Scientific Writing Workshop and Academic Write-Ups: Do Analytical Skills and Personal Efforts Matter?

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Niyaz Panakaje, S. M. Riha Parvin, Abhinandan Kulal, Harinakshi S, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4934358/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Scientific writing workshops have evolved as crucial platforms for elevating expertise in academic writing skills, developing confidence and stimulating research pursuits among researchers. In this quantitative study, using structured questionnaire with the sample of 440 researchers who have awarded/registered/enrolled for Master of Philosophy (MPhil), Doctor of Philosophy (PhD), and Post Doctoral Fellow through stratified convenience sampling from Karnataka, India, we have examined the influence of scientific writing workshop on researchers academic writeups by giving significant consideration to their analytical skills and personal efforts. We have found that scientific research workshop remarkably escalates academic write ups when researchers possess strong analytical skills and show high personal efforts. Particularly, it majorly contributes to enhance researchers’ quality publications flowed by assisting in topic selection, journal selection, collaboration, research ethics, research aspirations and content knowledge. Further, we have explored varied involvement of researchers in scientific workshop, analytical skills, personal efforts and academic write-ups with different workshop type, duration, and stages of Ph.D. The study has significant implications for professional growth, scientific writing, teaching, and research practices which implies that researchers can enhance their writing abilities while also developing their critical synthesis and analysis capabilities, resulting in higher calibre and deeper academic work. Scientific Writing Workshop Analytical Skills Academic Write Ups Research Higher Education Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Presently, there is a pressing need to improve the quality of higher education (Ruchina et al. 2015 ), where the predominant role of research is crucial along with teaching and extension (Caingcoy 2020 ). Despite the fact that research, innovation, and extension activities in Indian universities are often regarded as vital indicators of quality, they continue to fall behind in delivering quality research. Indian higher education institutions have a bad global reputation due to a weak research culture, a lack of fluency and scientific writing, and a desire to conduct research in the academic sector (Jha 2023 ). When studying the research culture in the United States, it exemplifies a society that generates and disseminates information. Therefore, it is argued that India can become a global power in the twenty-first century by encouraging scientific research at its universities. Research, innovation, and extension activities account for a 25% of the university's total grade (Joshi & Bhojak, 2021) which includes research publications and awards. To do this, it must guarantee substantial financial support for research, as well as revive a research culture and consistently impart it to the following generation of students (Patel 2016 ; Basilio and Bueno 2019 ). A doctoral degree is essential to academic work. Numerous complementing aspects that contribute to the PhD experience have been found by prior research on doctoral education. Occasionally, the community of practice may fall short in offering PhD students sufficient guidance and shared authority. Students and the learning environment may experience ongoing, harmful friction as a result, which might negatively impact their wellbeing (Pyhältö et al. 2012 ). Understanding the state of the art in higher education research presents difficulties for inexperienced researchers. Similarly, seasoned academics may struggle to stay up to date with advancements in their profession and may get isolated in their area of specialty (Daenekindt and Huisman 2020 ). However, academicians usually participate in the research and development activities for mere presence and acknowledgment and not involving in producing research papers which need to be tackled by examining and addressing each researchers’ capabilities, competencies and research aspiration (Wong 2019 ). It is a skill which needs relentless exercise and can be developed over a period of time(Manongsong and Panopio 2018 ) by actively participating in research workshops, capacity building programmes and training programmes. Previous research has demonstrated that a scientific writing workshop can reduce writing-related stress, anxiety, and frustration by fostering a greater sense of confidence in one's writing abilities(Wortman-Wunder and Wefes 2020a ). When a basic instructional workshop in written scientific communication is conducted by a writing instruction expert, novice researchers' writing self-efficacy and familiarity with the instructional content increase, enabling them to more successfully take on the challenging writing tasks they encounter (Bandura et al. 1999 ; Wortman-Wunder and Wefes 2020a ). Nonetheless, only a few percent of universities have adopted a thorough and systematic strategy to address their students' writing deficiencies (Gardner et al. 2018 ); the majority, on the other hand, have pieced together a patchwork of emergency support and corrections that neither deal with the underlying problems nor offer students ongoing assistance. Without expertise, supervision, and training, research will stay unsatisfactory. With a lack of training, workshops, and technological advancements, the art and ability of writing have lost relevance. Research is considered a constructive activity with the goal of contributing to society and solving its issues. Faculty development programmes and workshops play an important role in improving the skill and confidence of the researchers. Publishing in high quality journals necessitates sound reasoning, proficiency in writing, and expertise in academic achievement as well as research article publication. Majority of the researchers face difficulties in writing research papers. Nevertheless, workshops can help in improving these abilities. A positive relationship exists between workshop efficiency and students research writing skills, and confidence which further stresses that educational institutions should organize trainings and workshops to improve the performance of the students. Workshops and mini projects can motivate and transform the learners to evolve their identity as a researcher with adequate knowledge and skills, which will facilitate them to identify research areas and set research goals (Ramdani et al. 2022 ). Hence, there is a need for institutional initiatives to accelerate researchers’ participation in scientific writing workshop. Prior research has examined writing retreats, the formation of writing groups, confidence in writing skills (McGrail et al. 2006 ; Cameron et al. 2009 ; Cable et al. 2013 ; Wortman-Wunder and Wefes 2020a ), but none that we are aware of has examined the influence of scientific writing workshop on researchers academic writeups by giving significant consideration to analytical skills and personal efforts. This research delves into understanding the impact of scientific writing workshop by considering all the domains and attempt to assess its contribution on each component of academic writeups including publication, topic selection, journal selection, research aspiration etc. It provides the comprehensive understanding of scientific workshop by assessing researchers’ analytical skills as well as personal efforts in improving researchers academic writeups. The manuscript initially delves into understanding numerous theories and literatures available in the domain of scientific writing workshop further explains the development of hypothesis. After describing the methodological approach, study provides results and discussion and concludes with realistic implication to enhance the quality of research in higher education. REVIEW OF LITERATURE Academic writing is a core component in imparting research findings, enriching knowledge and enhancing a researcher’s academic presence. However, researchers face problems in impactful conveyance of their ideas. To apprise these problems, scientific writing workshops contribute substantially as crucial mechanisms by providing guidance and content for skill advancement. The emphasis of this review is to comprehend the literature encompassing the role of scientific writing workshops in enhancing the confidence and potential of researchers in research writing. Scientific Workshops and Academic Writing Academic writing is of significant importance in all streams as it is a crucial skill for researchers, but a large number encounter setbacks in obtaining proficiency in this skill. Research studies have shown the resourcefulness of structured workshops in intensifying the assertiveness and intellectual capacity of pre- and postdoctoral researchers, which helps them in capacitating their potential in facing the challenges they confront in their research careers (Wortman-Wunder and Wefes 2020b ).The workshops which includes a structured format, which offer participants with all the required skills for research. In Kenya, participatory workshops that adopt enculturation and operational methods have depicted the ability in developing skills for authorship in health professionals pursuing their master’s programme (Onguka et al. 2023 ). Involvement in interactive exercises and engaging them in acquiring in-depth knowledge in their respective subjects, help in facilitating identity in writing skills of the researchers, which is crucial for academic excellence. Additionally, studies have investigated how self-assessment of teaching writing influences the proficiency in writing and academic achievement at the higher education level in science courses (Navarro et al. 2022 ). Integrating pedagogical strategies with self-assessment not only improves the quality of writing but also enhances academic achievement. This method highlights the significance of metacognition and self- appraisal in the process of writing. Consequently, research has documented the advantages of intensive writing engagements, such as those provided in writing programmes for research focused doctoral candidates, from particularly international backgrounds (Mckenna and Kyser 2022a ). These engagements not only improve proficiency in writing but also give a deep understanding of the need to seek help at the right time. Moreover, these workshops play a crucial role in the enhancement of effective pedagogy aligned to cater the needs of varied needs of student population. Furthermore, it has been recommended that English teaching should focus on enhancing academic communication and interaction with academic literature along with concentration on proficiency in writing (Mckenna and Kyser 2022b ). By enhancing students’ ability to effectively communicate and comprehend in English in their area of expertise, these workshops significantly contribute towards their complete academic competence and their capability for international professional engagement. Effectiveness of Scientific Workshops Previous studies have shown the impact of the competencies of the resource persons, quality and organisation of the workshop and content relevance of the scientific workshops on academic writing, which are discussed as follows: Competencies of Resource Persons The effectiveness of the workshop is considerably influenced by the proficiency and competence of the resource persons of the workshop. Agésilas ( 2002 ) highlight those enhanced results of the workshop such as involvement of participants, retention of knowledge and acquisition of skill are evident if workshops are handled by academicians of expertise. Mastered facilitators can deliver detailed writing skills, provide productive feedback, and efficiently cater to the individual needs of the participants (Hapinat, 2023). Furthermore, their expertise and recognition among the academic clan strengthen the appraised worth of the workshop, encouraging participants to positively participate and implement the newfound knowledge (Syafrina and Khotimah 2022 ). Quality and Organization of Workshop The effectiveness of scientific writing workshops in increasing participants learning outcomes and engagement have been documented in various studies. (Ka..smann et al. 2023) showcased the effect of interactive and online course on student experience with scientific studies in radiation oncology, directing towards the enhancement of reflective reasoning and technical knowledge. Similarly, Fatmawati et al. ( 2023 ) highlighted the role of workshops in augmenting knowledge and capability in proposal preparation, thus enhancing the scientific writing practice. Oktavia Liza and Zudeta ( 2023 ) emphasised how training in scientific writing enhances the professional career of teachers through the publication of research papers. Kurniawan et al. ( 2023 ) substantiated the importance of workshops for teachers aimed at scientific writing techniques led to improved proficiency and understanding in scientific paper writing. Muhammad Sirozi et al. ( 2023 ) further upholds the conception that workshops are of predominant significance in advancing the students’ ability in structuring their thesis as well as improving their research design scientifically. These outcomes together highlight the significance of structured workshops in terms of pertinent content, involvement, peer appraisal, appropriate support, and accessibility to the required resources in advancing the grasping and skill enhancement experience in scientific writing. Content Relevance The suitability and applicability of the workshop’s content are vital for obtaining participants concentration and capacitating continuous learning. Goyal et al. ( 2020 ) emphasises the need for orienting the workshops content with the expectations of the participants. Application of various teaching strategies such as exchange of ideas in groups, writing trials, and command help in meeting the varied learning expectations and methods. Additionally, workshops that include practicality and case studies offer enhanced clarity on the ability of participants to understand the subject content, instilling independency in their writing ability. Lastly, a multifaceted approach to workshops related to academic writing, combining systematic formats, engaging components, strategies for self-evaluation, and regional belongingness, is indispensable to advance the efficacy of academic writing skills among various student cohorts. By considering the relevant need and challenges faced by researchers, these workshops are vital in preparing researchers for successful pursuits in research and academics. Hence, Hypothesis H1 has been developed. Hypothesis 1 Scientific workshops significantly enhance academic write ups among the researchers. Essentiality of analytical skills and personal efforts of the researcher in academic write ups The outcome of enriched analytical skills and exertion shown by the researcher is prominent in academic writing techniques (Rallón and Peñaloza-Rallón 2021 ). The ability to strategically analyse the content and make knowledge-based conclusions is considered as a significant aspect in the advancement of academic writing (Craig et al., 2020 ). Additionally, independent potential by the researcher with regard to dealing with issues, learning capability, and academic involvement with other research stakeholders, significantly converts the ability of the researcher, which impacts their skills of academic writing (Nygaard 2017 ). Therefore, a coalescence of analytical skills and individual commitment can positively enhance the potentiality of the academic writing skills among the researchers. Hence, it can be hypothesised that Hypothesis 2 Analytical skills and personal efforts of the researchers significantly mediates the relationship between scientific workshops and academic write ups. Role of workshop type, duration, domain knowledge and Ph.D. progression on academic write ups The efficiency of the academic writing excellence in researchers is not only dependent on scientific writing workshops but is impacted by various other factors(Permana et al. 2023 ). These workshops are organised in varied types such as systematised, interactive, self-evaluation and enculturation frameworks, specifically benefitting the academic writing skills (Yeboah et al. 2022 ). Consequently, the duration of the workshops have a widespread influence on the intensity of learning and skill retention both in the form of short – term concentrated programs or long – term module courses (Celik and Kapukaya 2022 ). Participants of scientific workshops with extensive domain knowledge portray superior proficiency in writing (Evans and Bart 1995 ). Furthermore, the reach of relevant domain knowledge substantially impacts the precision of written elucidations, emphasising its relevance in comprehension (Darabi Bazvand 2019 ) Additionally, the academic writing skills of the researchers depends on the varied needs and stage of Ph.D of the researchers (McAlpine and Boz 2023 ). The basic writing techniques and research proposal enhancement training are beneficial for novice researchers (Gimenez et al. 2024 ). Data analysis and manuscript preparation is very essential for mid-stage researchers (Meuleners et al. 2023 ). Thesis writing and publication writing techniques significantly impact the performance of last-stage researchers (Rafi and Moghees 2023 ). Hence, understanding the distinct perspectives and requirements of researchers are of predominant importance for organising structured scientific writing workshops that address the multifaceted needs in various streams and stages of Ph.D. advancement. Hence, the hypothesis H3 has been developed. Hypothesis 3 The researcher with different gender, age, disciplines, workshop type, duration, domain and stages of Ph.D. have depicted varied involvement in scientific workshop, analytical skill, personal efforts and academic write-ups. Impact on Academic Write-ups Scientific writing workshops have a deep-rooted impact on numerous facets of academic write- ups. Lavinghouze et al. ( 2022 ) found that learners in well-structured workshops possibly produce standard manuscripts, undergo the process of publication efficiently, and exhibit expertise in writing for multiple needs. Additionally, workshops aid in multidisciplinary collaboration and networking platforms, widening participants’ research potential, thereby enhancing exchange of knowledge and peer support. Scientific writing workshops serve as invaluable platforms for enhancing scholars’ confidence and proficiency in academic writing. Previous studies have highlighted the various aspects involved in assessing the impact of academic write ups. However, there is a need to understand the interconnected mechanisms through which these widespread factors influence academic write ups. Further, research is warranted to justify the exploration of the interaction and influence of variables such as content relevance, competency of resource person and organisation of scientific workshops on academic writing as well as understanding the mediating role of academic skills and personal efforts of the researcher on the academic write ups. By focusing on facilitator competencies, workshop quality, and content relevance, these interventions empower participants to navigate scholarly communication effectively. THEORETICAL BACKGROUND OF THE STUDY The Kirkpatrick’s Evaluation Model This Kirkpatrick’s evaluation model was proposed by Dr. Don Kirkpatrick in the year of 1950. Basically, this Kirkpatrick’s evaluation model helps to evaluate the results of training and learning programs by considering both formal and informal way (Streitwieser et al. 2010 ). This approach, which comprises four stages starting with ‘Reaction’ consists of the participants’ satisfaction with the resource individuals, workshop quality, workshop materials and presentations (Abdulghani et al. 2014 ). These remarks serve as a quick indicator of participants’ initial impressions of the workshop (Rajeev et al. 2009 ). The next stage is ‘Learning’ observed improvement in knowledge of content delivered in the workshop addressed both theoretical and practical aspects in the research field and abilities of the participants (Ulum 2015 ). ‘Behaviour’ is the third stage, where