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Digital Marketing Capability and Its Impact on Performance of Tourism Accommodation Small and Medium-Sized Enterprises in the Central Coast Region of Vietnam
No full textDigital marketing technologies are no longer a rare and inimitable resource in tourism and hospitality. The critical question for enterprises in recent years is not the adoption of digital marketing technologies but how to leverage these technologies to create competitive advantages and improve a firm’s performance. Digital marketing capability has been recognised as crucial to employing digital marketing successfully. However, empirical studies examining this concept, particularly within the context of tourism accommodation SMEs in emerging countries like Vietnam, remain limited.
This research project investigates digital marketing capability (DMC), its critical antecedents, and its impact on the performance of tourism accommodation SMEs in the Central Coast Region of Vietnam. The research objectives are to (a) determine the critical drivers of digital marketing capability in tourism accommodation SMEs; (b) determine the impact of applying digital marketing on tourism accommodation SMEs’ performance; (c) develop a guideline for tourism accommodation SMEs in Vietnam to improve their performance using digital marketing.
With the identified research objectives, this study conducted a comprehensive literature review on digital marketing in tourism and hospitality. The extant studies were systematically selected, compared and contrasted to identify critical research themes, the achievements of extant studies, and the research gaps. The intensive literature review resulted in a conceptual framework that combined the resource-based view (RBV) and dynamic capabilities theory as theoretical lenses. The conceptual framework reflected the whole picture of digital marketing capability, its antecedents, and its consequences in the context of tourism SMEs. The conceptual framework provided the directions to investigate market orientation and RBV factors as the critical antecedents of digital marketing capability. The framework also explained how digital marketing capability can improve higher-order marketing capabilities, including customer engagement, customer relationship management, innovation, and branding. Then, both the DMC and higher-order marketing capabilities enhance the performance of tourism accommodation SMEs.
Under the pragmatism paradigm, this study applied a mixed-methods design that used the qualitative study to explore the conceptual framework and then used the quantitative study to test the theoretical frameworks.
In the qualitative study, the conceptual framework was explored and validated. Fourteen cases of tourism accommodation SMEs in three typical tourism destinations of Vietnam, including Hue, Danang, and Hoian, were investigated. The template analysis technique was applied to analyse the qualitative data from in-depth interviews with owners or managers. The data analysis process resulted in seven themes that suggest the direction to propose the theoretical frameworks for the quantitative study in the next phase.
Two theoretical frameworks were developed in the quantitative study to address the research questions. An online survey conducted via the Qualtrics platform collected responses from owners and managers of tourism accommodation SMEs in three provinces and one city of the Central Coast Region in Vietnam, including Thua Thien Hue, Quang Nam, Khanh Hoa, and Danang. The PLS-SEM was adopted to analyse the data of 249 qualified respondents.
The key findings show that human, business, and technology resources are critical for building digital marketing capability. DMC significantly enhances non-financial performance directly and indirectly via branding capability. DMC also results in customer engagement capability and service innovation capability. These two capabilities indirectly impact non-financial performance via branding capability. The findings extend the discussions on DMC and the role of higher-order marketing capabilities in explaining the mechanism of transforming digital marketing technology adoption into a firm’s performance. The findings also provide practical implications for owners and managers of tourism accommodation SMEs to improve their performance using digital marketing.</p
Development of Efficient Novel Composites for Decontamination of Heavy Metals from Water and its Validation
No full textIn recent years, the existence of various harmful heavy metal ions in water bodies has become a major concern due to their persistent nature and detrimental impact on human well-being. The presence of arsenic, lead, and cadmium is a matter of concern as these contaminants are highly toxic and widely spread. The wastewater generated from different industries is the main reason for the contamination of different water systems by these toxic heavy metals. These metals do not degrade, and even at low concentrations, they can cause severe toxicity. These metal ions are notorious for their harmful impacts on the health of humans and other living organisms. The use of activated carbon to remove metal ions is widespread, but it has some shortcomings, like low adsorption rate, poor capacity, and limited selectivity in the presence of co-ions. Hence, there is a need for more efficient materials that can overcome these limitations. Despite the development of various composite materials in recent years, there are still some gaps in understanding their regeneration potential, real-world performance, and ability to remove both anionic and cationic metal ions. Most of the materials target the removal of individual cations or anions, there is a need for the development of materials that can target both cationic as well as anionic species. Moreover, most of the studies are concentrated on bench scale studies, as the real water contains other organic and inorganic ions along with these heavy metal ions studying the potential of the composite materials in natural/ real water systems will provide a better idea about the performance of composite materials.
