiency, client satisfaction and more [36 –38].

Yet, from the latest published literature, it turns out that a majority of studies are marked by several limitations. While some are informed by practices such as the project management body of knowledge (PMBOK) guide, which significantly contributes to project management performance, others are not. This claim is also supported by another researcher [39] who showed that there is a lack of robust procedures to ensure effective micro-knowledge management aiming to yield good project activity performance by individuals. The authors also emphasize the need for “coherent integration of knowledge deliverables into the project management tools and practices, especially in the context of the COVID-19 pandemic.” [39, p.529]

This phenomenon is intensified by inconsistency of published research and its relevance to industry, expressed by high quality research even as other elements come into play to determine whether a manuscript will finally be accepted for publication [40]. While many of these factors are controlled directly by the research, not all authors are aware of the potential of high impact scientific writing. As Matarese noted, “high-impact publishing means that the manuscript is considered important.” [40, p.453]

Indeed, the academic community has more or less universally come to regard impact factor (IF) as a sign of research quality. But, according to two studies [41] and [42], article citations in any one specific journal do not follow a normal distribution, hence leading to potential bias in the correlation between the attributes that can be interpreted as holding great potential in determining the publication quality (i.e., IF) and the journal where the study is finally published, which may hold a considerably lower IF, or none at all. Moreover, one of these scholars [41] observed that “citations to articles in any one journal are not normally distributed.”

Publishing in a high-IF journal will positively influence the number of citations, but this is not the only measure of scientific quality. However, other factors not directly related to research quality or subject area also affect citation rates. These include poor English skills, resulting in lower total citations and low h-index values [43]. In addition, it has been noted [44] that the citation success of a paper is indirectly influenced by the author’s geographical location and additional factors such as field of study and its particular citation practices.

It can thus be seen that in a diverse range of research fields, IF inconsistency remains a striking issue. This is also the situation with respect to research in the disciplines of business finance and project management in which many project management practices are dependent on theory and guidelines derived from academic scholarship. As such, this may lead to inefficient new project practices, leading us to evaluate and analyze factors and contextual components affecting whether journals are rated and categorized as having a high or low impact factor (high IF journals are categorized as Q1 or Q2, and low impact as Q3 or Q4). There is thus a need to evaluate their relevance to the success of projects in the industry. Our aim is to guide project leaders in the domain of project management from an applied point of view. Therefore, the research question of whether managers learn more about successful project management methods from articles published in high impact factor journals is what we aim to address through this study.

In summary, based on the literature review (listed in Table 5), it is deduced that new methodologies and frameworks for project management that can be measured in terms of financial success are needed. Such frameworks and guidelines should be incorporated within project procedures to be carefully followed by project managers. It is expected that new practical guidelines and frameworks will be found in applied journals (especially those with a high IF). Therefore, we can say that a gap persists between the use and evaluation of factors related to project management guidelines and their relevance to practitioners as they appear in high quality impact factor journals.

Table 5
List of All Literature Review Papers

# Authors Title of Manuscript Year Journal Name Volume & Pages

1 Seglen PO. Why the impact factor of journals should not be used for evaluating research. 1997 Bmj 314(7079)1497

2 Atkinson R. Project management; cost, time and quality, two best guesses and a phenomenon, it 1999 international journal of project management 17(6):337-42

3 Kwak YH, Ibbs CW. Calculating project management’s returnon investment. 2000 Project Management Journal. 31(2):38–47

4 Rand GK. Critical chain: the theory of constraints applied to project management. 2000 International Journal of Project Management 18(3):173-7

5 Herroelen W, Leus R. On the merits and pitfalls of critical chain scheduling. 2001 Journal of operations management 19(5)559-77

6 Meneghini R, Packer AL, Nassi-Calo L. Articles by Latin American authors in prestigious journals have fewer citations. 2008 PLoS One 3(ll):e3804

7 Vasconcelos SM, Sorenson MM, Leta J, Sant’Ana MC, Batista PD. Researchers’ writing competence: a bottleneck in the publication of Latin-American s 2008 EMBO reports 9(8):70Q-2

1 Andersen ES, Vaagaasar AL. Project management improvement efforts—creating project management value by u 2009 Project Management Journal 40(l):19–27