the application of acquired skills and knowledge by the participants were focused. The final stage is ‘Results’ which shows the impact of workshops to evaluate the feasibility of the program (Smidt et al. 2009 ). This is frequently used to assess how well training courses, seminars, and other educational initiatives function. Experiential Learning Theory This concept was published in year 1984 by an American psychologist, educator, and education theorist David Kolb, (Kolb and Kolb 2005 ). According to this, learning comprises’ concrete learning’ or tangible experience, which occurs when a participant gets fresh experience, experiences curiosity, or represents an initial experience in research (Kolb et al. 2014 ). The ‘reflective observation’ or reflection of the participant considers the fresh experience to comprehend what it means, like trusting information through statistical evidence (Redmond et al. 2022 ). The ‘Abstract Conceptualization’ will help the participants’ improve their critical thinking ability and decision-making based on facts and figures (Chang et al. 2020 ). Finally, the ‘active experimentation’ reflects the practical application of information or knowledge attained via workshops (Olanipekun et al. 2020 ). According to this, learning is most successful when people participate in all four phases of the learning cycle. It shows the importance of participating in the research activities through these stages fostering the growth of analytical abilities, critical thinking, and a more in-depth knowledge of their subject matter by the researchers. Self-Determination Theory (SDT) This theory was developed by Deci and Ryan, contends that when people’s basic psychological needs for autonomy, competence, and relatedness are satisfied, they are driven by internal causes (Ryan and Deci 2017 ). According to this theory, the notion of ‘autonomy’ comprises the various publications in research and identification of topics for research (Orsini et al. 2015 ), ‘competence’ comprises an understanding of proficiency and efficiency through the quality of content (Reeve 2012 ), and connectivity which will increase the collaboration with other researchers and the topic preference viewpoint of the contributors are crucial in developing and maintaining personal enhancement and improvement (Chiu 2023 ). This theory will be used to understand the in-depth impact workshops have on the deep-rooted persuasion and individual development of the contributors. The concept provides a concrete conceptual structure for detailing the reason and the manner in which contributors can enhance their research and academic achievements when their basic psychological needs are met. Hence, this study focuses on understanding the extent of influence of scientific workshops on academic writing considering the various factors influencing the outcomes of academic writing as shown in the conceptual model (See Fig. 1). METHODS Research Design Present study was carried out using quantitative approach from collecting the data till analysing it. However, qualitative information was utilized to derive at the hypothesis and understand the existing state of studies context. With this multiple approach, objective of assessing the effectiveness of scientific workshop on academic write-ups through researchers’ personal efforts and analytical skills was fulfilled. Data Collection and Sampling Present research targeted researchers who have awarded/registered/enrolled for Master of Philosophy (MPhil), Doctor of Philosophy (Ph.D.), and Post-Doctoral Fellow in the region of Karnataka, India. The inclusion criteria for selecting the sample satisfied the following conditions: Only those full-time and part-time researchers from various fields who have attended at least two scientific workshops in the last six months, with a minimum of one year of Ph.D or Masters experience in research. Survey questionnaire was formulated to obtain researchers participation in scientific workshop and their view on academic write-ups along with measuring their analytical skills and personal efforts. The Google form was circulated through varied online platforms such as WhatsApp, Gmail, Instagram, Facebook and through personal visits. Also, few responses were collected in the google form. As per All India Survey on Higher Education 2019-20, there are 14988 regular mode Ph.D. scholars, 528 M.Phil scholars and 13444 distance mode Ph.D. scholar and 528 M.Phil scholars in Karnataka. However, the exact number of doctoral fellows was not found in the report. Considering the available information sample size was calculated which was rounded up to 395. Overall, we have collected 440 responses through stratified convenience sampling, where we firstly divided the population into varied strata based on the types of universities and then based on convenience, researchers were selected who fulfilled the above criteria to fill up the form. Sample Profile The sample profile presents a diverse demographic distribution (See Table 1 ), with a slight majority of females (55.5%) over males (44.5%). Age distribution is concentrated predominantly between 21 to 40 years, with the majority falling within the 31 to 40 age bracket (49.0%). Regarding designation, the majority are faculty members (69.0%), followed by full time research scholars (23.9%). In terms of discipline, Management and Commerce holds the highest representation (70.1%), while Science and Arts follow at 13.8% and 15.8%, respectively. The data suggests a predominantly young and academic-oriented demographic with a strong emphasis on Management and Commerce within the sample population. Table 1 Sample Profile Variable Categories Percentage Gender Male 44.5 Female 55.5 Age 21 to 30 40.3 31 to 40 49.0 41 to 50 8.7 51 to 60 2.0 Designation Faculty 69.0 Industrialist 2.0 Full-time Research Scholar 23.9 Other (Please Specify) 5.1 Discipline Management and Commerce 70.1 Science 13.8 Arts 15.8 Other .3 Source : Survey data Research Instrument The measurement of scientific research workshops is diligently designed using a comprehensive framework which comprises of various constructs and corresponding items. These are strained from the prevailing literatures in the area of the study, insights from focus group interviews, observations and the researchers’ field expertise. The questionnaire was systematically divided into multiple sections to ensure a detailed and sophisticated data collection. The initial part of the questionnaire intended to determine the personal and research profile of the respondents. This consists of age, gender, designation, number of year(s) of experience in teaching, discipline, time reserve for research activities, workshops attended, year of pursuing Ph.D., and so on. The latter part of the questionnaire analyses the impact of scientific research workshops on enhancing academic write-ups, which is measured by considering various variables. Within the scope of the study, scientific research workshop is considered as independent variable, academic write ups as dependent variable and analytical skills as a mediator. Measurement Scale A total of 52 assessment criteria were utilised to evaluate the scientific workshops, academic write ups and analytical skills. The investigation includes 20 items to measure scientific workshops, 30 items for evaluating academic write ups and 12 items to measure analytical skills. The main objective of the study is to investigate the impact of attending scientific workshops in improving academic write ups among the academic scholars. Five-point Likert scale was employed to elicit the research scholars’ perception regarding the subject matter of the study. The scale spans from 1 (strongly disagree) to 5 (strongly agree). This methodology enabled a thorough investigation of the participants attitude and beliefs regarding the topic, offering a deeper comprehension of the influence of scientific workshops attendance on improving academic write up skills among research scholars. Construct Development In the present study, intensive review of past research in the area led to the identification of three key variables. The independent variable under investigation is the scientific research workshop which is measured using various dimensions such as Competencies of the Resource Person, Quality and Organization of Workshop and Content Relevance. These dimensions are derived from research published by Goyal et al. ( 2020 ), Gaber and Ali ( 2022 ) and Jusslin and Widlund ( 2024 ). The study proposes analytical skills and personal efforts as a mediator, with the evaluation parameters driven from studies of Knowles and McGloin ( 2007 ), Al-Hasnawi and Muslem Al-Mousawi ( 2021 ), Cullen et al. ( 2018 ), Mukti et al. ( 2020 ), Politsinsky et al. ( 2015 ) and Theabthueng et al. ( 2022 ). The dependent variable under analysis is academic writeups which is assessed using parameters such as publications, journal selection, topic selection, collaboration, research ethics, content knowledge and research aspiration which is obtained from works by Goyal et al. ( 2020 ), Gaber and Ali ( 2022 ), Li et al. ( 2018 ), Jusslin and Widlund ( 2024 ), Mydin et al. ( 2021 ) and Knowles and McGloin ( 2007 ). Ethical Consideration The questionnaire is approved in the 3rd meeting of the Scientific Research Board, which scrutinized and deliberated on the application submitted through letter no. SRB/2024/05 of YIASCM, Yenepoya, Mangaluru (Deemed to be University), under the said policies and procedures for research work of the said university, on 09th March 2024. Oral consent was obtained from every respondent prior to the administration of the questionnaire. The respondents’ voluntary participation was encouraged, and any risks or discomfort were kept to a minimum. Furthermore, there were no conflicts of interest or prejudices because the study was conducted impartially, transparently, and honestly, with the participants’ rights and dignity in mind. Data analysis and procedure Following the data collection, the data was fed into SPSS 26 which was then coded, checked and verified for errors. Moreover, the software Smart PLS 4 was used in this work to test the conceptual models (Ringle et al. 2005 ). Partial least square structural equation modeling was used to examine quantitative data, which is predominantly used in the business and social science studies (Hair et al., 2014 ). Firstly, we tested the measurement model to confirm the constructs and the indicators used to measure the respective constructs with acceptable factor loading, reliability and construct validity. Here, the measurement model was confirmed with Cronbach Alpha and composite reliability test to check its consistency and discriminant and convergent validity were used to test the accuracy of the data. Lastly, structural equation modelling was run in PLS SEM and hypothetical relationships were tested using path coefficients. Descriptive Statistics The data indicates a varied distribution of years of experience in the research field among respondents, with the majority (36.1%) having 1 to 3 years of experience, followed closely by those with 3 to 5 years (33.8%). Notably, a significant proportion (14.1%) have less than 1 year of experience, suggesting a considerable influx of newcomers into the research field. Regarding the average time spent on research-related activities per week, a substantial portion (47.3%) dedicate 10–30% of their time, while 34.6% allocate 30–50% of their time. This distribution suggests a considerable commitment to research endeavours among respondents, with a notable proportion dedicating a significant portion of their weekly schedule to research activities. The data presents the duration and frequency of workshops attended by participants. Across all duration categories (1 to 2 days, 3 to 5 days, 5 to 7 days, and more than 7 days), the most common response for the frequency of attendance is "1–3 times", indicating a consistent level of engagement for participants across different workshop durations. Notably, for workshops lasting 5 to 7 days or more than 7 days, a considerable proportion of respondents never attended such workshops, suggesting that longer-duration workshops might pose scheduling or accessibility challenges for some participants. Additionally, the frequency of attendance tends to decrease as the duration of the workshops increases, with fewer respondents attending workshops more than 9 times across all duration categories. This suggests that while participants are engaged in workshops, they may prioritize shorter-duration events or face limitations in their availability for frequent attendance, particularly for longer workshops. The data illustrates the distribution of participants across different stages of their Ph.D. programs. The majority of respondents are in their early years of the Ph.D. journey, with 35.7% in their 1st year and 24.5% in their 2nd year. As expected, the percentage of participants decreases as the years progress, with 18.4% in their 3rd year, 9.5% in their 4th year, and 5.7% in their 5th year. Notably, a small portion (6.1%) of participants have been pursuing their Ph.D. for 5 years or more, indicating a longer duration of study for some individuals. This distribution suggests a diverse representation of participants at various stages of their doctoral research, with the majority being in the initial years of their programs. The descriptive statistics presented in Table 2 provide insights into participants’ perceptions across various dimensions of the evaluated workshops or programs. Overall, the mean scores for competencies of the resource person, quality and organization of the workshop, content relevance, and the scientific research workshop as a whole indicate a consistently high level of satisfaction, with mean scores ranging from 4.2991 to 4.3577 out of 5. Similarly, participants rated aspects related to academic writeups, including publications, journal selection, topic selection, collaboration, research ethics, content knowledge, and research aspiration, positively, with mean scores ranging from 4.2028 to 4.3577 out of 5. Furthermore, participants perceived high levels of proficiency and personal effort in analytical skills, with a mean score of 4.3315 out of 5. These findings suggest a strong overall positive evaluation of the workshops or programs, indicating effectiveness in various dimensions such as content delivery, workshop organization, academic engagement, and personal skill development. Moreover, the descriptive statistics for each dimension with respective items of scientific writing workshops (Table S1 ), analytical skills and personal efforts (Table S2) and academic writeups (Table S3) are presented in appendix by indicating its individual Mean, Std. Deviation and Std. Error Mean. Table 2 Descriptive Statistics Number of Items Minimum Maximum Mean Std. Deviation Competencies of the Resource Person 7 2.14 5.00 4.3525 .43195 Quality and Organization of Workshop 7 2.50 5.00 4.2991 .44662 Content Relevance 6 3.00 5.00 4.3215 .42673 Scientific Research Workshop (Overall) 2.79 5.00 4.3244 .39403 Publications 5 2.00 5.00 4.2028 .54149 Journal Selection 5 3.00 5.00 4.2428 .45915 Topic Selection 5 2.60 5.00 4.2434 .42736 Collaboration 3 1.00 5.00 4.1474 .62676 Research Ethics 4 3.00 5.00 4.3577 .43200 Content Knowledge 4 2.50 5.00 4.3204 .43134 Research Aspiration 4 2.50 5.00 4.3204 .43134 Academic Writeups (Overall) 3.00 5.00 4.2621 .38536 Analytical Skills and Personal Efforts 12 3.00 5.00 4.3315 .39605 Source : Primary source computed using AMOS Inferential Analysis At the first stage, we examined the rationality and the veracity of our instrument with the help of the Smart-PLS software, thereby improving the quality of our tool. First, it was represented in terms of Structural Equation Modeling (SEM) assumptions. PLS path modeling, as mentioned by Gudergan et al. ( 2008 ), is a good technique, which is often used in putting forward the cause-and-effect relationships of management research (Gudergan et al. 2008 ). Compared to the CB-SEM (Covariance-Based SEM), which Rigdon ( 2012 ) speaks, the PLS-SEM preserves the construct validity that embraces the indicator variables based on theory and improves the predictive accuracy. Specifically, the PLS-SEM technique helps in the modeling of complex models that comprise of multiple constructs, indicators and relationships, according to F. Hair Jr et al. ( 2014 ). It is beneficial for both development of elaborate models in the early stage theory development and testing as well as for the examination of constructs in the context of models. The efficiency makes theoretical programs more relevant for practical recommendations. This coincides with the current stage of development of the Structural Paraphrase Mapping (SPM) theory, where what is to be discovered are the relationships, their directions, unmistakable measures, and scales. As PLS-SEM suits the case herein, therefore; it is appropriate in our circumstances. In CB-SEM, larger samples are needed because all the variables are simultaneously evaluated while in PLS-SEM, the model is broken down into multi-blocks, so they are smaller. Unlike conventional methods that demands big sample, PLS-SEM is effective with small sample size and complex model, without making any assumptions about the data distributions (F. Hair Jr et al. 2014 ). With regard to PLS-SEM sample size, according F. Hair Jr et al. ( 2014 ), general rule is at least 10 as number of directed arrows (F. Hair Jr et al. 2014 ). Besides, CB-SEM needs a sample number that is not less than 5 times the number of indicators in the input model. This property makes PLS-SEM especially apt for our present research. This paper incorporates a reflective argument on measurement, which is in line with scale development and what business commonly uses. According to this approach, there is an assumption that the tie between indicators and constructs is reflective. This implies that a latent construct has its autonomy and it is not linked by any particular measures, but they all carry some common semantic content. The same argument validates that excluding some dimensions from the construct will not change the conceptual meaning and purpose of the sense it is made to convey. Besides, it’s believed that probably, the items have a high positive interrelation because they are assessing one and the same construct. The monitors applied in this study are based on the instruments of the past studies. These constructs are considered a kind of reflective phenomenon that is in line with the modelling approach for project success, stakeholder engagement, and team building. Meanwhile, SPM as a tactic is known to be represented in most existing literature as being reflective. The paper is narrative, which is a popular practice in business and unit evaluation application theory (Coltman et al. 2008 ). This approach assumes that this relationship between the reflection of construct and its indicators is reflective, which consequently means that underlying construct exists independently of the measurement methods being used and items are all themed around something common (Coltman et al. 2008 ). Hence, abandoning some aspects of the scale would not change the underlying content associated with the phenomenon. More so, the positively high inter-correlation assumption is strongly found, since they are used to measure the same underlying aspect (Diamantopoulos and Siguaw 2006 ). In this study, the scales are applied following the validation in the earlier research in the area of engagement of stakeholders (Jang et al. 2017 ), team building (Aga et al. 2016 ), and project success. The latent variables are identified as reflective constructs that are characteristic of these areas. Moreover, the literature is not disconnected from the Strategic Project Management measure and looks to it as an illustrative (Carvalho and Rabechini Jr 2017 ). RESULTS Here is a section which presents in-depth insight to the data that we received from the survey questionnaire. The objective is to ensure consistency of the research model, addressing the research questions correctly, and evaluating the hypotheses efficiently thus making possible the derived reliable conclusions. Several analytical approaches are used to look into and fully understand the existing information. This is done to make sure that everything tallies with the given research aims and objectives. Through the use of a data-driven thorough evaluation and interpretation of the survey data, the main objective of this section is to provide a valid base for the foundation of research outcomes and their impact. Individual Variable Contribution Analysis The Table 3 presents an individual contribution analysis, delineating how each predictor variable impacts the model constructs. In Model 1, Content Relevance (β = 0.497, Sig.