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1 – 10 of 63B.V. Binoy, M.A. Naseer and P.P. Anil Kumar
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Sumant Sharma, Deepak Bajaj and Raghu Dharmapuri Tirumala
Land value in urban areas in India is influenced by regulations, bylaws and the amenities associated with them. Planning interventions play a significant role in enhancing the…
Abstract
Purpose
Land value in urban areas in India is influenced by regulations, bylaws and the amenities associated with them. Planning interventions play a significant role in enhancing the quality of the neighbourhood, thereby resulting in a change in its value. Land is a distinct commodity due to its fixed location, and planning interventions are also specific to certain locations. Consequently, the factors influencing land value will vary across different areas. While recent literature has explored some determinants of land value individually, conducting a comprehensive study specific to each location would be more beneficial for making informed policy decisions. Therefore, this article aims to examine and identify the critical factors that impact the value of residential land in the National Capital Territory of Delhi, India.
Design/methodology/approach
The study employed a combination of semi-structured and structured interview methods to construct a Relative Importance Index (RII) and ascertain the critical determinants affecting residential land value. A sample of 36 experts, comprising property valuers, urban planners and real estate professionals operating within the National Capital Territory of Delhi, India, were selected using snowball sampling techniques. Subsequently, rank correlation and ANOVA methods were employed to evaluate the obtained results.
Findings
Location and stage of urban development are the most critical determinants in determining residential land values in the National Capital Territory of Delhi, India. The study identifies a total of 13 critical determinants.
Practical implications
A scenario planning approach can be developed to achieve an equitable distribution of values and land use entropy. A land value assessment model can also be developed to assist professional valuers.
Originality/value
There has been a lack of emphasis on assessing the impact of planning interventions and territorial regulation on land values in the context of Delhi. This study will contribute to policy decision-making by developing a rank list of planning-based determinants of land value.
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Hassan Shuaibu Liman, Abdul-Rasheed Amidu and Deborah Levy
The complexity of property valuation, coupled with valuers’ cognitive limitations, makes some degree of error inevitable in valuations. However, given the crucial role that…
Abstract
Purpose
The complexity of property valuation, coupled with valuers’ cognitive limitations, makes some degree of error inevitable in valuations. However, given the crucial role that valuations play in the efficient functioning of the economy, there is a need for continuous improvement in the reliability of reported values by enhancing the quality of the decision-making process. The purpose of this paper is to review previous research on valuation decision-making, with particular interest in examining the approaches to improving the quality of valuation decisions and identifying potential areas for further research.
Design/methodology/approach
The paper adopts a narrative approach to review 42 research articles that were obtained from Scopus and Web of Science databases and through author citation searches.
Findings
Our findings show that existing literature is skewed towards examining the use of technology in the form of decision support systems (DSS), with limited research attention on non-technological (i.e. behavioural) approaches to improving the quality of valuation decisions. We summarise the non-technological approaches and note that much of the discussions on these approaches often appear as recommendations arising from other studies rather than original investigations in their own rights.
Practical implications
We conclude that studies investigating the effectiveness of the non-technological approaches to improving valuation decision-making are lacking, providing various avenues for further research.
Originality/value
This paper presents the first attempt to provide a comprehensive overview of non-technological approaches to improving the quality of valuation decisions.
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This paper aims to determine the implications of Covid-19 on the livelihood of marine fishermen. It gives a concrete picture of how vulnerable communities like marine fishermen…
Abstract
Purpose
This paper aims to determine the implications of Covid-19 on the livelihood of marine fishermen. It gives a concrete picture of how vulnerable communities like marine fishermen are affected due to the lockdown policies. The paper examines these communities' present status and the extent of vulnerability during the post-Covid period.
Design/methodology/approach
The study uses an exploratory research design to find the solution to the research problem. 298 samples were collected and analysed within a sustainable livelihood theoretical framework. The scope of the study is limited to marine fishermen in Kerala, residing in six districts out of the nine coastal districts. The impact of the lockdown on income was analysed using paired t-test and results linked with the theory.
Findings
The study has done an empirical analysis for three periods: before lockdown, lockdown and after lockdown, to identify the impact of lockdown on marine fishermen. The study's significant findings are that these fishermen's livelihood is at risk during the post-lockdown period, and many families are moving into a “debt-trap”.
Research limitations/implications
Policymakers can develop appropriate policy strategies to enhance the livelihood assets of vulnerable communities to include them in a sustainable framework.
Originality/value
Only a few studies are highlighting the impact of Covid-19 on vulnerable communities in India. The effects of climate change on the marine ecosystem are already endangering marine fisher folks' livelihoods. In this light, it is vital to study the extent of the impact of income shock on the livelihood assets of marine fishermen due to the lockdown policy implemented in the State to prevent the spread of Covid-19.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2023-0192
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Sanjeev Yadav, Sunil Luthra, Anil Kumar, Rohit Agrawal and Guilherme F. Frederico
This study aims to explore the mediating role of digital technologies-based supply chain integrating (SCI) strategies on the agri-supply chain performance (SCP) and firm…
Abstract
Purpose
This study aims to explore the mediating role of digital technologies-based supply chain integrating (SCI) strategies on the agri-supply chain performance (SCP) and firm performance (FP). This research has introduced recently emerged digital technologies such as Internet of Things (IoT). Further, based on theoretical support and an extensive literature review, this research has proposed some hypotheses, which have been quantitatively validated for their significance.
Design/methodology/approach
A conceptual model was formulated based on an extensive literature review. Data for this research were gathered from a survey completed by 119 respondents from different departments of agri-firms. Further, partial least square (PLS)-based structured equation modelling (SEM) was used to test the proposed hypothetical model.
Findings
The results confirm that IoT-based digital technologies and supply chain processes (organization integration [OI], information sharing and customer integration [CI]) have a significant positive correlation. Furthermore, supply chain practices are positively associated with SCP. Finally, it has been found that FP is positively impacted by SCP.
Research limitations/implications
This research is used to analyse the mediating impacts of digital supply chain processes as a linking strategy for SCP and FP. For practical purposes, this research provides investment decisions for implementing digital technologies in SC strategies. The findings have proposed implications for managers and practitioners in agri-firms based on existing theories: contingency theory (CT) and relational view theory. Also, this study suggests the deployment of smarter electronically based tags and readers, which improve the data analytics capabilities based on auto-captured data. Thus, the availability of quality information improves the data-driven decisional capabilities of managers at company level.
Originality/value
This is a unique and original study exploring the relationship between digitalization, resilient agri-food supply chain (AFSC) management practices and firm performance. This research may be extended to other industries in view of the results from SCP and impact of digitalization.
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Anil Kumar Swain, Aleena Swetapadma, Jitendra Kumar Rout and Bunil Kumar Balabantaray
The objective of the proposed work is to identify the most commonly occurring non–small cell carcinoma types, such as adenocarcinoma and squamous cell carcinoma, within the human…
Abstract
Purpose
The objective of the proposed work is to identify the most commonly occurring non–small cell carcinoma types, such as adenocarcinoma and squamous cell carcinoma, within the human population. