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A case is made for developing “Relationally Integrated Value Networks for Total Facilities Management” (RIVANS-TFM) by synergistically connecting significant stakeholders of the project management (PM) and facilities management (FM) phases to deliver substantially better value for the end users of built infrastructure. The paper aims to discuss these issues.

A structured questionnaire survey enabled identification of typically significant stakeholders in the PM and FM phases. In total, 14 key stakeholders were identified through t-test analysis in this Sri Lankan study. Semi-structured interviews unveiled relationships among the aforementioned stakeholders and the findings were used to develop the “required” RIVANS-TFM, as well as the “existing” RIVANS-TFM, using the UCINET social network analysis software package. Social network theory of relationships was applied to analyze the networks in terms of “Structural Holes” or missing links and “Brokerage Potentials.”

Structural holes analysis highlighted the existing setup to be more vulnerable to missing links than the “required”/targeted setup. Furthermore, brokerage potentials analysis revealed that owners, project managers, facility managers, maintenance engineers, main contractors, designers, principal consultants, and other specialist consultants can act as “brokers” to bridge the gaps or minimize structural holes, thereby uplifting and reinforcing the existing network to deliver better performance and value in TFM.

By revealing existing and required levels of integration of each stakeholder in RIVANS-TFM, clients are provided a great opportunity to identify the stakeholders who should be engaged more, or less – in order to best achieve clients’ long-term aspirations and project objectives. Furthermore, the findings also indicate appropriate levels of stakeholder relationships to target, in order to maintain efficient flows of information, material and services in the supply chains while enhancing TFM life-cycle values.

The purpose of this paper is to develop a collaborative facilities management (CFM) model incorporating facilities management (FM) functions that can be shared and performed collaboratively by two or more organisations to enhance the performance of FM functions of collaborating organisations.

A critical literature review followed by a preliminary investigation was used to identify main categories of FM functions that can be performed collaboratively and dimensions that affect FM collaboration. Subsequently, a detailed list of FM functions that can be performed collaboratively under eight different contexts based on three dimensions identified through observations, document reviews and in-depth interviews with experts who have experience in each context.

The paper introduces a novel concept of CFM to share utility/infrastructure facilities, facility services and information by two or more organisations to maintain, improve, adapt and ensure functionality of the built environments of the collaborating organisations. The study identified core business, geographical location and ownership as the key dimensions that affect CFM concept. The outcome of the study, which is CFM model, proposes FM functions that can be performed collaboratively and benefits of collaborating utilities/infrastructure facilities, facility services and information under the aforementioned key dimensions to create a win-win situation for the organisations.

The paper highlights the FM functions that can be performed collaboratively in order to provide favourable solutions for operational issues faced by facilities managers, while optimising the FM performance of the organisations.

The CFM model offers original insights to the FM functions that can be shared and performed collaboratively by organisations under different contexts to optimise the FM performance.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

By acquiring and growing knowledge, proactively using skills, and improving digital skills via project-based learning, students will be well-prepared for their future careers. In construction 4.0 and other digitally developing industries, it is essential for students that their education consists of digital training and innovation opportunities to foster proactive skills and prepare them effectively for the workforce.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

This study explores the role of proactive decision-making in enhancing academic motivation and career calling among university students in Central Asia. It aims to investigate how proactive traits and cognitive skills influence career calling and academic persistence while examining the moderating effects of demographic factors.

A convenience sample of 380 undergraduate business students from Kazakhstan was surveyed using validated scales for proactive decision-making, career calling and academic motivation. Data analysis involved correlation, regression and mediation tests using SPSS to evaluate relationships between variables and test the mediating role of career calling in the proactive decision-making-academic motivation link.

Proactive decision-making was found to significantly enhance career calling and academic motivation, with career calling partially mediating the relationship between proactive decision-making and intrinsic/extrinsic motivation. In addition, demographic variables, such as gender and GPA, moderated the effects of proactive decision-making on academic motivation.

This research contributes to the understanding of proactive decision-making in the educational context, particularly within international business education, by highlighting its role in shaping career aspirations and academic engagement. It provides new insights into the interplay between personality, decision-making and academic motivation in a Central Asian setting.

This paper aims to examine whether CEOs with an engineering background increase corporate investment efficiency (CIE). The authors further investigate the role of engineering directors on boards of the above association.

Drawing from upper-echelon theory, which suggests that corporate outcomes are a reflection of its top management characteristics, the authors hypothesise a positive association between engineer CEOs and CIE and a positive moderation role of the proportion of engineer directors on boards in the above association. The authors examine this link using a sample of Australian Securities Exchange 200 firms from 2015 to 2022. Engineer CEO data is hand-collected from corporate annual report biographies and investment efficiency is a measure that captures whether the investments are maintained at optimal levels relative to industry-year benchmarks, following the approaches of Biddle et al. (2009), Chen et al. (2011) and the average values of both models.