=0.000), Competencies of the Resource Person (β = 0.144, Sig.=0.035), and Quality and Organization of Workshop (β = 0.216, Sig.=0.001) all significantly contribute to the variance in the dependent variable. Similarly, in Model 2, the Scientific Research Workshop (β = 0.632, Sig.=0.000) emerges as a substantial predictor of the outcome. Model 3 highlights the crucial role of Analytical Skills and Personal Efforts (β = 0.761, Sig.=0.000) in driving the dependent variable. Moreover, the Scientific Research Workshop consistently demonstrates significant influence across Models 4 to 9, indicating its importance in various contexts. Nonetheless, in Model 10, the Scientific Research Workshop reasserts its significance (β = 0.453, Sig.=0.000), emphasizing its variable nature across different analyses. As a whole, the individual contribution of scientific workshop indicates that among the 7 components of academic write ups, scientific workshop is significantly vital to enhance the publications among the researchers (β = 0.634, Sig.=0.000). Moreover, this workshop is significantly helpful in Topic Selection (β = 0.616, Sig.=0.000), Journal Selection (β = 0.526, Sig.=0.000), Collaboration (β = 0.512, Sig.=0.000), Research Ethics (β = 0.462, Sig.=0.000), Research Aspiration (β = 0.453, Sig.=0.000) and Content Knowledge (β = 0.422, Sig.=0.000). Overall, these findings underscore the diverse contributions of individual predictor variables in shaping the respective constructs, highlighting areas of focus for enhancing model outcomes. Table 3 Individual Variable Contribution Analysis Model Relationship β t-value Sig. R. R 2 F Inference Model 1 CR →AW .144 2.492 .035 .669 .447 117.590 Accepted CRP → AW .216 3.477 .001 QOW → AW .497 7.842 .000 Model 2 SRW → AW .632 17.081 .000 .632 .400 291.759 Accepted Model 3 ASPE→ AW .761 24.564 .000 .761 .579 603.383 Accepted Model 4 SRW→ PUB .634 17.173 .000 .634 .402 294.905 Accepted Model 5 SRW → JS .526 12.937 .000 .526 .276 167.353 Accepted Model 6 SRW → TS .616 16.366 .000 .616 .379 267.841 Accepted Model 7 SRW → COL .462 10.887 .000 .462 .213 118.533 Accepted Model 8 SRW → RE .512 12.462 .000 .512 .262 155.307 Accepted Model 9 SRW → CK .556 13.995 .000 .556 .309 195.867 Accepted Model 10 SRW → RA .455 9.327 .000 .455 .207 109.543 Accepted Source : Primary source computed using AMOS Measurement Model The validity and reliability of the constructs were evaluated in the study in order to evaluate the measuring model. Confirmatory Factor analysis was used for the purpose of evaluation of measurement model. In the first stage, first order constructs of independent variable (Competencies of resource person, quality of workshop and content relevance) are evaluated with 21 items. Then, we checked factor loadings of each construct, where all items loaded greater than 0.5 except CRP1 loaded with 0.486 (See Figure S1 ). As per the suggestion of the J. F. Hair et al. ( 2019 a), the factor loading less than 0.5 is not good factor loading. Therefore, we removed CRP1 from further analysis. Further, CR1, CRP2, CRP3, QOW1, and QOW2 were affecting the discriminant validity of the constructs, therefore researchers had to remove the items from the model. Then, we again run confirmatory factor analysis, and result showed that the factor loadings were greater than 0.5 and even AVE (Average Variance Extracted) is greater than 0.5 as suggested by (Wong 2013 ). The outcomes of the model diagnostics were shown. Estimated model fits the data really well as it is supported by its relatively low chi square value (148.106) relative to its df (87) leading to a p-value that cannot be overestimated (0.000). Moreover, RMSEA (0.040), GFI (0.957), AGFI (0.940), PGFI (0.694), SRMR (0.030), NFI (0.955), TLI (0.977), and CFI (0.981), which are characteristics of suitable model fit, are all established through the AIC (214.106) and BIC (348.969) which are the additional predictions, confirming the model to be fit adequately. Thus, it can be regarded that the model estimations have shown to be strong and comprehensive, which have the capacity to represent the underlying relationships between variables appropriately. In the second stage, first order constructs of dependent variable (PU, JS, TS, CL, RE, CK and RA) were evaluated with help of confirmatory factor analysis. The result showed that the factor loadings of all constructs are above 0.5 and there is no problem with discriminant validity. Therefore, researcher kept all the items of second order constructs for further analysis. The result depicted in figure (See Figure S2). The model fit indices suggest that the picture about adequacy and the ability of the model to explain the observed data are in the range of not good. Some of the indices, such as the RMSEA, SRMR, NFI, TLI and CFI introduce a satisfactory fit, with many of the values either meeting or approaching determining thresholds (Kline 2015 ), yet other indices like the chi-square test, GFI, AGFI and PGFI show areas for further development. The model explains 39% and 41% of variances of the dependent variables. The significant chi-square statistic and low goodness of fit indices support that the model does not match completely with the actual data, possibly because of modelling discrepancies and extra noise (Schumacker and Lomax 2004 ). Besides, the PGFI statistic indicates that the model may be oversized, which is connected with considering too much complicated model for the amount of data that is available (Wheaton et al. 1977 ). However, the model still gives only a partial picture, so more work and rechecking may be needed before the suggested patterns to data are properly concluded. In the third stage, we evaluated first order of mediating variable (Analytical Skills and Personal efforts). The result showed that all the items of the construct are above 0.5 of factor loadings. The model fit also showed satisfactory. Therefore, we kept all the items for further analysis (see Figure S3). Finally, second order construct was measured using CFA (Confirmatory Factor Analysis) and result is depicted in Fig. 2. The result indicating that the items derived from the first order analysis were further kept and model fit indices are satisfactory. Table 4 Construct Reliability and Validity Variable Cronbach’s alpha (standardized) Cronbach’s alpha (unstandardized) Composite reliability Average variance extracted (AVE) CRP 0.814 0.813 0.814 0.522 CR 0.871 0.871 0.872 0.531 QOW 0.833 0.834 0.837 0.506 CK 0.814 0.814 0.810 0.516 CL 0.836 0.836 0.837 0.631 JS 0.877 0.876 0.876 0.587 PU 0.889 0.888 0.888 0.614 RA 0.815 0.814 0.815 0.528 RE 0.842 0.842 0.842 0.572 TS 0.856 0.856 0.857 0.544 Source : Primary source computed using AMOS Note : CRP = Competencies of the Resource Person; CO = Quality and Organization of Workshop; CR = Content Relevance; AW = Academic Writeups; ASPE = Analytical Skills and Personal Efforts; SRW = Scientific Research Workshop; PUB = Publications; JS = Journal Selection; TS = Topic Selection; COL = Collaboration; RE = Research Ethics; RA = Research Aspiration Table 4 Depicts how reliable and valid the constructs turned out. The results of the analysis of Cronbach’s alpha statistics are shown both standardized and unstandardized and all constructs possess high internal consistency reliability. The values of the Cronbach’s alpha range from 0.814 to 0.889 (George and Mallery 2018 ). The computed coefficients of composite reliability (rho_c) additionally convey a high level of inter-item consistency as they range from 0.810 to 0.888 exceeding a threshold of 0.70, Hair (2010). Also, AVE values show that variables are unidimensional, with 0.506–0.631 values exceeding the bench mark of 0.50 (Fornell and Larcker 1981 ). Hence, convergent validity has been attained. The results imply that the tools employed were good measures for the traits being examined. Therefore, analysts and interpreters can be certain that they have a stable basis for more complex analyses and interpretations. Concurrent validity was evaluated using the Close-to-Perfect criterion (Fornell and Larcker 1981 ) as well as the Heterotrait-Monotrait Ratio method (HTMT). In applying Fornell Larcker’s criterion, the Average Variance Extracted (AVE) of each factor gotten is to be measured against the cross-loadings between each factor and other factors. Also, according to Henseler et al. ( 2015 ) by theme value of HTMT less than 0.9 can be stated as confirmed discriminant validity. Arguably, we have reconfirmed that all HMTV ratios were noticed beneath 0.90, as well, in conformity with the discriminant validity principles. After that, we assessed an overall MoF value over the recommended threshold of 0.617, which was defined as the recommendation of the article of (Wetzels et al. 2009) as a benchmark for the model to fit with the given complexity. Thus, according to this evaluation, the study of the model motivated us to think of the adequate structure. Structural Model The next step is to analyse the hypothesized model to test developed hypotheses. In the first stage, we have analysed the direct relationship. The result of direct relationship is tabulated in Table 5 and depicted in Fig. 3. The Table 5 presents the direct relationships between Analytical Skills and Personal Efforts, Scientific Research Workshops, and Academic Writeups, as indicated by their path coefficients. Analytical Skills and Personal Efforts exhibit a strong positive relationship with Academic Writeups (path coefficient = 0.658), suggesting that individuals with enhanced analytical skills and who invest considerable personal efforts tend to produce better academic writeups. Additionally, Scientific Research Workshops positively influence Academic Writeups (path coefficient = 0.162), indicating that participation in such workshops contributes, albeit to a lesser extent, to the quality of academic writeups. Hence, H1 holds true. Moreover, the strongest relationship is observed between Scientific Research Workshops and Analytical Skills and Personal Efforts (path coefficient = 0.782), implying that these workshops significantly enhance individuals’ analytical skills and personal efforts, potentially through the acquisition of knowledge and practical research experience. Overall, these findings underscore the critical role of both individual efforts and external training programs, such as scientific research workshops, in shaping the quality of academic writeups, thereby emphasizing the importance of fostering analytical skills and facilitating participation in research-oriented activities for academic success. Table 5 Direct Relationships Relationship Path coefficients Analytical Skills and Personal Efforts -> Academic Writeups 0.658*** Scientific Research Workshop -> Academic Writeups 0.162*** Scientific Research Workshop -> Analytical Skills and Personal Efforts 0.782*** Source : Primary source computed using AMOS Mediation Analysis Table 6 presents compelling findings from mediation analysis, indicating substantial relationships between variables and the mediating influence of another variable. The results reveal a notable total effect (0.658, p < 0.05), encompassing both direct and indirect pathways, underscoring a robust association between the independent and dependent variables. Moreover, both the direct (0.144, p < 0.05) and indirect (0.514, p < 0.05) effects stand as individually significant, suggesting that the independent variable significantly impacts the dependent variable both directly and through the mediator. These findings emphasize the crucial role of considering both pathways in understanding the relationship between these variables, shedding light on the pivotal mediating function of the intermediary variable. Therefore, H2 is accepted. Further exploration into the specific value and mechanisms of this mediator promises to unveil deeper insights into the underlying dynamics governing the observed relationships. Table 6 Mediation Analysis Effect Standardized Estimation (SE) P-value Results Total 0.658 .000 Statistically significant Direct 0.144 .000 Statistically significant Indirect 0.514 .000 Statistically significant Source : Primary source computed using AMOS Multigroup Analysis Multigroup analysis is applied in social sciences to examine the differences across various groups. Statistics show refined variances in mean ratings between males and females across three domains: scientific research workshops, analytical skills and personal efforts, and academic writeups. Females showed higher ratings compared to males in all three domains. The independent samples t-tests executed subsequently validate these differences, showing statistically significant gender variations in perceptions of scientific research workshops and analytical skills and personal efforts (p 0.05). Particularly, females regard scientific research workshops and analytical skills and personal efforts remarkably more than males, propounding prospective gender-connected nuances in educational perceptions. These findings underscore the importance of gender-sensitive approaches in educational practices, offering insights to foster equitable learning environments and tailored support mechanisms. Further exploration into the underlying determinants of these differences promises valuable implications for educational policy and practice. The descriptive and ANOVA results (See Table S4) shows that perceptions remain relatively consistent across age brackets, with slight variations observed in mean ratings. The ANOVA results indicate non-significant differences in perceptions of scientific research workshops (p = 0.214), analytical skills and personal efforts (p = 0.626), and academic writeups (p = 0.972) between age groups. This suggests that age does not significantly influence perceptions in these domains among the sampled population. These findings imply that factors other than age may play a more substantial role in shaping perceptions of educational experiences and academic tasks. The descriptive statistics and ANOVA results (Table S5) shows that significant differences are observed between groups for all three domains (p < 0.05), indicating that role or position significantly influences perceptions. In comparison to the faculty, industrialists, and research assistants, the full-time research scholars have rated the scientific research workshops, analytical skills and personal efforts, and academic write-ups high. There is a consistent low rating by the industrialists in all domains, showing a significant distance between industry requirements and academic perceptions. The findings highlight the importance of including various viewpoints within the educational and proficient professional settings, with suggestions for curriculum advancement, collaboration and tiding over the gap between academia and industry. The descriptive statistics and ANOVA results (See Table S6) shows imperceptible differences in mean ratings between management and commerce, science, and arts disciplines. ANOVA results indicate insignificant differences in perceptions across these groups for all three domains (p > 0.05). This shows that the educational aspects related to the sample considered in the population is not significantly influenced by academic discipline. These findings posit a consistent level in perceptions across diversified academic streams, signifying the potential universality of specific educational skills and experiences. The descriptive statistics and ANOVA results (See Table S7) show that mean ratings vary slightly across academic years. The ANOVA results reveal significant differences in perceptions for analytical skills and personal efforts (p = 0.009) and academic write-ups (p = 0.004), but not for scientific research workshops (p = 0.421). Since there are variations in perceptions among students in various academic years regarding analytical skills and personal efforts as well as academic write-ups, propounding that student’s perception towards the various aspects of their education is influenced by academic progression. These findings highlight the significance of including the educational experience of students when outlining curriculum and support services, emphasizing the need for structured interventions to meet the evolving needs and challenges across various stages of academic learning. The descriptive statistics and ANOVA results (See Table S8) offer impactful insights into perceptions of scientific research workshops, analytical skills and personal efforts, and academic write-ups across various types of workshops. Here, mean ratings significantly vary across different types of workshops, the ANOVA results reveal significant differences in perceptions for analytical skills and personal efforts (p = .049) and academic write-ups (p = .044), and scientific research workshops (p = .021). Specifically, researchers who have attended quantitative workshops (i.e. Data Analysis using any Statistical Software Packages such as SPSS, AMOS, R, PLS, etc, Development and Validation of Research Instrument/Questionnaire, Bibliometric analysis, Meta-analysis, Sampling techniques, Financial analytics, Econometrics etc.) depicted high level of perception on scientific research workshops, analytical skills and personal efforts, and academic write-ups compared to qualitative and mixed method related workshops, suggesting that quantitative workshops are highly entertained by the researchers to understand the analytical tools. Moreover, the descriptive statistics and ANOVA results (See Table S9) offer insights into perceptions of scientific research workshops, analytical skills and personal efforts, and academic write-ups across different duration of workshops. Interestingly, the researchers who attended the workshop for a long duration (More than 7 days) demonstrated high involvement in scientific research workshops, analytical skills and personal efforts. Furthermore, the ANOVA results indicate significant differences in perceptions across varied duration of workshops (p < 0.05). These findings indicate the relevance of longer duration of workshops to enhance their perceptions on their research culture and significance. As a whole, it was proved that the researcher with different gender, age, disciplines, workshop type, duration, domain and stages of Ph.D. have depicted varied involvement in scientific workshop, analytical skill, personal efforts and academic write-ups leading to the acceptance of H3. DISCUSSION Research results deemed to be incomplete until it is communicated effectively to the broader spectrum of the stakeholders. Scientific research workshops are designed to ease the complexities involved in conducting and communicating research especially for the researchers involved in first phase or other phases of research. In order to give meaning to the purpose of research and its contribution to existing knowledge, the researcher should understand that “knowledge is power” as claimed by Francis Bacon. Hence, each researcher shall take an initiative to gain a scientific knowledge to enhance the quality and rigour of research publication. Considering its relevance, present study aimed to assess the effectiveness of scientific work shop on researcher’s academic write ups. Moreover, no study had argued the relevance of analytical skills and personal efforts of the researcher in this context which is addressed in this study. As per the findings, content relevance of the workshop found to be the significant predictor of academic write ups of the researcher. However, competencies of the resource person, quality and organization of scientific research workshop also played a significant role in improving researcher’s academic write ups. Furthermore, the role of Analytical Skills and Personal Efforts in driving the academic write-ups is immense suggesting that individuals with enhanced analytical skills and who invest considerable personal efforts tend to produce better academic write-ups. As per the study, the components of academic write ups included numerous outcomes such as aid in publications, topic selection, journal sections, collaboration, research ethics, research aspirations and content knowledge. In totality, the individual contribution of scientific