Search results

Land value varies at a micro level depending on the location’s economic, geographical and political determinants. The purpose of this study is to present a comprehensive assessment of the determinants affecting land value in the Indian city of Thiruvananthapuram in the state of Kerala.

The global influence of the identified 20 explanatory variables on land value is measured using the traditional hedonic price modeling approach. The localized spatial variations of the influencing parameters are examined using the non-parametric regression method, geographically weighted regression. This study used advertised land value prices collected from Web sources and screened through field surveys.

Global regression results indicate that access to transportation facilities, commercial establishments, crime sources, wetland classification and disaster history has the strongest influence on land value in the study area. Local regression results demonstrate that the factors influencing land value are not stationary in the study area. Most variables have a different influence in Kazhakootam and the residential areas than in the central business district region.

This study confirms findings from previous studies and provides additional evidence in the spatial dynamics of land value creation. It is to be noted that advanced modeling approaches used in the research have not received much attention in Indian property valuation studies. The outcomes of this study have important implications for the property value fixation of urban Kerala. The regional variation of land value within an urban agglomeration shows the need for a localized method for land value calculation.

Increasing overhang of serviced apartments poses a serious concern to the national property market. This study aims to examine the impacts of macroeconomic determinants, namely, gross domestic product (GDP), consumer confidence index (CF), existing stocks (ES), incoming supply (IS) and completed project (CP) on serviced apartment price changes.

To achieve more accurate, quality price changes, a serviced apartment price index (SAPI) was constructed through a self-developed hedonic price index model. This study has collected 1,567 transaction data in Kuala Lumpur, covering 2009Q1–2018Q4 for price index construction and data were analysed using the vector autoregressive model, the vector error correction model and the fully modified ordinary least squares (OLS) (FMOLS).

Results of the regression model show that only GDP, ES and IS were significantly associated with SAPI, with an R² of 0.7, where both ES and IS have inverse relationships with SAPI. More precisely, it is predicted that the price of serviced apartments will be reduced by 0.56% and 0.21% for every 1% increase in ES and IS, respectively.

Therefore, government monitoring of serviced apartments’ future supply is crucial by enforcing land use-planning regulations via stricter development approval of serviced apartments to safeguard and achieve more stable property prices.

By adopting an innovative approach to estimating the response of price change to supply and demand in a situation where there is no price indicator for serviced apartments, the study addresses the knowledge gap, especially in terms of understanding what are the key determinants of, and to what extent they influence, the SAPI.

We present configurational theorising as a novel approach to developing middle-range theory in two steps: (1) we illustrate configurational theorising as a new form of supply chain inquiry by connecting its philosophical assumptions with a methodological execution, and (2) we generate new insights underpinning a middle-range theory for supply chain resilience.

We synthesise information from a range of sources and invoke ‘critical realism” to suggest a five-phase configurational theorising roadmap to develop middle-range theory. We demonstrate this roadmap to explain supply chain resilience by analysing qualitative data from 22 organisations within the Australian food supply chain.

Coopetition and supply chain collaboration are necessary causal conditions, but they need to combine with either supply chain agility or multi-sourcing strategy to build supply chain resilience. Asymmetrical analyses showed that the simultaneous absence of supply chain collaboration, supply chain agility and multi-sourcing results in low supply chain resilience, but coopetition was indifferent to low supply chain resilience. Similarly, high supply chain resilience is possible with the non-presence of supply chain agility and multi-sourcing.

The configurational middle-range theorising roadmap presented and empirically tested in this paper constitutes a substantial advancement to both theory and the methodological domain.

This is the first attempt at developing a middle-range theory for supply chains by explicitly drawing on configurational theorising.

The rise of emerging economies in the innovation landscape has often been attributed to the positive spillovers of innovation capabilities from multinational corporations (MNCs). However, it is less certain that their innovative capabilities imported from the home country function effectively in the host country from the outset. This study examines the performance of the innovation capabilities of MNC subsidiaries in emerging economies over time by considering the gradual process of their learning about host countries.

We employed stochastic frontier analysis to measure innovation capabilities, our focal construct. For regression analysis, we applied the Mundlak estimator, a variant of the fixed-effects panel estimator, to a sample comprising subsidiaries of MNCs from technologically advanced nations operating in Korea between 2006 and 2016.