The results indicate support for the hypotheses, revealing that firms managed by engineer CEOs have higher investment efficiency than their counterpart firms. This association is exacerbated in the presence of a higher proportion of engineer directors on boards. The results are robust to year and industry-fixed effects, propensity score matching, alternative measures of investment efficiency and robust standard errors. Our results also remain valid for an industry sub-sample using certain industries in which engineering expertise maybe more desirable (e.g. metals and mining).

By showing that engineer CEOs are significantly associated with CIE, the authors contribute to upper-echelon literature examining the link between CEO characteristics and corporate outcomes, particularly, investment decision efficiency. The influence of engineering background on corporate outcomes is less examined in the literature; thus, the authors contribute to this thin literature.

The findings are informative to potential investors in evaluating firms’ investment efficiency before investing in firms. For example, firms with engineer CEOs are likely to maintain efficient investment levels in future years.

The purpose of this paper is to identify the determinants of dividend policy in an emerging and developing market.

The study employs a quantitative approach using 191 Sri Lankan firms and 1,337 firm-year observations as the sample. The authors apply a Binary Logistic Regression model to uncover the determinants of the propensity to pay dividends, and a Fixed Effect Panel Regression to investigate the determinants of dividend payout.

The authors identify past dividend decision, earnings, investment opportunities, profitability, free cash flow (FCF), corporate governance, state ownership, firm size and industry influence as the key determinants of propensity to pay dividends. In addition past dividends, investment opportunities, profitability and dividend premium are identified as the determinants of dividend payout. Moreover, there is a feedback between dividend yield and profitability in one lag and between dividend yield and dividend premium in two lags, as short-term relationships. Hence, past dividend decision or payout, profitability and investment opportunities are a common set of determinants with implications for both propensity to pay dividends and its payout. The findings support theories of dividends such as signaling, outcome, catering, life cycle, FCF and pecking order.

The findings are important for investors, managers and future research. Investors should focus on the determinants identified by our study when making investment decisions whereas managers should practice the same when formulating appropriate dividend policies for their firms. Future research should rely on propensity to pay dividends and its payout simultaneously to promote a theoretical consensus on the dividend determinant puzzle.

This is the first study that investigates determinants of propensity to pay dividends and dividend payout along with short-term relationships in a single study.

The objective of this study is to accurately predict the cost of green buildings to provide quantifiable criteria for investment decisions from investors.

This study proposes a hybrid prediction model ML-based for cost prediction of GBPs and obtains prediction parameters (PPs) associated with project characteristics through data mining (DM) techniques. The model integrates a principal component analysis (PCA) method to perform parameter dimensionality reduction (PDR) on a large number of raw variables to provide independent characteristic terms. Moreover, the support vector machine (SVM) algorithm is improved to optimize the prediction results and integrated with parameter dimensionality reduction and cost prediction.

The prediction results show that the mean absolute and relative errors of the hybrid prediction model proposed in this study are equal to 39.78 and 0.02, respectively, which are much lower than those of the traditional SVM model and MRA prediction model. Moreover, the hybrid prediction model with parameter dimensionality reduction also achieved better prediction accuracy (R² = 0.319) and superior prediction accuracy for different cost terms.

Theoretically, the hybrid prediction model developed in this study can reliably predict the cost while accurately capturing the characteristics of GBPs, which is a bold attempt at a comprehensive approach. Practically, this study provides developers with a new ML-based prediction model that is capable of capturing the costs of projects with ambiguous definitions and complex characteristics.

The present scenario of the Covid-19 pandemic, together with technological advancements, has provoked unprecedented turbulence in the education system, notably in universities, disrupting their conventional teaching and learning processes. Despite government support, teachers and students faced numerous challenges in the online teaching and learning process than expected. Extensive research has been undertaken to investigate the context of online education from the perspective of students, but less emphasis has been given to examining the views of teachers. Thus, the study aimed to explore attitudes of academics towards online teaching during the Covid-19 outbreak.

The study was conducted in Sri Lanka, with the population consisting of academics representing all state universities. An email survey was conducted randomly, and 355 academics responded to a Google form, out of which 332 refined questionnaires were used for analysis. Multi-item reflective indicators were used to operationalize the research constructs on a 7-point Likert scale. The face, content, and construct validities were ensured appropriately. The reflective-reflective two-stage hierarchical approach with disjoint option was applied to analyze the data using the Partial Least Squares Structural Equation Modelling (PLS-SEM) model. After rigorous and careful examination of data, the descriptive statistics revealed that the university academics are pretty distressed with their Internet connections and facilities at home to conduct online lectures.

Active student participation was just above the average, while teacher's attitudes towards online education, their willingness to continue it further seem quite unsatisfactory. Teacher readiness for online education appears to be acceptable only to a certain extent. The key findings of the study disclosed that the attitudes towards online teaching are determined by perceived usefulness, teacher readiness, perceived student participation whereas active student participation, a conducive teaching environment and institutional support have no significant impact on attitude towards online teaching. Further, the study contributed to the education literature analyzing online education in the Sri Lankan context.

This study sparked the debate and contributed to the literature exploring the concept from the side of teachers in which attitudes of teachers towards online teaching in Sri Lankan Contest.