workshop indicates that among the 7 components of academic write ups, scientific workshop is significantly vital to enhance the publications among the researchers. Moreover, this workshop is significantly helpful in topic selection, journal selection, collaboration, research ethics, research aspiration and content knowledge. In line with this, numerous studies have argued that Scientific writing workshops have demonstrated efficacy in improving participants' research writing abilities, resulting in higher publication rates and writing confidence (Gumusoglu et al. 2022 ; Balida et al. 2023 ; Sembiring et al. 2023 ; Hafid et al. 2023 ). These courses often include lectures, writing practice, and instructions on how to use research related software (Puteri 2022 ). Studies have found a strong association between workshop treatments and research writing abilities, stressing the importance of need based training sessions to improve academic performance. Moreover, we found that the contribution of scientific work shop on researcher’s analytical skills and personal efforts was significantly higher that its contribution towards academic write-ups, implying that these workshops significantly enhance individuals’ analytical skills and personal efforts, potentially through the acquisition of knowledge and practical research experience(Kaye and Colodner 1978 ; Bydder et al. 2006 ; Wortman-Wunder and Wefes 2020c ; Daud et al. 2020 ). As inferred earlier from the direct effect, scientific research workshop remarkably escalates academic write ups when researchers possess strong analytical skill and show high personal efforts than mere direct effect. These findings emphasize the crucial role of considering both pathways in understanding the relationship between these variables, shedding light on the pivotal mediating function of the analytical skills and personal efforts as argued by Rallón and Peñaloza-Rallón ( 2021 ). Further, the influence of age, designation, workshop type, domain, duration stages of Ph.D, and gender on respondents’ participation in scientific workshop, analytical skills and personal efforts as well as academic write ups was examined. Where, Females, in particular, demonstrated significantly higher involvement in scientific research workshops, analytical abilities, and personal efforts than males, indicating probable gender-related differences in educational perspectives. Moreover, full-time research scholars had higher ratings for scientific research workshops, analytical skills and personal efforts, and academic write-ups than academics, industrialists, and research assistants. Industrialists typically score lower across all areas, indicating a potential gap between academic perceptions and industrial viewpoints. These findings highlight the significance of incorporating multiple viewpoints in educational and professional settings, with implications for curriculum creation, cooperation, and bridging the gap between academia and industry. Further research into the underlying causes of these discrepancies might provide useful insights for improving educational and professional practices. However, researchers’ domain and age showed no influence on the above aspects. Specifically, there are notable differences in perceptions among students in different academic years or stages of PhD regarding analytical skills and personal efforts as well as academic write-ups, suggesting that academic progression influences how students perceive these aspects of their education where the scholars in 3rd year demonstrated high ratings for the above aspects but low for the beginners in research. Beginners, face numerous challenges due to the lack of guidance. PhD candidates who acknowledge their lack of preparation in academic writing believe that it is challenging for them to be completely independent as inexperienced researchers (Odena and Burgess 2017 ). Furthermore, as many PhD supervisors state that they do not have the time or expertise to impart such information, despite the fact that direct instruction may overcome this lack of preparation (Odena and Burgess 2017 ; González-Ocampo and Castelló 2018 ), the researchers must put a personal effort to extensively participate in scientific research workshop through their research journey as the study has proved that higher the experience in attending the workshop higher will be the analytical skills, personal efforts and academic write-ups. These findings emphasise and supports the previous discussions which argues that Scientific writing workshops are known to be effective in improving researchers' academic writing skills, although their effectiveness depends on a number of other variables (Permana et al. 2023 ) including varied types (Yeboah et al. 2022 ), duration of the workshops (Celik and Kapukaya 2022 ), stages of PhD (McAlpine and Boz 2023 )- where basic writing techniques and research proposal seems to be effective for novice researchers (Gimenez et al. 2024 ) and advanced research methodologies for the mid-stage researchers (Meuleners et al. 2023 ). Researchers’ ability to write better in their academic writing is greatly enhanced by attending scientific workshops. To build strong analytical abilities and engage in research-focused activities, it emphasizes the value of both individual dedication and outside training programmes like research workshops. It is also found that the degree to which researchers gain from the workshops in their academic writing is largely dependent on their personal efforts. CONCLUSION The study offers valuable insights into the relationships between scientific writing workshops, analytical abilities, and critical writing competency. As per the findings, attending scientific writing workshops is strongly associated with enhanced critical writing abilities. Furthermore, this relationship is mediated in large part by analytical skills, demonstrating that improving analytical skills is necessary for scientific writing workshops to improve critical writing skills. It emphasizes how important analytical skills are to the growth of critical writing competency and how important it is to provide scientific writing workshops and concentrate on the development of analytical abilities simultaneously. Analyzing the relationship between scientific writing workshops, analytical skills, personal dedication, and academic writing reveals the complex dynamics that influence the improvement of researchers’ critical writing abilities and highlights the diverse aspects of workshop participation and its potential influence on analytical skills, personal dedication, and the quality of academic writing. With organized support, positive feedback, and plenty of chances for practice, these workshops greatly aid in strengthening writing skills in academic settings. Developing analytical skills is a key component in turning workshop participation into concrete improvements in the quality and effectiveness of writing. This highlights the importance of personal motivation, commitment, and independent learning in getting the most out of attending workshops. Institutions and educators must recognize the value of analytical writing skills and modify their pedagogical approaches accordingly. Participants’ critical writing abilities can be improved more significantly and permanently if activities that help them become better analysts are included. Furthermore, the study highlights the significance of fostering writing abilities that extend across academic domains, stressing the association between rational thinking and clear communication in scientific domains. This has implications for scientific writing instruction as well as for the value of developing analytical skills to strengthen critical thinking and communication in both academic and professional contexts. PRACTICAL IMPLICATIONS The study has significant implications for professional growth, scientific writing, teaching, and research practices. It proposes incorporating activities that target the development of analytical and critical writing skills into scientific writing workshops, changing educational curricula\ to include analytical thinking alongside writing instruction, and initiating professional development initiatives that target personnel’s analytical skill development. To improve research outcomes and advance evidence-based procedures in scientific domains, it also underscores the importance of developing analytical abilities. Customizing the format and content of scientific workshops to fit the demands of researchers in varying fields of expertise and phases of their academic careers is essential for maximizing their impact. Considerations should be made for elements including the workshop’s nature, duration, subject matter, and the academic standing of the researcher. Researchers’ writing abilities and the calibre of their scholarly work can both be improved by encouraging them to take part in these sessions. Acknowledging the substantial impact that these workshops have on improving academic writing is crucial. Scientific workshops and academic writing are closely linked by analytical capabilities, indicating that workshop programmes should include elements that especially focus on enhancing analytical abilities. With this approach, researchers can enhance their writing abilities while also developing their critical synthesis and analysis capabilities, resulting in higher calibre and deeper academic work. Abbreviations AGFI Adjusted Goodness of Fit Index ANOVA Analysis of Variance AVE Average Variance Extracted CFI Confirmatory Factor Analysis GFI Goodness of Fit Index HTMT Heterotrait-Monotrait Ratio Method MoF Measure of Fit MPhil Master of Philosophy NFI Normed Fit Index PDF Post Doctoral Fellow PGFI Parsimony Goodness of Fit Index PhD Doctor of Philosophy RMSEA Root Mean Square Error of Approximation Smart PLS Smart Partial Least Squares SPM Structural Paraphrase Mapping Theory SPSS Statistical Package for Social Science SRMR standardized Root Mean Square Residual TLI Tucker-Lewis Index Declarations Acknowledgements The authors would like to thank Yenepoya (Deemed to be University), Mangalore, Karnataka, India for their valuable support and financial contributions. Author contributions Conceptualization & Formal Analysis: Niyaz Panakaje; Project Supervision: Arun A Bhagwath; Instrument Development: S. M. Riha Parvin, Harinakshi S; Methodology: S. M. 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15:10:58","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":299383,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-4934358/v1/a02c69901aa564c62048caa6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Scientific Writing Workshop and Academic Write-Ups: Do Analytical Skills and Personal Efforts Matter?","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePresently, there is a pressing need to improve the quality of higher education (Ruchina et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), where the predominant role of research is crucial along with teaching and extension (Caingcoy \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite the fact that research, innovation, and extension activities in Indian universities are often regarded as vital indicators of quality, they continue to fall behind in delivering quality research. Indian higher education institutions have a bad global reputation due to a weak research culture, a lack of fluency and scientific writing, and a desire to conduct research in the academic sector (Jha \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). When studying the research culture in the United States, it exemplifies a society that generates and disseminates information. Therefore, it is argued that India can become a global power in the twenty-first century by encouraging scientific research at its universities. Research, innovation, and extension activities account for a 25% of the university's total grade (Joshi \u0026amp; Bhojak, 2021) which includes research publications and awards. To do this, it must guarantee substantial financial support for research, as well as revive a research culture and consistently impart it to the following generation of students (Patel \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Basilio and Bueno \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A doctoral degree is essential to academic work. Numerous complementing aspects that contribute to the PhD experience have been found by prior research on doctoral education. Occasionally, the community of practice may fall short in offering PhD students sufficient guidance and shared authority. Students and the learning environment may experience ongoing, harmful friction as a result, which might negatively impact their wellbeing (Pyh\u0026auml;lt\u0026ouml; et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Understanding the state of the art in higher education research presents difficulties for inexperienced researchers. Similarly, seasoned academics may struggle to stay up to date with advancements in their profession and may get isolated in their area of specialty (Daenekindt and Huisman \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, academicians usually participate in the research and development activities for mere presence and acknowledgment and not involving in producing research papers which need to be tackled by examining and addressing each researchers\u0026rsquo; capabilities, competencies and research aspiration (Wong \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is a skill which needs relentless exercise and can be developed over a period of time(Manongsong and Panopio \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) by actively participating in research workshops, capacity building programmes and training programmes. Previous research has demonstrated that a scientific writing workshop can reduce writing-related stress, anxiety, and frustration by fostering a greater sense of confidence in one's writing abilities(Wortman-Wunder and Wefes \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). When a basic instructional workshop in written scientific communication is conducted by a writing instruction expert, novice researchers' writing self-efficacy and familiarity with the instructional content increase, enabling them to more successfully take on the challenging writing tasks they encounter (Bandura et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Wortman-Wunder and Wefes \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNonetheless, only a few percent of universities have adopted a thorough and systematic strategy to address their students' writing deficiencies (Gardner et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); the majority, on the other hand, have pieced together a patchwork of emergency support and corrections that neither deal with the underlying problems nor offer students ongoing assistance. Without expertise, supervision, and training, research will stay unsatisfactory. With a lack of training, workshops, and technological advancements, the art and ability of writing have lost relevance. Research is considered a constructive activity with the goal of contributing to society and solving its issues. Faculty development programmes and workshops play an important role in improving the skill and confidence of the researchers. Publishing in high quality journals necessitates sound reasoning, proficiency in writing, and expertise in academic achievement as well as research article publication. Majority of the researchers face difficulties in writing research papers. Nevertheless, workshops can help in improving these abilities. A positive relationship exists between workshop efficiency and students research writing skills, and confidence which further stresses that educational institutions should organize trainings and workshops to improve the performance of the students. Workshops and mini projects can motivate and transform the learners to evolve their identity as a researcher with adequate knowledge and skills, which will facilitate them to identify research areas and set research goals (Ramdani et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Hence, there is a need for institutional initiatives to accelerate researchers\u0026rsquo; participation in scientific writing workshop.\u003c/p\u003e \u003cp\u003ePrior research has examined writing retreats, the formation of writing groups, confidence in writing skills (McGrail et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Cameron et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Cable et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wortman-Wunder and Wefes \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e), but none that we are aware of has examined the influence of scientific writing workshop on researchers academic writeups by giving significant consideration to analytical skills and personal efforts. This research delves into understanding the impact of scientific writing workshop by considering all the domains and attempt to assess its contribution on each component of academic writeups including publication, topic selection, journal selection, research aspiration etc. It provides the comprehensive understanding of scientific workshop by assessing researchers\u0026rsquo; analytical skills as well as personal efforts in improving researchers academic writeups. The manuscript initially delves into understanding numerous theories and literatures available in the domain of scientific writing workshop further explains the development of hypothesis. After describing the methodological approach, study provides results and discussion and concludes with realistic implication to enhance the quality of research in higher education.\u003c/p\u003e"},{"header":"REVIEW OF LITERATURE","content":"\u003cp\u003eAcademic writing is a core component in imparting research findings, enriching knowledge and enhancing a researcher\u0026rsquo;s academic presence. However, researchers face problems in impactful conveyance of their ideas. To apprise these problems, scientific writing workshops contribute substantially as crucial mechanisms by providing guidance and content for skill advancement. The emphasis of this review is to comprehend the literature encompassing the role of scientific writing workshops in enhancing the confidence and potential of researchers in research writing.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eScientific Workshops and Academic Writing\u003c/h2\u003e \u003cp\u003eAcademic writing is of significant importance in all streams as it is a crucial skill for researchers, but a large number encounter setbacks in obtaining proficiency in this skill. Research studies have shown the resourcefulness of structured workshops in intensifying the assertiveness and intellectual capacity of pre- and postdoctoral researchers, which helps them in capacitating their potential in facing the challenges they confront in their research careers (Wortman-Wunder and Wefes \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e).The workshops which includes a structured format, which offer participants with all the required skills for research. In Kenya, participatory workshops that adopt enculturation and operational methods have depicted the ability in developing skills for authorship in health professionals pursuing their master\u0026rsquo;s programme (Onguka et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Involvement in interactive exercises and engaging them in acquiring in-depth knowledge in their respective subjects, help in facilitating identity in writing skills of the researchers, which is crucial for academic excellence. Additionally, studies have investigated how self-assessment of teaching writing influences the proficiency in writing and academic achievement at the higher education level in science courses (Navarro et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Integrating pedagogical strategies with self-assessment not only improves the quality of writing but also enhances academic achievement. This method highlights the significance of metacognition and self- appraisal in the process of writing.