To fill these gaps, three materials were evaluated as potential solutions for heavy metal ion removal from water.
The primary objective of the research was to create a composite material that could effectively eliminate lead, cadmium, and arsenic. Various metal-based composite materials were developed and tested for their potential use as adsorbents. These included MnO2-modified water caltrop peel activated biochar, MgO-modified humic acid-based magnetic composite, and reduced graphene oxide-Zn/Al LDH composite.
The choice of biochar as the material for elimination was based on its porous structure and high specific surface area, making it an efficient solution for metal ion remediation. It can also be customized to meet specific application requirements. Water caltrop peel was chosen as the source material due to its availability and low cost, and its conversion into biochar provided a solution for disposing of waste biomass. However, the biochar material alone exhibited limited performance due to the absence of surface functional groups. Thus, it was further modified with MnO2 to enhance its removal efficiency. While it showed potential in removing lead and cadmium, it was inefficient in removing arsenic. This was attributed to the material's negative surface charge, which resulted in unfavourable electrostatic interactions with arsenate ions. However, the material effectively removed lead and cadmium, and further studies were conducted to determine its potential. The synthesized biochar composite exhibited a removal efficiency of 116.96 and 102.67 mg/g for lead and cadmium respectively at pH 6, contact time 30 min and adsorbent dosage 1g/L, which was higher than that reported in the literature for other biochar-based composites. The prominent mechanism for the removal of lead and cadmium by biochar-based material was electrostatic interactions and oxygen-complexation.
One of the materials that was synthesized is the MgO-HA@Fe3O4 composite. Some research papers suggest that HA@Fe3O4 has the potential to remediate both cationic and anionic metal ions due to its abundant surface functional groups and electrostatic interactions. However, it has limited adsorption capacity. To enhance the material's adsorption capacity, magnesium oxide was utilized to improve its ion exchangeability. The synthesized composite material showed promise in removing lead and arsenic ions with an adsorption capacity of 248.76 and 104.17 mg/g at pH 6, contact time of 20 min and adsorbent dosage of 0.15 g/L, and it had a negative surface charge in the studied pH range, leading to the preferential adsorption of lead ions by electrostatic interactions. However, the material's ligand complexation interactions were crucial in adsorbing arsenic ions. The prominent mechanisms for the removal of lead and arsenic were ion –exchange, oxygen-complexation and precipitation. Despite having a negative surface charge, the material's removal efficiency towards cadmium ions was suboptimal. This could be due to the differences in metal ion characteristics and their affinity towards the binding site. Cadmium ions have a larger hydration radius than lead ions, and despite the material's negative surface charge, the large hydration size of cadmium can limit their accessibility to the adsorption sites, leading to lower efficiency for cadmium removal. Additionally, cadmium is a relatively soft Lewis acid with a lower surface charge density. Therefore, hard bases present on the material surface have lower efficiency for cadmium.
The other material that was synthesized and utilized for metal ions removal was reduced graphene oxide based double layered hydroxide composite. rGO prevented the aggregation of LDH material and enhanced the surface area of the composite. The layered composite displayed high efficiency for the removal of lead, cadmium and arsenic with an adsorption capacity of 280.11, 227.27 and 178.25 mg/g for Pb(II), Cd(II) and As(V) respectively at pH 5.5, contact time of 45 min, and adsorbent dosage of 0.4 g/L. The layered structure of the material as well as its high ion exchange ability promoted the removal of both cationic (lead and cadmium) as well as the anionic (arsenate) species. The potential mechanism for the removal of metal ions involved was electrostatic interactions, ion –exchange, and oxygen-complexation.