2 Eskerod P, Riis E. Project management models as value creators, 2009 Project Management Journal 40(1):4–18

3 Globerson S, Shtuv A, Zwikael. Project management planning, execution and control. 2009 Dyonon Publication

4 Kwak YH, Anbari FT. Availability-impact analysis of project management trends: Perspectives from allied 2009 Project Management Journal 40(2):94–103

5 Mengel T, Cowan-Sahadath K, Follert F. The value of project management to organisations in Canada and Germany, or do val 2009 Project Management Journal 40(1):28–41

6 Zlui L, Xin Y, Cheng C. Understanding the value of project management from a stakeholder’s perspective: Ci 2009 Project Management Journal 40(1):99–109

7 Matarese V. Emerging concepts in high-impact publishing: insights from the First Brasilian Colloqu 2010 Annali dell’lstituto superiore di sanita 46:451-5

8 Mir FA, Pinnington AH, Exploring the value of project management: linking project management performa no 2014 International journal of project management 32(2):202-17

9 Badewi A. The impact of project management (PM) and benefits management (BM) practices c 2016 International Journal of Project Management 34(4):761-78

10 Yarg Y, Sun L, Zhai J. Stakeholder relations, environmental sustainability and sales performance, 2018 Human Systems Management 37(31:333-43

11 Bornmann L, Wohlrabe K. Normalisation of citation impact in economics. 2019 Scientometrics 120(2):841-84

12 Djikhy S, Moustaghfir K. International faculty, knowledge transfer, and innovation in higher education: A huma 2019 Human Systems Management 38(4):423-31

13 Petroutsatou K. proposal of project management practices In public Institutions through a comparatif 2019 International Journal of Construction Management. 4:1-0

14 Tereso A, Ribeiro P, Fernandes G, Loureiro I, Ferreira M. Project management practices in private organisations. 2019 Project Management Journal 50(l):6–22

15 Caramidaru I, Ionica A, Leba M. A learning-by-doing based framework for micro-know ledge management in project o 2020 Human Systems Management 39(4):529-536

16 Popescu S, Santa R, Teleaba F, Ilesan H. A structured framework for identifying risks sources related to human resources in a - 2020 Human Systems Management 39(4):511-527

#	Authors	Title of Manuscript	Year	Journal Name	Volume & Pages
1	Seglen PO.	Why the impact factor of journals should not be used for evaluating research.	1997	Bmj	314(7079)1497
2	Atkinson R.	Project management; cost, time and quality, two best guesses and a phenomenon, it	1999	international journal of project management	17(6):337-42
3	Kwak YH, Ibbs CW.	Calculating project management’s returnon investment.	2000	Project Management Journal.	31(2):38–47
4	Rand GK.	Critical chain: the theory of constraints applied to project management.	2000	International Journal of Project Management	18(3):173-7
5	Herroelen W, Leus R.	On the merits and pitfalls of critical chain scheduling.	2001	Journal of operations management	19(5)559-77
6	Meneghini R, Packer AL, Nassi-Calo L.	Articles by Latin American authors in prestigious journals have fewer citations.	2008	PLoS One	3(ll):e3804
7	Vasconcelos SM, Sorenson MM, Leta J, Sant’Ana MC, Batista PD.	Researchers’ writing competence: a bottleneck in the publication of Latin-American s	2008	EMBO reports	9(8):70Q-2
1	Andersen ES, Vaagaasar AL.	Project management improvement efforts—creating project management value by u	2009	Project Management Journal	40(l):19–27
2	Eskerod P, Riis E.	Project management models as value creators,	2009	Project Management Journal	40(1):4–18
3	Globerson S, Shtuv A, Zwikael.	Project management planning, execution and control.	2009	Dyonon Publication
4	Kwak YH, Anbari FT.	Availability-impact analysis of project management trends: Perspectives from allied	2009	Project Management Journal	40(2):94–103
5	Mengel T, Cowan-Sahadath K, Follert F.	The value of project management to organisations in Canada and Germany, or do val	2009	Project Management Journal	40(1):28–41
6	Zlui L, Xin Y, Cheng C.	Understanding the value of project management from a stakeholder’s perspective: Ci	2009	Project Management Journal	40(1):99–109
7	Matarese V.	Emerging concepts in high-impact publishing: insights from the First Brasilian Colloqu	2010	Annali dell’lstituto superiore di sanita	46:451-5
8	Mir FA, Pinnington AH,	Exploring the value of project management: linking project management performa no	2014	International journal of project management	32(2):202-17
9	Badewi A.	The impact of project management (PM) and benefits management (BM) practices c	2016	International Journal of Project Management	34(4):761-78
10	Yarg Y, Sun L, Zhai J.	Stakeholder relations, environmental sustainability and sales performance,	2018	Human Systems Management	37(31:333-43
11	Bornmann L, Wohlrabe K.	Normalisation of citation impact in economics.	2019	Scientometrics	120(2):841-84
12	Djikhy S, Moustaghfir K.	International faculty, knowledge transfer, and innovation in higher education: A huma	2019	Human Systems Management	38(4):423-31
13	Petroutsatou K.	proposal of project management practices In public Institutions through a comparatif	2019	International Journal of Construction Management.	4:1-0
14	Tereso A, Ribeiro P, Fernandes G, Loureiro I, Ferreira M.	Project management practices in private organisations.	2019	Project Management Journal	50(l):6–22
15	Caramidaru I, Ionica A, Leba M.	A learning-by-doing based framework for micro-know ledge management in project o	2020	Human Systems Management	39(4):529-536
16	Popescu S, Santa R, Teleaba F, Ilesan H.	A structured framework for identifying risks sources related to human resources in a -	2020	Human Systems Management	39(4):511-527