\u003c/p\u003e \u003cp\u003eConsequently, research has documented the advantages of intensive writing engagements, such as those provided in writing programmes for research focused doctoral candidates, from particularly international backgrounds (Mckenna and Kyser \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). These engagements not only improve proficiency in writing but also give a deep understanding of the need to seek help at the right time. Moreover, these workshops play a crucial role in the enhancement of effective pedagogy aligned to cater the needs of varied needs of student population. Furthermore, it has been recommended that English teaching should focus on enhancing academic communication and interaction with academic literature along with concentration on proficiency in writing (Mckenna and Kyser \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). By enhancing students\u0026rsquo; ability to effectively communicate and comprehend in English in their area of expertise, these workshops significantly contribute towards their complete academic competence and their capability for international professional engagement.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEffectiveness of Scientific Workshops\u003c/h3\u003e\n\u003cp\u003ePrevious studies have shown the impact of the competencies of the resource persons, quality and organisation of the workshop and content relevance of the scientific workshops on academic writing, which are discussed as follows:\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCompetencies of Resource Persons\u003c/h2\u003e \u003cp\u003eThe effectiveness of the workshop is considerably influenced by the proficiency and competence of the resource persons of the workshop. Ag\u0026eacute;silas (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) highlight those enhanced results of the workshop such as involvement of participants, retention of knowledge and acquisition of skill are evident if workshops are handled by academicians of expertise. Mastered facilitators can deliver detailed writing skills, provide productive feedback, and efficiently cater to the individual needs of the participants (Hapinat, 2023). Furthermore, their expertise and recognition among the academic clan strengthen the appraised worth of the workshop, encouraging participants to positively participate and implement the newfound knowledge (Syafrina and Khotimah \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eQuality and Organization of Workshop\u003c/h2\u003e \u003cp\u003eThe effectiveness of scientific writing workshops in increasing participants learning outcomes and engagement have been documented in various studies. (Ka..smann et al. 2023) showcased the effect of interactive and online course on student experience with scientific studies in radiation oncology, directing towards the enhancement of reflective reasoning and technical knowledge. Similarly, Fatmawati et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) highlighted the role of workshops in augmenting knowledge and capability in proposal preparation, thus enhancing the scientific writing practice. Oktavia Liza and Zudeta (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) emphasised how training in scientific writing enhances the professional career of teachers through the publication of research papers. Kurniawan et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) substantiated the importance of workshops for teachers aimed at scientific writing techniques led to improved proficiency and understanding in scientific paper writing. Muhammad Sirozi et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) further upholds the conception that workshops are of predominant significance in advancing the students\u0026rsquo; ability in structuring their thesis as well as improving their research design scientifically. These outcomes together highlight the significance of structured workshops in terms of pertinent content, involvement, peer appraisal, appropriate support, and accessibility to the required resources in advancing the grasping and skill enhancement experience in scientific writing.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eContent Relevance\u003c/h3\u003e\n\u003cp\u003eThe suitability and applicability of the workshop\u0026rsquo;s content are vital for obtaining participants concentration and capacitating continuous learning. Goyal et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) emphasises the need for orienting the workshops content with the expectations of the participants. Application of various teaching strategies such as exchange of ideas in groups, writing trials, and command help in meeting the varied learning expectations and methods. Additionally, workshops that include practicality and case studies offer enhanced clarity on the ability of participants to understand the subject content, instilling independency in their writing ability.\u003c/p\u003e \u003cp\u003eLastly, a multifaceted approach to workshops related to academic writing, combining systematic formats, engaging components, strategies for self-evaluation, and regional belongingness, is indispensable to advance the efficacy of academic writing skills among various student cohorts. By considering the relevant need and challenges faced by researchers, these workshops are vital in preparing researchers for successful pursuits in research and academics. Hence, Hypothesis H1 has been developed.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003eScientific workshops significantly enhance academic write ups among the researchers.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEssentiality of analytical skills and personal efforts of the researcher in academic write ups\u003c/h2\u003e \u003cp\u003eThe outcome of enriched analytical skills and exertion shown by the researcher is prominent in academic writing techniques (Rall\u0026oacute;n and Pe\u0026ntilde;aloza-Rall\u0026oacute;n \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The ability to strategically analyse the content and make knowledge-based conclusions is considered as a significant aspect in the advancement of academic writing (Craig et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, independent potential by the researcher with regard to dealing with issues, learning capability, and academic involvement with other research stakeholders, significantly converts the ability of the researcher, which impacts their skills of academic writing (Nygaard \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, a coalescence of analytical skills and individual commitment can positively enhance the potentiality of the academic writing skills among the researchers. Hence, it can be hypothesised that\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003eAnalytical skills and personal efforts of the researchers significantly mediates the relationship between scientific workshops and academic write ups.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eRole of workshop type, duration, domain knowledge and Ph.D. progression on academic write ups\u003c/h2\u003e \u003cp\u003eThe efficiency of the academic writing excellence in researchers is not only dependent on scientific writing workshops but is impacted by various other factors(Permana et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These workshops are organised in varied types such as systematised, interactive, self-evaluation and enculturation frameworks, specifically benefitting the academic writing skills (Yeboah et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, the duration of the workshops have a widespread influence on the intensity of learning and skill retention both in the form of short \u0026ndash; term concentrated programs or long \u0026ndash; term module courses (Celik and Kapukaya \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Participants of scientific workshops with extensive domain knowledge portray superior proficiency in writing (Evans and Bart \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Furthermore, the reach of relevant domain knowledge substantially impacts the precision of written elucidations, emphasising its relevance in comprehension (Darabi Bazvand \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Additionally, the academic writing skills of the researchers depends on the varied needs and stage of Ph.D of the researchers (McAlpine and Boz \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The basic writing techniques and research proposal enhancement training are beneficial for novice researchers (Gimenez et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Data analysis and manuscript preparation is very essential for mid-stage researchers (Meuleners et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thesis writing and publication writing techniques significantly impact the performance of last-stage researchers (Rafi and Moghees \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Hence, understanding the distinct perspectives and requirements of researchers are of predominant importance for organising structured scientific writing workshops that address the multifaceted needs in various streams and stages of Ph.D. advancement. Hence, the hypothesis H3 has been developed.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003cp\u003eThe researcher with different gender, age, disciplines, workshop type, duration, domain and stages of Ph.D. have depicted varied involvement in scientific workshop, analytical skill, personal efforts and academic write-ups.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eImpact on Academic Write-ups\u003c/h2\u003e \u003cp\u003eScientific writing workshops have a deep-rooted impact on numerous facets of academic write- ups. Lavinghouze et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that learners in well-structured workshops possibly produce standard manuscripts, undergo the process of publication efficiently, and exhibit expertise in writing for multiple needs. Additionally, workshops aid in multidisciplinary collaboration and networking platforms, widening participants\u0026rsquo; research potential, thereby enhancing exchange of knowledge and peer support.\u003c/p\u003e \u003cp\u003eScientific writing workshops serve as invaluable platforms for enhancing scholars\u0026rsquo; confidence and proficiency in academic writing. Previous studies have highlighted the various aspects involved in assessing the impact of academic write ups. However, there is a need to understand the interconnected mechanisms through which these widespread factors influence academic write ups. Further, research is warranted to justify the exploration of the interaction and influence of variables such as content relevance, competency of resource person and organisation of scientific workshops on academic writing as well as understanding the mediating role of academic skills and personal efforts of the researcher on the academic write ups. By focusing on facilitator competencies, workshop quality, and content relevance, these interventions empower participants to navigate scholarly communication effectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTHEORETICAL BACKGROUND OF THE STUDY\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eThe Kirkpatrick\u0026rsquo;s Evaluation Model\u003c/h2\u003e \u003cp\u003eThis Kirkpatrick\u0026rsquo;s evaluation model was proposed by Dr. Don Kirkpatrick in the year of 1950. Basically, this Kirkpatrick\u0026rsquo;s evaluation model helps to evaluate the results of training and learning programs by considering both formal and informal way (Streitwieser et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This approach, which comprises four stages starting with \u0026lsquo;Reaction\u0026rsquo; consists of the participants\u0026rsquo; satisfaction with the resource individuals, workshop quality, workshop materials and presentations (Abdulghani et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These remarks serve as a quick indicator of participants\u0026rsquo; initial impressions of the workshop (Rajeev et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The next stage is \u0026lsquo;Learning\u0026rsquo; observed improvement in knowledge of content delivered in the workshop addressed both theoretical and practical aspects in the research field and abilities of the participants (Ulum \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). \u0026lsquo;Behaviour\u0026rsquo; is the third stage, where the application of acquired skills and knowledge by the participants were focused. The final stage is \u0026lsquo;Results\u0026rsquo; which shows the impact of workshops to evaluate the feasibility of the program (Smidt et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This is frequently used to assess how well training courses, seminars, and other educational initiatives function.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eExperiential Learning Theory\u003c/h2\u003e \u003cp\u003eThis concept was published in year 1984 by an American psychologist, educator, and education theorist David Kolb, (Kolb and Kolb \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). According to this, learning comprises\u0026rsquo; concrete learning\u0026rsquo; or tangible experience, which occurs when a participant gets fresh experience, experiences curiosity, or represents an initial experience in research (Kolb et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The \u0026lsquo;reflective observation\u0026rsquo; or reflection of the participant considers the fresh experience to comprehend what it means, like trusting information through statistical evidence (Redmond et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The \u0026lsquo;Abstract Conceptualization\u0026rsquo; will help the participants\u0026rsquo; improve their critical thinking ability and decision-making based on facts and figures (Chang et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Finally, the \u0026lsquo;active experimentation\u0026rsquo; reflects the practical application of information or knowledge attained via workshops (Olanipekun et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to this, learning is most successful when people participate in all four phases of the learning cycle. It shows the importance of participating in the research activities through these stages fostering the growth of analytical abilities, critical thinking, and a more in-depth knowledge of their subject matter by the researchers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSelf-Determination Theory (SDT)\u003c/h2\u003e \u003cp\u003eThis theory was developed by Deci and Ryan, contends that when people\u0026rsquo;s basic psychological needs for autonomy, competence, and relatedness are satisfied, they are driven by internal causes (Ryan and Deci \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to this theory, the notion of \u0026lsquo;autonomy\u0026rsquo; comprises the various publications in research and identification of topics for research (Orsini et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), \u0026lsquo;competence\u0026rsquo; comprises an understanding of proficiency and efficiency through the quality of content (Reeve \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and connectivity which will increase the collaboration with other researchers and the topic preference viewpoint of the contributors are crucial in developing and maintaining personal enhancement and improvement (Chiu \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This theory will be used to understand the in-depth impact workshops have on the deep-rooted persuasion and individual development of the contributors. The concept provides a concrete conceptual structure for detailing the reason and the manner in which contributors can enhance their research and academic achievements when their basic psychological needs are met.\u003c/p\u003e \u003cp\u003eHence, this study focuses on understanding the extent of influence of scientific workshops on academic writing considering the various factors influencing the outcomes of academic writing as shown in the conceptual model (See Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e "},{"header":"METHODS","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eResearch Design\u003c/h2\u003e \u003cp\u003ePresent study was carried out using quantitative approach from collecting the data till analysing it. However, qualitative information was utilized to derive at the hypothesis and understand the existing state of studies context. With this multiple approach, objective of assessing the effectiveness of scientific workshop on academic write-ups through researchers\u0026rsquo; personal efforts and analytical skills was fulfilled.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eData Collection and Sampling\u003c/h2\u003e \u003cp\u003ePresent research targeted researchers who have awarded/registered/enrolled for Master of Philosophy (MPhil), Doctor of Philosophy (Ph.D.), and Post-Doctoral Fellow in the region of Karnataka, India. The inclusion criteria for selecting the sample satisfied the following conditions: Only those full-time and part-time researchers from various fields who have attended at least two scientific workshops in the last six months, with a minimum of one year of Ph.D or Masters experience in research.\u003c/p\u003e \u003cp\u003eSurvey questionnaire was formulated to obtain researchers participation in scientific workshop and their view on academic write-ups along with measuring their analytical skills and personal efforts. The Google form was circulated through varied online platforms such as WhatsApp, Gmail, Instagram, Facebook and through personal visits. Also, few responses were collected in the google form. As per All India Survey on Higher Education 2019-20, there are 14988 regular mode Ph.D. scholars, 528 M.Phil scholars and 13444 distance mode Ph.D. scholar and 528 M.Phil scholars in Karnataka. However, the exact number of doctoral fellows was not found in the report. Considering the available information sample size was calculated which was rounded up to 395. Overall, we have collected 440 responses through stratified convenience sampling, where we firstly divided the population into varied strata based on the types of universities and then based on convenience, researchers were selected who fulfilled the above criteria to fill up the form.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSample Profile\u003c/h2\u003e \u003cp\u003eThe sample profile presents a diverse demographic distribution (See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with a slight majority of females (55.5%) over males (44.5%). Age distribution is concentrated predominantly between 21 to 40 years, with the majority falling within the 31 to 40 age bracket (49.0%). Regarding designation, the majority are faculty members (69.0%), followed by full time research scholars (23.9%). In terms of discipline, Management and Commerce holds the highest representation (70.1%), while Science and Arts follow at 13.8% and 15.8%, respectively. The data suggests a predominantly young and academic-oriented demographic with a strong emphasis on Management and Commerce within the sample population.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample Profile\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 to 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 to 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 to 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 to 60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDesignation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndustrialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull-time Research Scholar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther (Please Specify)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDiscipline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManagement and Commerce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eSource\u003c/b\u003e: \u003cem\u003eSurvey data\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eResearch Instrument\u003c/h2\u003e \u003cp\u003eThe measurement of scientific research workshops is diligently designed using a comprehensive framework which comprises of various constructs and corresponding items. These are strained from the prevailing literatures in the area of the study, insights from focus group interviews, observations and the researchers\u0026rsquo; field expertise. The questionnaire was systematically divided into multiple sections to ensure a detailed and sophisticated data collection. The initial part of the questionnaire intended to determine the personal and research profile of the respondents. This consists of age, gender, designation, number of year(s) of experience in teaching, discipline, time reserve for research activities, workshops attended, year of pursuing Ph.D., and so on. The latter part of the questionnaire analyses the impact of scientific research workshops on enhancing academic write-ups, which is measured by considering various variables. Within the scope of the study, scientific research workshop is considered as independent variable, academic write ups as dependent variable and analytical skills as a mediator.