Water has a wide range of analytes and complex matrices. Thus, the concentration of elements varies from trace to percentage level in water starting from ground/surface water to tap water being used for household work. The present study is confined to real water samples from different sources like river/tank/tap water but not industrial wastewater. So, the validation study is carried out with the novel synthesized adsorbents to optimize their efficiency towards uptake of the contaminants up to a certain level. However, industrial waste is specific to the area with a very high concentration level of contaminants which will be a vast study.
The materials displayed potentially good performance in real water samples displaying their potential for real world applications.The study found that rGO-Zn/Al LDH is the most effective material for removing lead, cadmium, and arsenic from water among the synthesized materials. However, further optimization of the synthesis conditions is still required to improve its performance. One possible approach for enhancing the material's removal capacity is through chemical modification, which can increase its surface area and functional groups. Further research is needed to optimize the synthesis conditions by varying factors such as temperature and time. This study provides insights into the mechanism behind the removal of lead, cadmium, and arsenic and suggests the potential of environmentally friendly adsorbents for metal ion remediation in water, which can support the development of more effective adsorbents in the future.</p
Beyond Accuracy: Understanding and Modeling the Role of User Conformity in Recommender Systems
No full textRecommender Systems (RSs), particularly Collaborative Filtering (CF) models, commonly employed in e-commerce domains, face challenges in adapting to sudden population-scale events, such as the COVID-19 pandemic and annual Black Friday shopping events.
My initial study uncovers a phenomenon termed "population-scale concept drift" in user interaction behavior. During pandemic-like events, individuals exhibit imitative behavior, leading to abrupt shifts in their interactions with CF systems. Grounded in the theory of user needs, our simulation-based framework, the Need Evolution Simulator (NEST), utilizing model-agnostic reinforcement learning, investigates the impact of rapid drifts in user behavior on RSs. By simulating the evolution of human needs, we analyze macro-trends in population dynamics, providing insights into the influence of events on CF models. Experimental results reveal an initial impact on CF performance during the early stages of events, followed by an exacerbated population herding effect. This effect introduces a popularity bias, benefiting some users but compromising the overall user experience. To address this, we propose an adaptive ensemble method that optimally adapts algorithms to different event stages.
In our second study, we comprehensively examine temporal dynamics in user behavior and their impact on recommendation systems under both pandemic-related contingencies and calm circumstances. Drawing on user need theory, we highlight the irrationality of individuals' decision-making, often influenced by peers in the same social community. Our community detection-based evaluation approach effectively identifies collaborative concept drifts among users, shedding light on the evolution of herds in RS under extreme outlier events and calm environments. Findings indicate that CF models may achieve high accuracy by recommending popular items during population-level herd behavior, limiting recommendation diversity. Traditional evaluation methods focusing solely on accuracy may not comprehensively measure RS performance.
Additionally, our third work investigates the influence of user conformity behavior on RSs. Conventional RSs assume user behavior is solely driven by individual interests, overlooking the impact of peer influence and resulting conformity behavior. Solutions that indiscriminately eliminate such bias may depersonalize recommendations. We propose the Temporal Conformity-aware Hawkes Network (TCHN) model, based on Hawkes processes, to identify two forms of conformity behavior: informational conformity and normative conformity. TCHN disentangles user interest and conformity in a personalized manner, incorporating attention-based methods to model stable and volatile dynamics. Experiments on real-world datasets demonstrate varying conformity scales among users, with TCHN exhibiting advantages in accuracy and diversity in recommendations.</p
Life Cycle Assessment in Circular Economy for Built Environment
No full textBuilt environment, also referred to as the product of construction industry, encompasses all buildings and infrastructure and exerts tremendous impact on the natural environment. The concept of the circular economy is increasingly being applied to improve resource efficiency and reduce environmental burdens of the built environment. During the implementation of a circular economy, adopting life cycle assessment is necessary to evaluate the trade-offs between circularity and sustainability.