Based on the above, we developed hypotheses in which “shorthand” terms represent different variables to be measured. Thus, the term “finance” refers to all aspects related to a project’s financial issues, such as budgets, various costs, revenue, and more. “Procedure” refers to the implementation of various processes, methods and management frameworks. “Teach” refers to aspects of training and education of employees and managers related to the project described in the analyzed journal articles; “impact factor” is the journal’s IF ranking, considered a measure that reflects the average number of citations of articles published in scientific journals and also used as a proxy to indicate the relative importance of a journal within its field. The term “process” refers to the project delivery methods.

We hypothesize that:

H1. Finance (finance) is positively correlated with impact factor (impact factor) via the mediation of procedure (procedure) and teaching subject variables (teach)

The H1 Hypothesis, can be defined as composed of three sub-hypotheses:

H1a. Project procedures (procedure) positively affect impactfactor.

H1b. Teaching-related aspects (teach) negatively affect impactfactor.

H1c. Teaching-related aspects (teach) positively correlate with the process of project delivery methods (process)

H2. There is no direct relationship between financial aspects (finance) and impact factor (impactfactor).

3 Methodology

We implemented the study methodology in five steps:

Creation of an initial sample of data from the extraction of 1461 articles (published during the last 5 years) in the field of project management. Within each article we extracted the following text: The journal name in which the article was published, the title of the manuscript, the abstract text, and the affiliated keywords.

We performed a text analysis using specialized software developed to support text mining and analysis [45].

Statistical analysis based on SEM (structural equation modeling) was used to test the model’s goodness-of-fit. The SEM analysis was created by the AMOS^™ software. For this purpose, we first constructed the data to be uploaded to the SEM via Microsoft Excel. Next, we selected a group of variables and a group of values related to the following independent variables: procedure, teach, finance and process and the dependent variable, impact factor (IF). We then drew the path analysis model via the AMOS –path diagram model. The final SEM model provided estimations of co-variances and regression weights as well as the model’s fit measures. These measures enabled estimation of the quality of the SEM analysis.

For the comparison analysis among IF journals, we first concatenated all the dataset text files that end with “^*.txt” and embody a journal’s name into one file named: “journal.txt”. The concatenation was performed by the following Terminal command for i in ^*.txt; do echo > > $i; done; cat ^*.txt | sort –u > journal.txt. Next, we randomly selected 78 records (from the journal.txt file) whose journal name reflected one of the following domains: project management, education, and business and management. The 78 records were saved under the filename “IF_List.txt”. In the next phase, we opened the IF_List.txt file in Excel and for each record associated with a journal name, we extracted the JCR (journal citation reports) impact factor (five-year) and Rank in Category from the Web of Science website (https://www.webofscience.com/wos/woscc/ basic-search). We highlighted records associated with a journal’s name in the field of management (including business) in pale blue, those related to education in pale orange, and those related to project management in yellow. We sorted the records associated with the journal names according to their impact factor in ascending order and checked whether those in the field of project management with an IF > 3 contain on average more practical rather than theoretical articles (Table 6).