Our results indicate that the innovation capabilities of MNC subsidiaries initially underperform those of local firms but improve over time, eventually surpassing the capabilities of their local counterparts. Furthermore, our findings reveal that institutional distance amplifies the underperformance of the innovation capabilities of MNC subsidiaries.

This study contributes to the literature by extending both theoretical development and empirical measurement of innovation capabilities in cross-national settings. Additionally, it deepens our understanding of whether and how MNC subsidiaries adapt their innovation capabilities to the local market environment.

Polymethyl methacrylate (PMMA) is a remarkable biocompatible material for bone cement and regeneration. It is also considered 3D printable but requires in-depth process–structure–properties studies. This study aims to elucidate the mechanistic effects of processing parameters and sterilization on PMMA-based implants.

The approach comprised manufacturing samples with different raster angle orientations to capitalize on the influence of the filament alignment with the loading direction. One sample set was sterilized using an autoclave, while another was kept as a reference. The samples underwent a comprehensive characterization regimen of mechanical tension, compression and flexural testing. Thermal and microscale mechanical properties were also analyzed to explore the extent of the appreciated modifications as a function of processing conditions.

Thermal and microscale mechanical properties remained almost unaltered, whereas the mesoscale mechanical behavior varied from the as-printed to the after-autoclaving specimens. Although the mechanical behavior reported a pronounced dependence on the printing orientation, sterilization had minimal effects on the properties of 3D printed PMMA structures. Nonetheless, notable changes in appearance were attributed, and heat reversed as a response to thermally driven conformational rearrangements of the molecules.

This research further deepens the viability of 3D printed PMMA for biomedical applications, contributing to the overall comprehension of the polymer and the thermal processes associated with its implementation in biomedical applications, including personalized implants.

The purpose of this paper was to investigate how variations in the geometry of silicon chips and the presence of surface defects affect their static bending properties. By comparing the bending radius and strength across differently sized and treated chips, the study sought to understand the underlying mechanics that contribute to the flexibility of silicon-based electronic devices. This understanding is crucial for the development of advanced, robust and adaptable electronic systems that can withstand the rigors of manufacturing and everyday use.

This study explores the impact of silicon chip geometry and surface defects on flexibility through a multifaceted experimental approach. The methodology included preparing silicon chips of three distinct dimensions and subjecting them to thinning processes to achieve a uniform thickness verified via scanning electron microscopy (SEM). Finite element method (FEM) simulations and a series of four-point bending tests were used to analyze the bending flexibility theoretically and experimentally. The approach was comprehensive, examining both the intrinsic geometric factors and the extrinsic influence of surface defects induced by manufacturing processes.

The findings revealed a significant deviation between the theoretical predictions from FEM simulations and the experimental outcomes from the four-point bending tests. Rectangular-shaped chips demonstrated superior flexibility, with smaller dimensions leading to an increased bending strength. Surface defects, identified as critical factors affecting flexibility, were analyzed through SEM and atomic force microscopy, showing that etching processes could reduce defect density and enhance flexibility. Notably, the study concluded that surface defects have a more pronounced impact on silicon chip flexibility than geometric factors, challenging initial assumptions and highlighting the need for defect minimization in chip manufacturing.

This research contributes valuable insights into the design and fabrication of flexible electronic devices, emphasizing the significant role of surface defects over geometric considerations in determining silicon chip flexibility. The originality of the work lies in its holistic approach to dissecting the factors influencing silicon chip flexibility, combining theoretical simulations with practical bending tests and surface defect analysis. The findings underscore the importance of optimizing manufacturing processes to reduce surface defects, thereby paving the way for the creation of more durable and flexible electronic devices for future technologies.

In pursuit of affordable and nutrient-rich food alternatives, the symbiotic culture of bacteria and yeast (SCOBY) emerged as a selected food ink for 3D printing. The purpose of this paper is to harness SCOBY’s potential to create cost-effective and nourishing food options using the innovative technique of 3D printing.

This work presents a comparative analysis of the printability of SCOBY with blends of wheat flour, with a focus on the optimization of process variables such as printing composition, nozzle height, nozzle diameter, printing speed, extrusion motor speed and extrusion rate. Extensive research was carried out to explore the diverse physical, mechanical and rheological properties of food ink.