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement Scale\u003c/h2\u003e \u003cp\u003eA total of 52 assessment criteria were utilised to evaluate the scientific workshops, academic write ups and analytical skills. The investigation includes 20 items to measure scientific workshops, 30 items for evaluating academic write ups and 12 items to measure analytical skills. The main objective of the study is to investigate the impact of attending scientific workshops in improving academic write ups among the academic scholars.\u003c/p\u003e \u003cp\u003eFive-point Likert scale was employed to elicit the research scholars\u0026rsquo; perception regarding the subject matter of the study. The scale spans from 1 (strongly disagree) to 5 (strongly agree). This methodology enabled a thorough investigation of the participants attitude and beliefs regarding the topic, offering a deeper comprehension of the influence of scientific workshops attendance on improving academic write up skills among research scholars.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eConstruct Development\u003c/h2\u003e \u003cp\u003eIn the present study, intensive review of past research in the area led to the identification of three key variables. The independent variable under investigation is the scientific research workshop which is measured using various dimensions such as Competencies of the Resource Person, Quality and Organization of Workshop and Content Relevance. These dimensions are derived from research published by Goyal et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Gaber and Ali (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Jusslin and Widlund (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The study proposes analytical skills and personal efforts as a mediator, with the evaluation parameters driven from studies of Knowles and McGloin (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), Al-Hasnawi and Muslem Al-Mousawi (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Cullen et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Mukti et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Politsinsky et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Theabthueng et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The dependent variable under analysis is academic writeups which is assessed using parameters such as publications, journal selection, topic selection, collaboration, research ethics, content knowledge and research aspiration which is obtained from works by Goyal et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Gaber and Ali (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Li et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Jusslin and Widlund (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Mydin et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Knowles and McGloin (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eEthical Consideration\u003c/h2\u003e \u003cp\u003eThe questionnaire is approved in the 3rd meeting of the Scientific Research Board, which scrutinized and deliberated on the application submitted through letter no. SRB/2024/05 of YIASCM, Yenepoya, Mangaluru (Deemed to be University), under the said policies and procedures for research work of the said university, on 09th March 2024. Oral consent was obtained from every respondent prior to the administration of the questionnaire.\u003c/p\u003e \u003cp\u003eThe respondents\u0026rsquo; voluntary participation was encouraged, and any risks or discomfort were kept to a minimum. Furthermore, there were no conflicts of interest or prejudices because the study was conducted impartially, transparently, and honestly, with the participants\u0026rsquo; rights and dignity in mind.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eData analysis and procedure\u003c/h2\u003e \u003cp\u003eFollowing the data collection, the data was fed into SPSS 26 which was then coded, checked and verified for errors. Moreover, the software Smart PLS 4 was used in this work to test the conceptual models (Ringle et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Partial least square structural equation modeling was used to examine quantitative data, which is predominantly used in the business and social science studies (Hair et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Firstly, we tested the measurement model to confirm the constructs and the indicators used to measure the respective constructs with acceptable factor loading, reliability and construct validity. Here, the measurement model was confirmed with Cronbach Alpha and composite reliability test to check its consistency and discriminant and convergent validity were used to test the accuracy of the data. Lastly, structural equation modelling was run in PLS SEM and hypothetical relationships were tested using path coefficients.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics\u003c/h2\u003e \u003cp\u003eThe data indicates a varied distribution of years of experience in the research field among respondents, with the majority (36.1%) having 1 to 3 years of experience, followed closely by those with 3 to 5 years (33.8%). Notably, a significant proportion (14.1%) have less than 1 year of experience, suggesting a considerable influx of newcomers into the research field. Regarding the average time spent on research-related activities per week, a substantial portion (47.3%) dedicate 10\u0026ndash;30% of their time, while 34.6% allocate 30\u0026ndash;50% of their time. This distribution suggests a considerable commitment to research endeavours among respondents, with a notable proportion dedicating a significant portion of their weekly schedule to research activities.\u003c/p\u003e \u003cp\u003eThe data presents the duration and frequency of workshops attended by participants. Across all duration categories (1 to 2 days, 3 to 5 days, 5 to 7 days, and more than 7 days), the most common response for the frequency of attendance is \"1\u0026ndash;3 times\", indicating a consistent level of engagement for participants across different workshop durations. Notably, for workshops lasting 5 to 7 days or more than 7 days, a considerable proportion of respondents never attended such workshops, suggesting that longer-duration workshops might pose scheduling or accessibility challenges for some participants. Additionally, the frequency of attendance tends to decrease as the duration of the workshops increases, with fewer respondents attending workshops more than 9 times across all duration categories. This suggests that while participants are engaged in workshops, they may prioritize shorter-duration events or face limitations in their availability for frequent attendance, particularly for longer workshops.\u003c/p\u003e \u003cp\u003eThe data illustrates the distribution of participants across different stages of their Ph.D. programs. The majority of respondents are in their early years of the Ph.D. journey, with 35.7% in their 1st year and 24.5% in their 2nd year. As expected, the percentage of participants decreases as the years progress, with 18.4% in their 3rd year, 9.5% in their 4th year, and 5.7% in their 5th year. Notably, a small portion (6.1%) of participants have been pursuing their Ph.D. for 5 years or more, indicating a longer duration of study for some individuals. This distribution suggests a diverse representation of participants at various stages of their doctoral research, with the majority being in the initial years of their programs.\u003c/p\u003e \u003cp\u003eThe descriptive statistics presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provide insights into participants\u0026rsquo; perceptions across various dimensions of the evaluated workshops or programs. Overall, the mean scores for competencies of the resource person, quality and organization of the workshop, content relevance, and the scientific research workshop as a whole indicate a consistently high level of satisfaction, with mean scores ranging from 4.2991 to 4.3577 out of 5. Similarly, participants rated aspects related to academic writeups, including publications, journal selection, topic selection, collaboration, research ethics, content knowledge, and research aspiration, positively, with mean scores ranging from 4.2028 to 4.3577 out of 5. Furthermore, participants perceived high levels of proficiency and personal effort in analytical skills, with a mean score of 4.3315 out of 5. These findings suggest a strong overall positive evaluation of the workshops or programs, indicating effectiveness in various dimensions such as content delivery, workshop organization, academic engagement, and personal skill development. Moreover, the descriptive statistics for each dimension with respective items of scientific writing workshops (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), analytical skills and personal efforts (Table S2) and academic writeups (Table S3) are presented in appendix by indicating its individual Mean, Std. Deviation and Std. Error Mean.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompetencies of the Resource Person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.3525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.43195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality and Organization of Workshop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.2991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.44662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContent Relevance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.3215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.42673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScientific Research Workshop (Overall)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.3244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.39403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.2028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.54149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJournal Selection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.2428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.45915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopic Selection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.2434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.42736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollaboration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.1474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.62676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch Ethics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.3577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.43200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContent Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.3204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.43134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch Aspiration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.3204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.43134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Writeups (Overall)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.2621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.38536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalytical Skills and Personal Efforts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.3315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.39605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eSource\u003c/b\u003e: \u003cem\u003ePrimary source computed using AMOS\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eInferential Analysis\u003c/h2\u003e \u003cp\u003eAt the first stage, we examined the rationality and the veracity of our instrument with the help of the Smart-PLS software, thereby improving the quality of our tool. First, it was represented in terms of Structural Equation Modeling (SEM) assumptions. PLS path modeling, as mentioned by Gudergan et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), is a good technique, which is often used in putting forward the cause-and-effect relationships of management research (Gudergan et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Compared to the CB-SEM (Covariance-Based SEM), which Rigdon (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) speaks, the PLS-SEM preserves the construct validity that embraces the indicator variables based on theory and improves the predictive accuracy. Specifically, the PLS-SEM technique helps in the modeling of complex models that comprise of multiple constructs, indicators and relationships, according to F. Hair Jr et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). It is beneficial for both development of elaborate models in the early\u003c/p\u003e \u003cp\u003estage theory development and testing as well as for the examination of constructs in the context of models. The efficiency makes theoretical programs more relevant for practical recommendations. This coincides with the current stage of development of the Structural Paraphrase Mapping (SPM) theory, where what is to be discovered are the relationships, their directions, unmistakable measures, and scales. As PLS-SEM suits the case herein, therefore; it is appropriate in our circumstances.\u003c/p\u003e \u003cp\u003eIn CB-SEM, larger samples are needed because all the variables are simultaneously evaluated while in PLS-SEM, the model is broken down into multi-blocks, so they are smaller. Unlike conventional methods that demands big sample, PLS-SEM is effective with small sample size and complex model, without making any assumptions about the data distributions (F. Hair Jr et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). With regard to PLS-SEM sample size, according F. Hair Jr et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), general rule is at least 10 as number of directed arrows (F. Hair Jr et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Besides, CB-SEM needs a sample number that is not less than 5 times the number of indicators in the input model. This property makes PLS-SEM especially apt for our present research.\u003c/p\u003e \u003cp\u003eThis paper incorporates a reflective argument on measurement, which is in line with scale development and what business commonly uses. According to this approach, there is an assumption that the tie between indicators and constructs is reflective. This implies that a latent construct has its autonomy and it is not linked by any particular measures, but they all carry some common semantic content. The same argument validates that excluding some dimensions from the construct will not change the conceptual meaning and purpose of the sense it is made to convey. Besides, it\u0026rsquo;s believed that probably, the items have a high positive interrelation because they are assessing one and the same construct. The monitors applied in this study are based on the instruments of the past studies. These constructs are considered a kind of reflective phenomenon that is in line with the modelling approach for project success, stakeholder engagement, and team building. Meanwhile, SPM as a tactic is known to be represented in most existing literature as being reflective.\u003c/p\u003e \u003cp\u003eThe paper is narrative, which is a popular practice in business and unit evaluation application theory (Coltman et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This approach assumes that this relationship between the reflection of construct and its indicators is reflective, which consequently means that underlying construct exists independently of the measurement methods being used and items are all themed around something common (Coltman et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Hence, abandoning some aspects of the scale would not change the underlying content associated with the phenomenon. More so, the positively high inter-correlation assumption is strongly found, since they are used to measure the same underlying aspect (Diamantopoulos and Siguaw \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In this study, the scales are applied following the validation in the earlier research in the area of engagement of stakeholders (Jang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), team building (Aga et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and project success. The latent variables are identified as reflective constructs that are characteristic of these areas. Moreover, the literature is not disconnected from the Strategic Project Management measure and looks to it as an illustrative (Carvalho and Rabechini Jr \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eHere is a section which presents in-depth insight to the data that we received from the survey questionnaire. The objective is to ensure consistency of the research model, addressing the research questions correctly, and evaluating the hypotheses efficiently thus making possible the derived reliable conclusions. Several analytical approaches are used to look into and fully understand the existing information. This is done to make sure that everything tallies with the given research aims and objectives. Through the use of a data-driven thorough evaluation and interpretation of the survey data, the main objective of this section is to provide a valid base for the foundation of research outcomes and their impact.\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eIndividual Variable Contribution Analysis\u003c/h2\u003e \u003cp\u003eThe Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents an individual contribution analysis, delineating how each predictor variable impacts the model constructs. In Model 1, Content Relevance (β\u0026thinsp;=\u0026thinsp;0.497, Sig.=0.000), Competencies of the Resource Person (β\u0026thinsp;=\u0026thinsp;0.144, Sig.=0.035), and Quality and Organization of Workshop (β\u0026thinsp;=\u0026thinsp;0.216, Sig.=0.001) all significantly contribute to the variance in the dependent variable. Similarly, in Model 2, the Scientific Research Workshop (β\u0026thinsp;=\u0026thinsp;0.632, Sig.=0.000) emerges as a substantial predictor of the outcome. Model 3 highlights the crucial role of Analytical Skills and Personal Efforts (β\u0026thinsp;=\u0026thinsp;0.761, Sig.=0.000) in driving the dependent variable. Moreover, the Scientific Research Workshop consistently demonstrates significant influence across Models 4 to 9, indicating its importance in various contexts. Nonetheless, in Model 10, the Scientific Research Workshop reasserts its significance (β\u0026thinsp;=\u0026thinsp;0.453, Sig.=0.000), emphasizing its variable nature across different analyses. As a whole, the individual contribution of scientific workshop indicates that among the 7 components of academic write ups, scientific workshop is significantly vital to enhance the publications among the researchers (β\u0026thinsp;=\u0026thinsp;0.634, Sig.=0.000). Moreover, this workshop is significantly helpful in Topic Selection (β\u0026thinsp;=\u0026thinsp;0.616, Sig.=0.000), Journal Selection (β\u0026thinsp;=\u0026thinsp;0.526, Sig.=0.000), Collaboration (β\u0026thinsp;=\u0026thinsp;0.512, Sig.=0.000), Research Ethics (β\u0026thinsp;=\u0026thinsp;0.462, Sig.=0.000), Research Aspiration (β\u0026thinsp;=\u0026thinsp;0.453, Sig.=0.000) and Content Knowledge (β\u0026thinsp;=\u0026thinsp;0.422, Sig.=0.000). Overall, these findings underscore the diverse contributions of individual predictor variables in shaping the respective constructs, highlighting areas of focus for enhancing model outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndividual Variable Contribution Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelationship\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eInference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCR →AW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e117.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRP → AW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQOW → AW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRW → AW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e291.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASPE→ AW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e603.