Finally, we used three machine learning classification methods to evaluate whether articles published in high impact factor journals in the domain of project management which may be of value to industry, can be classified by various variables as found in the text analysis process and the construction of the SEM model.

Table 6
Comparison Analysis of Journals with IF based on Web of Science Ranking

Elaboration on the above is provided in the next sections.

3.1 Initial sample

The authors extracted 1461 abstracts from articles in project management journals, along with the impact factor of the journal. The justification for extracting project management journals derives from the study’s objective to explore the degree of influence of theoretical studies versus practical articles. We therefore selected project management as a field that leans on theory and practice, which may be considered representative of other fields in management.

In order to extract the data from the journal, the following steps were performed:

We searched the entire manuscript list in the field of “project management.”

Next, we extracted and downloaded the publications into a separate directory.

We wrote a Python script and ran it under the Spyder 3.3.2 program through the Anaconda-Navigator platform. For each manuscript, we extracted an additional file with the following items: abstract, journal name, keywords used in the manuscript, and the title of the manuscript.

We also searched for the impact factor of each journal, which was then saved to a designated Excel file. Journal impact factor ranged from 0.035 to 12.244.

We measured each variable: project procedure (procedure), teaching-related aspects (teach), financial aspects (finance), and impact factor by their frequency values in each of the selected 1461 abstracts. Thus, the data was not collected using questionnaires but through a direct collection of text from the journal articles themselves. Hence, the total measurement for each variable derived from the N-gram frequencies was analyzed by text analysis methods, as described in detail in the Text Analysis section.

3.2 Analysis

We used R Studio for Structural Equation Modeling (SEM) [46] to test the model’s goodness-of-fit. SPSS v.25 was used for other statistical procedures such as correlations. TEXTIMUS v.1.0 [47] was used for N-gram and BoW (bag-of-words). Model fit was estimated using comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), Root mean square error of approximation (SRMR) [48], and χ2/df ratio (Hoe, 2008). Cutoff values of > 0.9 indicate an acceptable fit for CFI and TLI, values of < .06 for RMSEA, and values of < 0.8 for SRMR (Hu and Bentler, 1999); χ2/df ratio should be < 3 [49].

3.3 Text analysis

First, we generated N-gram frequencies. N-gram refers to a contiguous sequence of n words from a given sequence of text [50]. N-gram is often used in Natural Language Processing (NLP) [51]. Next, we used bag-of-words (BoW), known as the most common method for text analysis based on NLP [52]. Bag of words is used for representation in NLP and information retrieval domains, where a sentence is represented by a bag (or multi) set of words which disregards grammar and word order influence. Each word is assigned a value according to its appearance and the number of its appearances in the document. Hence, the measurement of each word is calculated by its occurrence in a specific sentence. Hence, using BoW, a set of keywords is selected as the bag of words [53] and searched for in all texts. Each keyword is assigned a dichotomous value (1/0) relating to whether the word appears in a document (1) or not (0) and the number of times it appears. Next, we analyzed the frequency of the words in all the sets, and compiled its value into groups of the 2000 highest frequency unigram and bigram sequences employed for the research variables. Similar to studies that employed BoW [54, 55], we then summed the frequencies of the groups in order to generate the variables. We performed stemming on every word collected. Stemming is useful for identifying variants of lexical forms [56]. That is, for the word “academic,” we also counted words such as “academics” and “academically.”

The variables were generated by using the following terms: finance –financial orientation, including words such as “investment,” “cost,” and “financial.” For procedure –best practices, including words such as “how to,” “rule,” and “agreement.” For teach –words such as “university,” “academic,” and “teach,” and for process –words such as “stage” and “step.”

Since teach involves the process of teaching, and no other possible perspectives of teaching, we added a correlation between teach and process. Another correlation was added between teach and procedure, based on the argument in this study which implies that they represent two opposite approaches. In the next step, we reinforced the results by showing that they are replicated using additional well-known classification algorithms for classifying labeled variables (also considered the dependent variables) by using the independent variables (the independent variables). In this study, the impact factor value (IF value) was used as the labeled dependent variable, where all other variables were considered independent.