Among the ratios tested, SCOBY, with SCOBY:wheat flour ratio at 1:0.33 exhibited the highest precision and layer definition when 3D printed at 50 and 60 mm/s printing speeds, 180 rpm motor speed and 0.8 mm nozzle with a 0.005 cm³/s extrusion rate, with minimum alteration in colour.

Food layered manufacturing (FLM) is a novel concept that uses a specialized printer to fabricate edible objects by layering edible materials, such as chocolate, confectionaries and pureed fruits and vegetables. FLM is a disruptive technology that enables the creation of personalized and texture-tailored foods, incorporating desired nutritional values and food quality, using a variety of ingredients and additions. This research highlights the potential of SCOBY as a viable material for 3D food printing applications.

This research paper aims to evaluate the sustainability knowledge and background of students, staff and faculty regarding current university sustainability practices and individual behaviors at Central Michigan University (CMU); to compare sustainability background and knowledge based on academic discipline of enrollment or employment; and to assess sustainability awareness and interest of the campus community to guide future sustainability initiatives and resources at CMU.

An electronic cross-sectional survey was used to collect anonymous responses through Qualtrics, and then results were analyzed through SPSS. Analyses were performed based on the academic structures at CMU.

This research has found that students in STEM fields are more inclined to have pro-sustainability attitudes, knowledge and behaviors, compared to those studying the arts and business. Additionally, results indicate that there is a significant difference in knowledge between the students, and the staff and faculty respondents regarding sustainability knowledge and application, with the staff and faculty consistently demonstrating more pro-sustainability knowledge and behavior.

While research has previously been conducted on sustainability attitudes and behaviors, this research is unique because it ties sustainability knowledge to academic discipline. Additionally, it serves to gauge which sustainability programs and topics members of the campus community are most interested in, and which areas they are most willing to support.

The primary objective of this research is to provide evidence that there are two distinct layers of investor sentiments that can affect asset valuation models. The first is general market-wide sentiments, while the second is biased approaches toward specific assets.

To achieve the goal, the authors conducted a multi-step analysis of stock returns and constructed complex sentiment indices that reflect the optimism or pessimism of stock market participants. The authors used panel regression with fixed effects and a sample of the US stock market to improve the explanatory power of the three-factor models.

The analysis showed that both market-level and stock-level sentiments have significant contributions, although they are not equal. The impact of stock-level sentiments is more profound than market-level sentiments, suggesting that neglecting the stock-level sentiment proxies in asset valuation models may lead to severe deficiencies.

In contrast to previous studies, the authors propose that investor sentiments should be measured using a multi-level factor approach rather than a single-factor approach. The authors identified two distinct levels of investor sentiment: general market-wide sentiments and individual stock-specific sentiments.

Inclination angle has been reported to have an enhancing effect on the thermal-hydraulic characteristics and entropy of some thermal systems. Therefore, this paper aims to numerically investigate the effects of inclination angle, volume concentration and Reynolds number on the thermal and hydraulic characteristics and entropy generation rates of water-based Al₂O₃ nanofluids through a smooth circular aluminum pipe in a turbulent flow.

A constant heat flux of 2,000 Watts is applied to the circular surface of the tube. Reynolds number is varied between 4,000 and 20,000 for different volume concentrations of alumina nanoparticles of 0.5%, 1.0% and 2.0% for tube inclination angles of ±90o, ±60o, ±45o, ±30o and 0o, respectively. The simulation is performed in an ANSYS Fluent environment using the realizable kinetic energy–epsilon turbulent model.

Results show that +45o tube orientation possesses the largest thermal deviations of 0.006% for 0.5% and 1.0% vol. concentrations for Reynolds numbers 4,000 and 12,000. −45o gives a maximum pressure deviation of −0.06% for the same condition. The heat transfer coefficient and pressure drop give maximum deviations of −0.35% and −0.39%, respectively, for 2.0% vol. concentration for Reynolds number of 20,000 and angle ±90o. A 95%–99.8% and 95%–98% increase in the heat transfer and total entropy generation rates, respectively, is observed for 2.0% volume concentration as tube orientation changes from the horizontal position upward or downward.

Research investigating the effect of inclination angle on thermal-hydraulic performance and entropy generation rates in-tube turbulent flow of nanofluid is very scarce in the literature.