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRW→ PUB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e294.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRW → JS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e167.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRW → TS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e267.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRW → COL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e118.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRW → RE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e155.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRW → CK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e195.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRW → RA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e109.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eSource\u003c/b\u003e: \u003cem\u003ePrimary source computed using AMOS\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement Model\u003c/h2\u003e \u003cp\u003eThe validity and reliability of the constructs were evaluated in the study in order to evaluate the measuring model. Confirmatory Factor analysis was used for the purpose of evaluation of measurement model. In the first stage, first order constructs of independent variable (Competencies of resource person, quality of workshop and content relevance) are evaluated with 21 items. Then, we checked factor loadings of each construct, where all items loaded greater than 0.5 except CRP1 loaded with 0.486 (See Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). As per the suggestion of the J. F. Hair et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003ea), the factor loading less than 0.5 is not good factor loading. Therefore, we removed CRP1 from further analysis. Further, CR1, CRP2, CRP3, QOW1, and QOW2 were affecting the discriminant validity of the constructs, therefore researchers had to remove the items from the model. Then, we again run confirmatory factor analysis, and result showed that the factor loadings were greater than 0.5 and even AVE (Average Variance Extracted) is greater than 0.5 as suggested by (Wong \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe outcomes of the model diagnostics were shown. Estimated model fits the data really well as it is supported by its relatively low chi square value (148.106) relative to its df (87) leading to a p-value that cannot be overestimated (0.000). Moreover, RMSEA (0.040), GFI (0.957), AGFI (0.940), PGFI (0.694), SRMR (0.030), NFI (0.955), TLI (0.977), and CFI (0.981), which are characteristics of suitable model fit, are all established through the AIC (214.106) and BIC (348.969) which are the additional predictions, confirming the model to be fit adequately. Thus, it can be regarded that the model estimations have shown to be strong and comprehensive, which have the capacity to represent the underlying relationships between variables appropriately.\u003c/p\u003e \u003cp\u003eIn the second stage, first order constructs of dependent variable (PU, JS, TS, CL, RE, CK and RA) were evaluated with help of confirmatory factor analysis. The result showed that the factor loadings of all constructs are above 0.5 and there is no problem with discriminant validity. Therefore, researcher kept all the items of second order constructs for further analysis. The result depicted in figure (See Figure S2). The model fit indices suggest that the picture about adequacy and the ability of the model to explain the observed data are in the range of not good. Some of the indices, such as the RMSEA, SRMR, NFI, TLI and CFI introduce a satisfactory fit, with many of the values either meeting or approaching determining thresholds (Kline \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), yet other indices like the chi-square test, GFI, AGFI and PGFI show areas for further development. The model explains 39% and 41% of variances of the dependent variables. The significant chi-square statistic and low goodness of fit indices support that the model does not match completely with the actual data, possibly because of modelling discrepancies and extra noise (Schumacker and Lomax \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Besides, the PGFI statistic indicates that the model may be oversized, which is connected with considering too much complicated model for the amount of data that is available (Wheaton et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). However, the model still gives only a partial picture, so more work and rechecking may be needed before the suggested patterns to data are properly concluded.\u003c/p\u003e \u003cp\u003eIn the third stage, we evaluated first order of mediating variable (Analytical Skills and Personal efforts). The result showed that all the items of the construct are above 0.5 of factor loadings. The model fit also showed satisfactory. Therefore, we kept all the items for further analysis (see Figure S3).\u003c/p\u003e \u003cp\u003eFinally, second order construct was measured using CFA (Confirmatory Factor Analysis) and result is depicted in Fig.\u0026nbsp;2. The result indicating that the items derived from the first order analysis were further kept and model fit indices are satisfactory.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConstruct Reliability and Validity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCronbach\u0026rsquo;s alpha (standardized)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCronbach\u0026rsquo;s alpha (unstandardized)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComposite reliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage variance extracted (AVE)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQOW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eSource\u003c/b\u003e: \u003cem\u003ePrimary source computed using AMOS\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e: CRP\u0026thinsp;=\u0026thinsp;Competencies of the Resource Person; CO\u0026thinsp;=\u0026thinsp;Quality and Organization of Workshop; CR\u0026thinsp;=\u0026thinsp;Content Relevance; AW\u0026thinsp;=\u0026thinsp;Academic Writeups; ASPE\u0026thinsp;=\u0026thinsp;Analytical Skills and Personal Efforts; SRW\u0026thinsp;=\u0026thinsp;Scientific Research Workshop; PUB\u0026thinsp;=\u0026thinsp;Publications; JS\u0026thinsp;=\u0026thinsp;Journal Selection; TS\u0026thinsp;=\u0026thinsp;Topic Selection; COL\u0026thinsp;=\u0026thinsp;Collaboration; RE\u0026thinsp;=\u0026thinsp;Research Ethics; RA\u0026thinsp;=\u0026thinsp;Research Aspiration\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e Depicts how reliable and valid the constructs turned out. The results of the analysis of Cronbach\u0026rsquo;s alpha statistics are shown both standardized and unstandardized and all constructs possess high internal consistency reliability. The values of the Cronbach\u0026rsquo;s alpha range from 0.814 to 0.889 (George and Mallery \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The computed coefficients of composite reliability (rho_c) additionally convey a high level of inter-item consistency as they range from 0.810 to 0.888 exceeding a threshold of 0.70, Hair (2010). Also, AVE values show that variables are unidimensional, with 0.506\u0026ndash;0.631 values exceeding the bench mark of 0.50 (Fornell and Larcker \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). Hence, convergent validity has been attained. The results imply that the tools employed were good measures for the traits being examined. Therefore, analysts and interpreters can be certain that they have a stable basis for more complex analyses and interpretations.\u003c/p\u003e \u003cp\u003eConcurrent validity was evaluated using the Close-to-Perfect criterion (Fornell and Larcker \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) as well as the Heterotrait-Monotrait Ratio method (HTMT). In applying Fornell Larcker\u0026rsquo;s criterion, the Average Variance Extracted (AVE) of each factor gotten is to be measured against the cross-loadings between each factor and other factors. Also, according to Henseler et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) by theme value of HTMT less than 0.9 can be stated as confirmed discriminant validity. Arguably, we have reconfirmed that all HMTV ratios were noticed beneath 0.90, as well, in conformity with the discriminant validity principles. After that, we assessed an overall MoF value over the recommended threshold of 0.617, which was defined as the recommendation of the article of (Wetzels et al. 2009) as a benchmark for the model to fit with the given complexity. Thus, according to this evaluation, the study of the model motivated us to think of the adequate structure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eStructural Model\u003c/h2\u003e \u003cp\u003eThe next step is to analyse the hypothesized model to test developed hypotheses. In the first stage, we have analysed the direct relationship. The result of direct relationship is tabulated in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and depicted in Fig.\u0026nbsp;3.\u003c/p\u003e \u003cp\u003eThe Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the direct relationships between Analytical Skills and Personal Efforts, Scientific Research Workshops, and Academic Writeups, as indicated by their path coefficients. Analytical Skills and Personal Efforts exhibit a strong positive relationship with Academic Writeups (path coefficient\u0026thinsp;=\u0026thinsp;0.658), suggesting that individuals with enhanced analytical skills and who invest considerable personal efforts tend to produce better academic writeups. Additionally, Scientific Research Workshops positively influence Academic Writeups (path coefficient\u0026thinsp;=\u0026thinsp;0.162), indicating that participation in such workshops contributes, albeit to a lesser extent, to the quality of academic writeups. Hence, H1 holds true. Moreover, the strongest relationship is observed between Scientific Research Workshops and Analytical Skills and Personal Efforts (path coefficient\u0026thinsp;=\u0026thinsp;0.782), implying that these workshops significantly enhance individuals\u0026rsquo; analytical skills and personal efforts, potentially through the acquisition of knowledge and practical research experience. Overall, these findings underscore the critical role of both individual efforts and external training programs, such as scientific research workshops, in shaping the quality of academic writeups, thereby emphasizing the importance of fostering analytical skills and facilitating participation in research-oriented activities for academic success.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDirect Relationships\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath coefficients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalytical Skills and Personal Efforts -\u0026gt; Academic Writeups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.658***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScientific Research Workshop -\u0026gt; Academic Writeups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.162***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScientific Research Workshop -\u0026gt; Analytical Skills and Personal Efforts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.782***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cb\u003eSource\u003c/b\u003e: \u003cem\u003ePrimary source computed using AMOS\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMediation Analysis\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents compelling findings from mediation analysis, indicating substantial relationships between variables and the mediating influence of another variable. The results reveal a notable total effect (0.658, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), encompassing both direct and indirect pathways, underscoring a robust association between the independent and dependent variables. Moreover, both the direct (0.144, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and indirect (0.514, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) effects stand as individually significant, suggesting that the independent variable significantly impacts the dependent variable both directly and through the mediator. These findings emphasize the crucial role of considering both pathways in understanding the relationship between these variables, shedding light on the pivotal mediating function of the intermediary variable. Therefore, H2 is accepted. Further exploration into the specific value and mechanisms of this mediator promises to unveil deeper insights into the underlying dynamics governing the observed relationships.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandardized Estimation (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResults\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistically significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistically significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistically significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eSource\u003c/b\u003e: \u003cem\u003ePrimary source computed using AMOS\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eMultigroup Analysis\u003c/h2\u003e \u003cp\u003eMultigroup analysis is applied in social sciences to examine the differences across various groups. Statistics show refined variances in mean ratings between males and females across three domains: scientific research workshops, analytical skills and personal efforts, and academic writeups. Females showed higher ratings compared to males in all three domains. The independent samples t-tests executed subsequently validate these differences, showing statistically significant gender variations in perceptions of scientific research workshops and analytical skills and personal efforts (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), though not in academic writeups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Particularly, females regard scientific research workshops and analytical skills and personal efforts remarkably more than males, propounding prospective gender-connected nuances in educational perceptions. These findings underscore the importance of gender-sensitive approaches in educational practices, offering insights to foster equitable learning environments and tailored support mechanisms. Further exploration into the underlying determinants of these differences promises valuable implications for educational policy and practice.\u003c/p\u003e \u003cp\u003eThe descriptive and ANOVA results (See Table S4) shows that perceptions remain relatively consistent across age brackets, with slight variations observed in mean ratings. The ANOVA results indicate non-significant differences in perceptions of scientific research workshops (p\u0026thinsp;=\u0026thinsp;0.214), analytical skills and personal efforts (p\u0026thinsp;=\u0026thinsp;0.626), and academic writeups (p\u0026thinsp;=\u0026thinsp;0.972) between age groups. This suggests that age does not significantly influence perceptions in these domains among the sampled population. These findings imply that factors other than age may play a more substantial role in shaping perceptions of educational experiences and academic tasks.\u003c/p\u003e \u003cp\u003eThe descriptive statistics and ANOVA results (Table S5) shows that significant differences are observed between groups for all three domains (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that role or position significantly influences perceptions. In comparison to the faculty, industrialists, and research assistants, the full-time research scholars have rated the scientific research workshops, analytical skills and personal efforts, and academic write-ups high. There is a consistent low rating by the industrialists in all domains, showing a significant distance between industry requirements and academic perceptions. The findings highlight the importance of including various viewpoints within the educational and proficient professional settings, with suggestions for curriculum advancement, collaboration and tiding over the gap between academia and industry.\u003c/p\u003e \u003cp\u003eThe descriptive statistics and ANOVA results (See Table S6) shows imperceptible differences in mean ratings between management and commerce, science, and arts disciplines. ANOVA results indicate insignificant differences in perceptions across these groups for all three domains (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This shows that the educational aspects related to the sample considered in the population is not significantly influenced by academic discipline. These findings posit a consistent level in perceptions across diversified academic streams, signifying the potential universality of specific educational skills and experiences.\u003c/p\u003e \u003cp\u003eThe descriptive statistics and ANOVA results (See Table S7) show that mean ratings vary slightly across academic years. The ANOVA results reveal significant differences in perceptions for analytical skills and personal efforts (p\u0026thinsp;=\u0026thinsp;0.009) and academic write-ups (p\u0026thinsp;=\u0026thinsp;0.004), but not for scientific research workshops (p\u0026thinsp;=\u0026thinsp;0.421). Since there are variations in perceptions among students in various academic years regarding analytical skills and personal efforts as well as academic write-ups, propounding that student\u0026rsquo;s perception towards the various aspects of their education is influenced by academic progression.\u003c/p\u003e \u003cp\u003eThese findings highlight the significance of including the educational experience of students when outlining curriculum and support services, emphasizing the need for structured interventions to meet the evolving needs and challenges across various stages of academic learning.\u003c/p\u003e \u003cp\u003eThe descriptive statistics and ANOVA results (See Table S8) offer impactful insights into perceptions of scientific research workshops, analytical skills and personal efforts, and academic write-ups across various types of workshops. Here, mean ratings significantly vary across different types of workshops, the ANOVA results reveal significant differences in perceptions for analytical skills and personal efforts (p\u0026thinsp;=\u0026thinsp;.049) and academic write-ups (p\u0026thinsp;=\u0026thinsp;.044), and scientific research workshops (p\u0026thinsp;=\u0026thinsp;.021). Specifically, researchers who have attended quantitative workshops (i.e. Data Analysis using any Statistical Software Packages such as SPSS, AMOS, R, PLS, etc, Development and Validation of Research Instrument/Questionnaire, Bibliometric analysis, Meta-analysis, Sampling techniques, Financial analytics, Econometrics etc.) depicted high level of perception on scientific research workshops, analytical skills and personal efforts, and academic write-ups compared to qualitative and mixed method related workshops, suggesting that quantitative workshops are highly entertained by the researchers to understand the analytical tools.\u003c/p\u003e \u003cp\u003eMoreover, the descriptive statistics and ANOVA results (See Table S9) offer insights into perceptions of scientific research workshops, analytical skills and personal efforts, and academic write-ups across different duration of workshops. Interestingly, the researchers who attended the workshop for a long duration (More than 7 days) demonstrated high involvement in scientific research workshops, analytical skills and personal efforts. Furthermore, the ANOVA results indicate significant differences in perceptions across varied duration of workshops (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings indicate the relevance of longer duration of workshops to enhance their perceptions on their research culture and significance.\u003c/p\u003e \u003cp\u003eAs a whole, it was proved that the researcher with different gender, age, disciplines, workshop type, duration, domain and stages of Ph.D. have depicted varied involvement in scientific workshop, analytical skill, personal efforts and academic write-ups leading to the acceptance of H3.