3.4 Label classification

For the purpose of machine learning classification algorithms, we used Orange™ software, version 3.24.0, an open source machine learning and data visualization tool, and performed additional statistical procedures, such as inspecting the impact of the independent variables (procedure, finance, and teach) on the dependent variable (impact factor) (Fig. 1). The software was used with the following steps: 1. A table with the independent variables and class attribute (IF) was constructed. 2. A test and score widget was used to evaluate the following three models (logistic regression, random forest and k-nearest neighbors. 3. Evaluation of each model on the training set was used by the ROC (receiver operating characteristic) analysis and the confusion matrix. (Fig. 1 shows the general model scheme created by the software to test three prediction models, as described below.) The three models were tested by the confusion matrix and the receiver operating characteristic (ROC) analysis, components which are attached to the “test and score” item.

Fig. 1

Label Classification Model Executed with Orange Version 3.24.0.

The data for the classification process was designed in the following manner: We first transformed variables required in order to adapt them to the desired scale so that they were suited for analysis by various methods that work on discrete categorical values. For this purpose the following steps were implemented:

The impact factor (IF) variable was transformed from a discrete to a categorical value after normalization of the whole discrete value in the following manner. Each IF within each record was normalized to a number between 1 and 100 by applying the STANDARDIZE function in Excel. An IF normalized as < 50 was assigned to a group labeled “Low” IF, whereas an IF of > = 50 was assigned to a “High” IF group. Therefore, for the value of the dependent variable (IF label), we had a binary classification value: Low and High.

The IF label classification was performed using three common binary classification models: 1. logistic regression, 2. random forest, and 3. k-nearest neighbors (k-NN).

Logistic regression: This model is used for binary classification. It operates under the assumption that each variable is independent from the other variables and the data has no missing values.

K-nearest neighbors: This model is used for the estimation of new instance values according to the k closest similar attribute values in the training set. For a “good” prediction, the entire data training set of examples is needed. The method does not make any assumptions about the data distribution for classification purposes.

Random forest: This model is based on crowd wisdom and on the decision tree approach. Here, a large number of decision trees are used as an ensemble, and each uncorrelated tree individually tries to predict the class attribute. The decision tree with the most votes becomes the predictive model. The method is commonly used for classification when the independent variable is labeled as a categorical one. In addition, the method is considered to be a very good technique for analysis where a large proportion of the data is missing.

Model accuracy was evaluated based on the following measures: AUC and ROC. The AUC term (area under the curve) is used to estimate the performance of any given classifier. It ranges from 0.5 to 1, where 1 is considered a perfect prediction and 0.5 a random prediction. The algorithm compares each value to its relative. Values higher than 0.5 indicate an algorithm that is outperformed by the others [57, p.1-2]. The ROC measure (receiver operating characteristic) is also used to measure the accuracy of a statistical model. This measure calculates the ratio between the true positive (sensitivity) and the true negative (specificity) rates. Thus, the ROC plots a probability curve. When AUC equals 0.5, the ROC curve will fall on the diagonal and hence, the classifier will not have significant discriminant value. According to conventional hypothesis testing approaches, H0 defines AUC as equal to 0.5 (AUC = 0.5) whereas H1 defines AUC as not equal to this value (AUC ≠ 0.5) [58].

To categorize the AUC values on an ordinal scale we adopted the scale mentioned above [57] in the following similar way: an AUC score between 0.9 and 1.0 was labeled “excellent”; an AUC score between 0.8 and 0.9 was labeled “very good”; an AUC score of between 0.7 and 0.8 was labeled “good.” An AUC score of between 0.5 and 0.7 was labeled “acceptable”; and an AUC score below 0.5 was tagged “not good.”

4 Results

The correlations, means, and standard deviation values between the research variables are presented in Table 1.