This thesis systematically investigates the application of life cycle assessment in the circular economy for the built environment. The research identifies inherent limitations of life cycle assessment methods that hinder their applications and compares various standards of life cycle assessment, quantifying the inconsistency between the assessment results based on these standards. New methods for circular economy assessment are developed to evaluate material circularity and environmental sustainability of the built environment simultaneously. By incorporating reliability methods, circular economy indexes are developed to quantify the probabilities of unsatisfactory circularity, sustainability, and overall circular economy performances. Various uncertain variables, such as structural degradation rate, recycling rate, and embodied impact coefficients, are considered in different life cycle stages of the built environment, such as product, construction, use, and end-of-life stages. In addition, the developed method and indexes are further demonstrated through multiple case studies. A wide range of circular economy applications are considered, such as mass timber structures, design for disassembly, and closed- and open-loop recycling.
This research highlights that incorporating life cycle thinking and adopting circular economy assessment can facilitate the management and monitoring of circular economy applications in the built environment. This research contributes to the knowledge of circular economy assessment as well as its implementation throughout the life cycle of the built environment.</p
Determinants and consequences of sustainable development goals disclosure: International evidence
No full textThe study examines the determinants and consequences of firm-level Sustainable Development Goals (SDG) disclosure using a sample of 6941 firm-year observations from 30 countries during 2016–2019. Based on 17 SDG indicators developed by the United Nations (UN), the study forms an SDG Disclosure Index. The findings reveal that approximately 48.40% of firms in the sample had active stakeholder engagement programs, 53.90% maintained a sustainability committee, and 62.60% issued standalone sustainability reports. The findings indicate that Environmental, Social and Governance (ESG) performance, stakeholder engagement, and the issuance of standalone sustainability reports positively influence firm-level SDG disclosure. Moreover, the study finds a positive association between higher levels of SDG disclosure and increased firm value. Our findings are robust using a battery of robustness tests. Given the growing global focus on SDGs and the extent of SDG disclosure by firms, this study's findings hold significant implications for decision-makers and other stakeholders
Optimization of flame retardancy and mechanical properties of cotton fabrics with D-glucosamine hydrochloride and phosphorus-containing polyol coating
No full textThis study investigates the utilization of D-glucosamine hydrochloride (DGH) as a carbon donor in intumescent coating systems and as a nitrogen-based flame retardant, leveraging its ability to release ammonia under elevated temperatures to inhibit combustion. The use of phosphorus-containing polyol (PPO) complements the formulation by promoting the formation of char and further inhibiting combustion. The flame retardancy and thermal stability were assessed by using the Vertical Flame Test (ASTM D6413), Limiting Oxygen Index (LOI) Test and Cone Calorimeter Test. A substantial improvement in the treated fabric was reported when a ratio of 1:2 wt/wt. (DGH:PPO) was used in the coating formulation, reporting an LOI value of 30 % and a reduction in the peak heat release rate (p-HRR) of around 88 % compared to the control sample. Additionally, an improvement in the fabric flexibility was attained when concentrations between 5 wt% to 7.5 wt% of DGH were added
Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues
No full textThe rapid growth of the Internet of Things (IoT) has led to its widespread adoption in various industries, enabling enhanced productivity and efficient services. Integrating IoT systems with existing enterprise application systems has become common practice. However, this integration necessitates reevaluating and reworking current Enterprise Architecture (EA) models and Expert Systems (ES) to accommodate IoT and cloud technologies. Enterprises must adopt a multifaceted view and automate various aspects, including operations, data management, and technology infrastructure. Machine Learning (ML) is a powerful IoT and smart automation tool within EA. Despite its potential, a need for dedicated work focuses on ML applications for IoT services and systems. With IoT being a significant field, analyzing IoT-generated data and IoT-based networks is crucial. Many studies have explored how ML can solve specific IoT-related challenges. These mutually reinforcing technologies allow IoT applications to leverage sensor data for ML model improvement, leading to enhanced IoT operations and practices. Furthermore, ML techniques empower IoT systems with knowledge and enable suspicious activity detection in smart systems and objects. This survey paper conducts a comprehensive study on the role of ML in IoT applications, particularly in the domains of automation and security. It provides an in-depth analysis of the state-of-the-art ML approaches within the context of IoT, highlighting their contributions, challenges, and potential applications