\u003c/p\u003e \u003c/div\u003e\n"},{"header":"DISCUSSION","content":"\u003cp\u003eResearch results deemed to be incomplete until it is communicated effectively to the broader spectrum of the stakeholders. Scientific research workshops are designed to ease the complexities involved in conducting and communicating research especially for the researchers involved in first phase or other phases of research. In order to give meaning to the purpose of research and its contribution to existing knowledge, the researcher should understand that \u0026ldquo;knowledge is power\u0026rdquo; as claimed by Francis Bacon. Hence, each researcher shall take an initiative to gain a scientific knowledge to enhance the quality and rigour of research publication. Considering its relevance, present study aimed to assess the effectiveness of scientific work shop on researcher\u0026rsquo;s academic write ups. Moreover, no study had argued the relevance of analytical skills and personal efforts of the researcher in this context which is addressed in this study. As per the findings, content relevance of the workshop found to be the significant predictor of academic write ups of the researcher. However, competencies of the resource person, quality and organization of scientific research workshop also played a significant role in improving researcher\u0026rsquo;s academic write ups. Furthermore, the role of Analytical Skills and Personal Efforts in driving the academic write-ups is immense suggesting that individuals with enhanced analytical skills and who invest considerable personal efforts tend to produce better academic write-ups. As per the study, the components of academic write ups included numerous outcomes such as aid in publications, topic selection, journal sections, collaboration, research ethics, research aspirations and content knowledge. In totality, the individual contribution of scientific workshop indicates that among the 7 components of academic write ups, scientific workshop is significantly vital to enhance the publications among the researchers. Moreover, this workshop is significantly helpful in topic selection, journal selection, collaboration, research ethics, research aspiration and content knowledge. In line with this, numerous studies have argued that Scientific writing workshops have demonstrated efficacy in improving participants' research writing abilities, resulting in higher publication rates and writing confidence (Gumusoglu et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Balida et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sembiring et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hafid et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These courses often include lectures, writing practice, and instructions on how to use research related software (Puteri \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Studies have found a strong association between workshop treatments and research writing abilities, stressing the importance of need based training sessions to improve academic performance. Moreover, we found that the contribution of scientific work shop on researcher\u0026rsquo;s analytical skills and personal efforts was significantly higher that its contribution towards academic write-ups, implying that these workshops significantly enhance individuals\u0026rsquo; analytical skills and personal efforts, potentially through the acquisition of knowledge and practical research experience(Kaye and Colodner \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Bydder et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Wortman-Wunder and Wefes \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2020c\u003c/span\u003e; Daud et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As inferred earlier from the direct effect, scientific research workshop remarkably escalates academic write ups when researchers possess strong analytical skill and show high personal efforts than mere direct effect. These findings emphasize the crucial role of considering both pathways in understanding the relationship between these variables, shedding light on the pivotal mediating function of the analytical skills and personal efforts as argued by Rall\u0026oacute;n and Pe\u0026ntilde;aloza-Rall\u0026oacute;n (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther, the influence of age, designation, workshop type, domain, duration stages of Ph.D, and gender on respondents\u0026rsquo; participation in scientific workshop, analytical skills and personal efforts as well as academic write ups was examined. Where, Females, in particular, demonstrated significantly higher involvement in scientific research workshops, analytical abilities, and personal efforts than males, indicating probable gender-related differences in educational perspectives. Moreover, full-time research scholars had higher ratings for scientific research workshops, analytical skills and personal efforts, and academic write-ups than academics, industrialists, and research assistants. Industrialists typically score lower across all areas, indicating a potential gap between academic perceptions and industrial viewpoints. These findings highlight the significance of incorporating multiple viewpoints in educational and professional settings, with implications for curriculum creation, cooperation, and bridging the gap between academia and industry. Further research into the underlying causes of these discrepancies might provide useful insights for improving educational and professional practices. However, researchers\u0026rsquo; domain and age showed no influence on the above aspects. Specifically, there are notable differences in perceptions among students in different academic years or stages of PhD regarding analytical skills and personal efforts as well as academic write-ups, suggesting that academic progression influences how students perceive these aspects of their education where the scholars in 3rd year demonstrated high ratings for the above aspects but low for the beginners in research. Beginners, face numerous challenges due to the lack of guidance. PhD candidates who acknowledge their lack of preparation in academic writing believe that it is challenging for them to be completely independent as inexperienced researchers (Odena and Burgess \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, as many PhD supervisors state that they do not have the time or expertise to impart such information, despite the fact that direct instruction may overcome this lack of preparation (Odena and Burgess \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gonz\u0026aacute;lez-Ocampo and Castell\u0026oacute; \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the researchers must put a personal effort to extensively participate in scientific research workshop through their research journey as the study has proved that higher the experience in attending the workshop higher will be the analytical skills, personal efforts and academic write-ups. These findings emphasise and supports the previous discussions which argues that Scientific writing workshops are known to be effective in improving researchers' academic writing skills, although their effectiveness depends on a number of other variables (Permana et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) including varied types (Yeboah et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), duration of the workshops (Celik and Kapukaya \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), stages of PhD (McAlpine and Boz \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)- where basic writing techniques and research proposal seems to be effective for novice researchers (Gimenez et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and advanced research methodologies for the mid-stage researchers (Meuleners et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearchers\u0026rsquo; ability to write better in their academic writing is greatly enhanced by attending scientific workshops. To build strong analytical abilities and engage in research-focused activities, it emphasizes the value of both individual dedication and outside training programmes like research workshops. It is also found that the degree to which researchers gain from the workshops in their academic writing is largely dependent on their personal efforts.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe study offers valuable insights into the relationships between scientific writing workshops, analytical abilities, and critical writing competency. As per the findings, attending scientific writing workshops is strongly associated with enhanced critical writing abilities. Furthermore, this relationship is mediated in large part by analytical skills, demonstrating that improving analytical skills is necessary for scientific writing workshops to improve critical writing skills. It emphasizes how important analytical skills are to the growth of critical writing competency and how important it is to provide scientific writing workshops and concentrate on the development of analytical abilities simultaneously.\u003c/p\u003e \u003cp\u003eAnalyzing the relationship between scientific writing workshops, analytical skills, personal dedication, and academic writing reveals the complex dynamics that influence the improvement of researchers\u0026rsquo; critical writing abilities and highlights the diverse aspects of workshop participation and its potential influence on analytical skills, personal dedication, and the quality of academic writing. With organized support, positive feedback, and plenty of chances for practice, these workshops greatly aid in strengthening writing skills in academic settings. Developing analytical skills is a key component in turning workshop participation into concrete improvements in the quality and effectiveness of writing. This highlights the importance of personal motivation, commitment, and independent learning in getting the most out of attending workshops.\u003c/p\u003e \u003cp\u003eInstitutions and educators must recognize the value of analytical writing skills and modify their pedagogical approaches accordingly. Participants\u0026rsquo; critical writing abilities can be improved more significantly and permanently if activities that help them become better analysts are included. Furthermore, the study highlights the significance of fostering writing abilities that extend across academic domains, stressing the association between rational thinking and clear communication in scientific domains. This has implications for scientific writing instruction as well as for the value of developing analytical skills to strengthen critical thinking and communication in both academic and professional contexts.\u003c/p\u003e "},{"header":"PRACTICAL IMPLICATIONS","content":"\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003cp\u003eThe study has significant implications for professional growth, scientific writing, teaching, and research practices. It proposes incorporating activities that target the development of analytical and critical writing skills into scientific writing workshops, changing educational curricula\\ to include analytical thinking alongside writing instruction, and initiating professional development initiatives that target personnel\u0026rsquo;s analytical skill development. To improve research outcomes and advance evidence-based procedures in scientific domains, it also underscores the importance of developing analytical abilities. Customizing the format and content of scientific workshops to fit the demands of researchers in varying fields of expertise and phases of their academic careers is essential for maximizing their impact. Considerations should be made for elements including the workshop\u0026rsquo;s nature, duration, subject matter, and the academic standing of the researcher. Researchers\u0026rsquo; writing abilities and the calibre of their scholarly work can both be improved by encouraging them to take part in these sessions. Acknowledging the substantial impact that these workshops have on improving academic writing is crucial. Scientific workshops and academic writing are closely linked by analytical capabilities, indicating that workshop programmes should include elements that especially focus on enhancing analytical abilities. With this approach, researchers can enhance their writing abilities while also developing their critical synthesis and analysis capabilities, resulting in higher calibre and deeper academic work.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":" \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAGFI\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAdjusted Goodness of Fit Index\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eANOVA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAnalysis of Variance\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAVE\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAverage Variance Extracted\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCFI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eConfirmatory Factor Analysis\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eGFI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eGoodness of Fit Index\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHTMT\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eHeterotrait-Monotrait Ratio Method\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMoF\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMeasure of Fit\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMPhil\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMaster of Philosophy\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNFI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNormed Fit Index\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePDF\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePost Doctoral Fellow\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePGFI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eParsimony Goodness of Fit Index\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePhD\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eDoctor of Philosophy\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eRMSEA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eRoot Mean Square Error of Approximation\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSmart PLS\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSmart Partial Least Squares\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSPM\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eStructural Paraphrase Mapping Theory\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSPSS\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eStatistical Package for Social Science\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSRMR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003estandardized Root Mean Square Residual\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTLI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTucker-Lewis Index\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Yenepoya (Deemed to be University), Mangalore, Karnataka, India for their valuable support and financial contributions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization \u0026amp; Formal Analysis: Niyaz Panakaje; Project Supervision: Arun A Bhagwath; Instrument Development: S. M. Riha Parvin, Harinakshi S; Methodology: S. M. Riha Parvin, Madhura K, Abhinandan Kulal; Results \u0026amp; Interpretation: Abhinandan Kulal, Niyaz Panakaje; Writing - revision and editing: Shakira Irfana, Niha Sheik; Review: Shareena P, Ujwala. \u0026nbsp;Data Curation: All the Author; All the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by Yenepoya (Deemed to be University), Mangalore, Karnataka, India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting this study\u0026apos;s findings are not publicly available due to confidentiality and other ethical considerations. However, data may be available from the corresponding authors upon reasonable request and with permission from the institution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests that could bias the results of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest or any other declarations of interest to report.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdulghani, H. M., Shaik, S. A., Khamis, N., et al. (2014). 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ASCILITE Publications e22094. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14742/apubs.2022.94\u003c/span\u003e\u003cspan address=\"10.14742/apubs.2022.94\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"asian-pacific-journal-of-second-and-foreign-language-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jsfl","sideBox":"Learn more about [Asian-Pacific Journal of Second and Foreign Language Education](http://sfleducation.springeropen.com)","snPcode":"40862","submissionUrl":"https://submission.nature.com/new-submission/40862/3","title":"Asian-Pacific Journal of Second and Foreign Language Education","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Scientific Writing Workshop, Analytical Skills, Academic Write Ups, Research, Higher Education","lastPublishedDoi":"10.21203/rs.3.rs-4934358/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4934358/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eScientific writing workshops have evolved as crucial platforms for elevating expertise in academic writing skills, developing confidence and stimulating research pursuits among researchers. In this quantitative study, using structured questionnaire with the sample of 440 researchers who have awarded/registered/enrolled for Master of Philosophy (MPhil), Doctor of Philosophy (PhD), and Post Doctoral Fellow through stratified convenience sampling from Karnataka, India, we have examined the influence of scientific writing workshop on researchers academic writeups by giving significant consideration to their analytical skills and personal efforts. We have found that scientific research workshop remarkably escalates academic write ups when researchers possess strong analytical skills and show high personal efforts. Particularly, it majorly contributes to enhance researchers\u0026rsquo; quality publications flowed by assisting in topic selection, journal selection, collaboration, research ethics, research aspirations and content knowledge. Further, we have explored varied involvement of researchers in scientific workshop, analytical skills, personal efforts and academic write-ups with different workshop type, duration, and stages of Ph.D. The study has significant implications for professional growth, scientific writing, teaching, and research practices which implies that researchers can enhance their writing abilities while also developing their critical synthesis and analysis capabilities, resulting in higher calibre and deeper academic work.\u003c/p\u003e","manuscriptTitle":"Scientific Writing Workshop and Academic Write-Ups: Do Analytical Skills and Personal Efforts Matter?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-26 15:10:53","doi":"10.21203/rs.3.rs-4934358/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-04T06:48:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-02T11:59:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311022702999980220612998489276575286311","date":"2025-01-21T10:54:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-28T13:48:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213233928089641011015540685438704637696","date":"2024-10-25T09:27:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-03T04:18:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-27T08:10:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-27T08:09:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Asian-Pacific Journal of Second and Foreign Language Education","date":"2024-08-18T17:29:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"asian-pacific-journal-of-second-and-foreign-language-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jsfl","sideBox":"Learn more about [Asian-Pacific Journal of Second and Foreign Language Education](http://sfleducation.springeropen.com)","snPcode":"40862","submissionUrl":"https://submission.nature.com/new-submission/40862/3","title":"Asian-Pacific Journal of Second and Foreign Language Education","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8fe93aba-74d7-4ced-a57e-3e95639d8f9c","owner":[],"postedDate":"September 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T08:55:06+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-26 15:10:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4934358","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4934358","identity":"rs-4934358","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}