Table 1
Correlation Matrix, Means and SD

Finance Procedure Process Teach ImpactFactor

Finance –

Procedure 0.10^*** –

Process 0.03 0.04 –

Teach –0.07^** –0.06^* 0.07^* –

I.F. 0.03 0.08^ –0.02 –0.09^* –

Mean 1.13 0.72 2.11 1.53 2.61

SD 2.06 1.26 2.25 2.55 1.48

	Finance	Procedure	Process	Teach	ImpactFactor
Finance	–
Procedure	0.10^***	–
Process	0.03	0.04	–
Teach	–0.07^**	–0.06^*	0.07^*	–
I.F.	0.03	0.08^**	–0.02	–0.09^***	–
Mean	1.13	0.72	2.11	1.53	2.61
SD	2.06	1.26	2.25	2.55	1.48

^*p < 0.05, ^***p < 0.01, ^***p < 0.001.

The hypothesized model showed a very good fit: χ2/df = 1.40, p < 0.05, CFI = 0.97, TLI = 0.91, RMSEA = 0.02, SRMR = 0.01. A 90 percent confidence interval (CI) for RMSEA (0; 0.05). Figure 2 illustrates the model and results. All hypotheses were supported.

Fig. 2

SEM and Coefficients. ^*p < 0.05, ^***p < 0.01, ^***p < 0.001.

“Procedure” and “teach’s” multiple mediation effect on the relationship between “finance and “impactfactor” (H1) was supported by the data. A Sobel test for mediation [59] was significant for both procedure (Sobel z = 26.96, p < .001) and teach (Sobel z = 26.96, p < 0.001). The bootstrapped confidence interval (CI) for procedure’s indirect effect ranges from 1.46 to 1.69, and for teach’s indirect effect, 6.01 to 6.95 (bootstrap sample size = 5000). This result indicates that the indirect effect of the mediators is significant, since indirect effects are statistically significant when zero does not exist in their confidence interval range. Procedure positively affects impactfactor (b = 0.08, p < 0.01) (H1a), and teach negatively affects impactfactor (b = –0.05, p < 0.01) (H1b). The covariance between teach and procedure was on the cusp of statistical significance (b = –0.16, p = 0.052), although this has no effect on the hypotheses, and the covariance between teach and process was indeed significant (b = 0.40, p < 0.01) (H1c). Finally, there was no direct relationship between finance and impactfactor (b = 0.01, p > 0.05) (H2).

Data classification using the three models also showed procedure and teach’s multiple mediation effects on the relationship between finance and impactfactor since both k-NN and random forest classifiers assigned a higher classification value to impact factor over finance alone. The findings related to precision and AUC (area under the curve) are as follows: Finance vs impact factor was inspected by k-NN, random forest and logistic regression. Random forest (0.521) resulted in higher precision than logistic regression (0.467). Accordingly, the AUC value was also higher (0.503 vs 0.486) (Table 2). Procedure vs impact factor was inspected by k-NN, random forest and logistic regression. Random forest (0.583) resulted in higher precision than logistic regression (0.568). The AUC value was also higher in accordance with k-NN (0.507 vs 0.502) (Table 3).

Table 2

Area Under the ROC Curve (AUC) and Additional Classification Values: Finance vs Impact Factor

Model	AUC	F1	Precision	Recall
k-NN	0.474	0.487	0.488	0.486
Random forest	0.503	0.446	0.521	0.565
Logistic regression	0.486	0.415	0.467	0.567

Table 3

Area Under the ROC Curve (AUC) and Additional Classification Values: Procedure vs Impact Factor

Model	AUC	F1	Precision	Recall
k-NN	0502	0.460	0.514	0.477
Random forest	0.507	0.459	0.583	0.577
Logistic regression	0.514	0.429	0.568	0.571

Teach vs. impact factor was inspected by k-NN, random forest and logistic regression. The k-NN analysis (0.531) resulted in higher precision than logistic regression (0.324) (Table 4). Both AUC values related to procedure (0.514) and teach (0.539) were higher than the value obtained for finance alone (0.486).

Table 4

Area Under the ROC Curve (AUC) and Additional Classification Values: Teach vs Impact Factor

Model	AUC	F1	Precision	Recall
k-NN	0.505	0.482	0.5 31	0.494
Random forest	0.507	0.413	0.398	0.561
Logistic regression	0.539	0.429	0.324	0.569

Our study results support and elaborate on the knowledge in related studies. With regard to work mentioned earlier [39], this study strengthens the need for knowledge creation in projects such that knowledge is part of the procedures and training methods for employees as reflected in high impact factor journals. Such procedures may influence the success of the project, thus improving its quality and reduce project costs. Therefore, if new frameworks for project management are adopted through learning and training of new procedures, it would contribute to effective delivery by the project employees and managers. In other words, our findings support the claim [39, p.534] that “knowledge management, via knowledge deliverables (as part of the project aim) should be integrated into most of the tools and techniques of project management,” in a way that this knowledge is part of new procedures for project management as reflected in articles published in high impact journals.

