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The Hamilton canal in the western province of Sri Lanka is a man-made canal situated in an area with immense anthropogenic pressures. The purpose of this study is to identify the quality variations of the water in Hamilton canal and human perception about the present status of the water of the canal.

Sampling has been carried out in seven locations in the canal during dry and wet periods for water quality analysis. In situ field-testing and laboratory analysis have been conducted for physicochemical, heavy metal, oil and grease analysis of water. Only Pb, Cd, oil and grease were tested in the canal sediments. The samples were analyzed as per the standard methods of the American Public Health Association (APHA) Manual: 20th edition. A semi-structured questionnaire survey has been carried out to assess the human perception on the water of the canal.

The results revealed that average EC, Turbidity, Total Hardness, TDS, F⁻, Fe²+, Cl⁻, SO₄²⁻ and PO₄³⁻ of the canal water remained above the threshold limits of inland water standards. Concentrations of Pb and Cd were also above the standards in some locations. Oil and grease were in a very high level in water and sediments.

The water of the canal has been affected by nutrient, heavy metal and oil and grease pollution at present. Discharge of domestic, industrial, municipal wastes and sewage are the prominent reasons which have encouraged the deterioration of the quality of water in the canal.

The government-led public healthcare services in Sri Lanka became a major strength in managing the COVID-19 comparatively well. However, natural hazards are a major threat to this healthcare system, as they cause severe damages, especially to curative healthcare infrastructures such as hospitals. Floods have been the major contributor to the economic loss of the Sri Lankan healthcare system. Therefore, the purpose of this study is to develop a proper flood risk assessment framework for Sri Lankan hospitals.

This research study has attempted to develop a flood vulnerability assessment tool for hospitals using the concept of Depth Damage Functions (DDFs). Flood vulnerability curves have been developed for identified critical units of hospitals considering the damage caused to building contents which are predominantly expensive medical equipment. The damage caused only by wetting was considered in generating vulnerability curves. Structured interviews were conducted with government officials in the healthcare sector to gather details on the cost and damages of medical equipment. Pilot studies were carried out in two hospitals identified as located in flood-prone areas and have previous experiences of flooding, to acquire data regarding building contents of the critical units.

The developed vulnerability curves indicate that no major damage would occur to building contents in critical units (other than the labor room) until the inundation depth reaches a value of 0.6–0.9 m (varies for each type of unit). It is also noteworthy that after a certain range in the inundation depth, the damage increases drastically, and building contents would incur total damage if the inundation depth passes a value of 1.2–1.5 m.

This study explains the initial phase of developing a flood vulnerability assessment framework for Sri Lankan hospitals. Not many studies had been carried out to assess the vulnerability of hospitals specifically for floods using vulnerability curves. The study recommends a zoning system with pre-defined vulnerability levels for critical units during a flood, which can be associated with evacuation planning as well. Further studies must be carried out to verify this system for hospitals in Sri Lanka.

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.

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.

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.

Purpose: The public health burden caused by tobacco is heavy among first-generation Chinese immigrant men whose home country has significantly higher smoking rates than the United States. The current study is part of a larger effort to pilot an mHealth tobacco cessation intervention using MMS (graphic) mobile phone technologies to target East Asian immigrant populations. Grounded in the Extended Parallel Process Model (EPPM), our specific aims were to determine what message themes, level of graphic intensity, and types of efficacy information are most appropriate and useful for mHealth interventions targeting this population.

Methodology/Approach: A qualitative study utilizing a series of focus groups (k = 5) was conducted with male adult smokers who were born in China and currently reside in the United States. The primary aim of the focus groups was to solicit reactions to a series of preliminary messages developed by the research team. A secondary aim was to gauge receptivity to the use of MMS as a vehicle for smoking cessation intervention. Participants (n = 32) were recruited from local Chinese communities in a large Mid-Atlantic metropolitan area.

Findings: Opinions about different message strategies were mixed. However, participants tended to rate messages more positively when they focused on the impact of smoking on family and loved ones, particularly children. Messages with fear-arousing images were also perceived to be effective at low frequency of exposure, but there were concerns that they may backfire at high exposure. Awareness of and interest in Quitline were low, and concrete quitting tips were perceived as more effective. Participants reported a preference for receiving messages a few times a week, and an MMS message platform was generally preferred to WeChat (a Chinese social media platform).

Implications: Our results suggest that graphic MMS messaging holds promise as an effective intervention method for this population and that EPPM is an appropriate framework to develop, test, and analyze mHealth intervention messages. While messages that focused primarily on impact on children, health, and specific quitting tips were generally found to be more effective, a mix of different types of messages that address a wide range of issues may be most appropriate for this population.

Originality/Value: This study is the first to explore the utility of graphic text messaging as an intervention method to promote smoking cessation among male Chinese immigrants. Findings from the study provide important insights for future intervention work targeting this underserved population.

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.