Self-Charged Dual-Photoelectrode Vanadium–Iron Energy Storage Battery
No full textThe efficient utilization of solar energy in battery systems has emerged as a crucial strategy for promoting green and sustainable development. In this study, an innovative dual-photoelectrode vanadium–iron energy storage battery (Titanium dioxide (TiO2) or Bismuth vanadate (BiVO4) as photoanodes, polythiophene (pTTh) as photocathode, and VO2+/Fe3+ as redox couples.) is proposed, which can autonomously charge under sunlight. The dual-photoelectrode structure enables the efficient harnessing of solar energy. All processes are spontaneous and do not require external power sources. It is noteworthy that the vanadium–iron energy storage battery demonstrates excellent stability and remarkably low cost. The results show that the combinations of TiO2-pTTh and BiVO4-pTTh as photoelectrodes achieve spontaneous conversion rates of 29.17% and 25.46% for VO2+ and 25.6% and 23% for Fe3+ after 4 h of light charging. This study offers a promising solution for the development of large-scale, low-cost solar energy storage batteries
EZH2 inhibitors promote β-like cell regeneration in young and adult type 1 diabetes donors
No full textβ-cells are a type of endocrine cell found in pancreatic islets that synthesize, store and release insulin. In type 1 diabetes (T1D), T-cells of the immune system selectively destroy the insulin-producing β-cells. Destruction of these cells leads to a lifelong dependence on exogenous insulin administration for survival. Consequently, there is an urgent need to identify novel therapies that stimulate β-cell growth and induce β-cell function. We and others have shown that pancreatic ductal progenitor cells are a promising source for regenerating β-cells for T1D owing to their inherent differentiation capacity. Default transcriptional suppression is refractory to exocrine reaction and tightly controls the regenerative potential by the EZH2 methyltransferase. In the present study, we show that transient stimulation of exocrine cells, derived from juvenile and adult T1D donors to the FDA-approved EZH2 inhibitors GSK126 and Tazemetostat (Taz) influence a phenotypic shift towards a β-like cell identity. The transition from repressed to permissive chromatin states are dependent on bivalent H3K27me3 and H3K4me3 chromatin modification. Targeting EZH2 is fundamental to β-cell regenerative potential. Reprogrammed pancreatic ductal cells exhibit insulin production and secretion in response to a physiological glucose challenge ex vivo. These pre-clinical studies underscore the potential of small molecule inhibitors as novel modulators of ductal progenitor differentiation and a promising new approach for the restoration of β-like cell function
Experimental Phase Equilibria and Liquidus of CaO-Al2O3-SiO2-Na2O-B2O3 Slags Relevant to E-waste Processing
No full textTernary master slags based on the CaO-Al2O3-SiO2 system with CaO/SiO2 (C/S) ratio 0.3–1.0 were doped with 5–20 wt pct of anhydrous borax (Na2B4O7) to study the phase equilibria of the quinary CaO-Al2O3-SiO2-Na2O-B2O3 slag system within the temperature range 1050–1350 °C. This research uses the well-known method of high temperature equilibration of slags followed by rapid quenching. The quenched slag samples were examined using a Scanning Electron Microscope (SEM) and Wavelength Dispersive Electron Probe Microanalysis (WD-EPMA) technique to determine the structure and to analyse the chemistry of the phases in equilibrium. The primary phase of 15 slag compositions was identified and the liquidus temperature of the slags were determined within an uncertainty range of ±10–20 °C by using an iterative approach. Overall, the liquidus temperature of slags decreased with increasing borax content with the highest liquidus reduction observed in slags having a C/S ratio 0.3 and the lowest in the slag series with C/S ratio of 1.0. A comparative analysis of the effect of borax, Na2O and B2O3 on the liquidus temperature of slags is discussed. Results indicated that although borax reduces the liquidus of ternary CaO-Al2O3-SiO2 slags, the addition of B2O3 individually showed more prominent effects than borax in reducing the slag liquidus. Anorthite (CaO.Al2O3.2SiO2), pseudowollastonite (CaO.SiO2) and gehlenite (2CaO.Al2O3.SiO2) primary phases were identified