Our findings also strengthen recent results [26] by showing that risk in industrial projects can be mitigated through the implementation of new methods for risk identification related to the projects such as training, and control of work regulations and procedures. The current study clearly shows that training and new processes for successful project management can be adopted through practical academic frameworks published in high impact journals, thus mitigating the occurrence of potential risks brought about, in part, by various modes of project management and work models, as mentioned [26, p.524], to be explored in future work. Moreover, our findings, as shown in Fig. 6, demonstrate that leading journals with a high impact factor include articles on practice and theory. This leads to the recommendation for authors to include practical implications in their articles for the field of project management rather than only providing theoretical studies. In addition, journal editors should act to promote articles that hold practical implications for project management, since highly evaluated journals can make a real contribution to promoting projects in practice. For example, in Fig. 6, the “International Journal of Project Management” (IF –3.396 [Medium]) claims to be, “The leading journal for the field of project management and organization studies where its mission is to publish leading edge innovative research that significantly advances the field of project management. . . ” Another example can be seen with the Project Management Journal (IF 8.698 [High]) which claims to be a journal that publishes, “state-of-the-art research, techniques, theories, and applications in project management, where its mission is to shape thinking on the need for and impact of managing projects by publishing cutting-edge research that advances theory and evidence-based practice.”

5 Discussion and conclusions

This study offers empirical proof that higher IF journals publish articles focusing on practical rather than theoretical knowledge, thus closing the gap between academia and industry, a point often criticized by many commercial corporations [7]. The study contributes to existing theory and is beneficial for managers with the following implications. 1) We presented a theoretical model that emphasizes the need for new procedures and training methods related to project management which will enable mitigation of project risks with respect to financial aspects such as reducing costs. We also showed that new procedures which include implementation of best practices through different stages of a project’s life cycle can be found in high impact factor journals. 2) The theoretical model provided is beneficial to managers, emphasizing the need for them to be trained in new procedures and management practices or guidelines so as to mitigate project risks related to financial aspects and to improve the delivery outcomes of the project [26, 60]. In other words, as technology moves forward, the need for methods and guidelines that optimize “practical” project management processes are required in industry (hence to be included in high quality IF journals). In addition, new educational environments for the delivery of these new practical methods for students and practitioners should be developed by academic scholars [61].

Moreover, we demonstrate how classification methods-based machine learning can be used to classify the various methods described in articles about project management published in high impact factor journals. Since the success of any project is dependent on many factors as well as the implementation of new methods during the life cycle of the project, it is very important for managers to know, in the early stages of the project, which type of best practices to utilize along the project life cycle. To this end, we showed that k-NN and random forest algorithms can be used as statistical methods for the selection of specific best practices guidelines from a given repository of many guidelines in different types of projects. Similar to other scholars [62], our findings show that these algorithms outperformed logistic regression (which is perceived as a common and popular prediction model for the problem at hand) in cases where there is an uncertainty with respect to many factors that are part of the project such as budget, human capacity, logistic information, etc. These findings are presented in Figs. 3a (High IF) and 3b (Low IF) in accordance with the AUC values derived by the analysis of the independent variables (finance, procedure and teach) vs. the dependent variable (IF). They provide an aggregate measure of performance across all possible classification thresholds as part of the evaluation of the compared classifiers and the precision values obtained, as appear in Tables 3 –5. Thus, we showed that in cases where there is an increase in the variance of the explanatory and noise variables, it is better to use random forest and k-NN methods for the prediction of the best practices among many others that are published in high impact factor journals. A “good” selection of the “right” practical method will improve the probability of the project’s success as measured, for example, by reducing costs and raising additional external funds for the project.

Fig. 3

a. Receiver Operating Characteristic (ROC) Analysis Related to the Set of Independent Variables as a Function of the Dependent Variable (IF = High). b. Receiver Operating Characteristic (ROC) Analysis Related to the Set of Independent Variables as a Function of the Dependent Variable (IF = Low).

In addition, our findings also support the demand for more trained and educated BA graduates who may serve in managerial positions in the future [63]. Like these authors, we found that there is a need for improved training methods, such as practical knowledge tools during academic studies, especially in technology-rich fields, such as those they mentioned. Indeed, recent years have seen diminishing student enrollment in less technology-oriented fields such as the social sciences [64].

To conclude, as demonstrated in the literature, project management decision-making processes may generate risk for the success or failure of the project. The risk may be an outcome of many factors, some of which reside in improper implementation of project management guidelines and knowledge of project processes, whereas others involve beliefs and preferences [11] as well as knowledge acquired through experience. Our classification algorithms replicated the SEM findings, showing that procedure and teach have a multiple mediation effect on the relationship between finance and impact factor. The algorithm’s findings demonstrate that procedure and teach have a multiple mediation effect on the relationship between finance and impact factor.

Finally, we may thus conclude that the trend of publishing more practical guidelines in the field of project management in high quality IF journals will increase and as a result, improved tools for the best selection of these processes should be utilized. We believe that along with technological progress and online assisted learning, new practical methods based on knowledge and materials will continue to emerge in quality journals available to project managers, and likewise, new classification methods to assist project managers in choosing those best suited to their needs. We also conclude that the need of practitioners in the field of project management to rely on innovation and practical tools and processes to improve productivity and competitive advantage will influence more high impact journals to accept practical papers over theoretical ones.

6 Limitations and future research

One limitation of this study is that its data was not classified into different types of projects, for example, long projects that embody high risk and are more costly than shorter ones. In addition, how project management guideline implementation is conducted in the industry was not evaluated. The study only examined whether the guidelines were maintained and executed by managers according to published studies in this field. It is suggested that in future studies clustering analysis (such as DBSCAN, k-means, or Gaussian mixture model) be used. For example, analyses related to different types of projects should be conducted as well as project time period and affiliated risk. Future studies can also evaluate different types of project management methods (such as Waterfall, Agile, Scrum) and the implementation of policies and additional external and internal attributes that may indirectly affect project success. It is also suggested utilizing ensemble methods that may assist with the prediction accuracy improvement process. Finally, we believe that additional work should be done with more variables related to journals such as clustering of journals according to JCR with respect to Category, Rank in Category and Quartile in Category.

Footnotes

Acknowledgments

The authors have no acknowledgments.

Author contributions

CONCEPTION: Moti Zwilling and Eyal Eckhaus

METHODOLOGY: Moti Zwilling and Eyal Eckhaus

DATA COLLECTION: Moti Zwilling

ANALYSIS OF DATA: Moti Zwilling and Eyal Eckhaus

PREPARATION OF THE MANUSCRIPT: Moti Zwilling and Eyal Eckhaus

REVISION FOR IMPORTANT INTELLECTUAL CONTENT: Moti Zwilling

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Do managers learn more about successful project management methods from articles in high impact factor journals?

Abstract

BACKGROUND:

OBJECTIVE:

METHOD:

RESULTS:

CONCLUSION:

Keywords

1 Introduction

2 Literature review and hypotheses

Table 6 Comparison Analysis of Journals with IF based on Web of Science Ranking

3.2 Analysis

3.3 Text analysis

3.4 Label classification

Table 1 Correlation Matrix, Means and SD Finance Procedure Process Teach ImpactFactor Finance – Procedure 0.10*** – Process 0.03 0.04 – Teach –0.07** –0.06* 0.07* – I.F. 0.03 0.08** –0.02 –0.09*** – Mean 1.13 0.72 2.11 1.53 2.61 SD 2.06 1.26 2.25 2.55 1.48

Footnotes

Acknowledgments

Author contributions

References

Table 6
Comparison Analysis of Journals with IF based on Web of Science Ranking

Table 1
Correlation Matrix, Means and SD

Finance Procedure Process Teach ImpactFactor

Finance –

Procedure 0.10^*** –

Process 0.03 0.04 –

Teach –0.07^** –0.06^* 0.07^* –

I.F. 0.03 0.08^ –0.02 –0.09^* –

Mean 1.13 0.72 2.11 1.53 2.61

SD 2.06 1.26 2.25 2.55 1.48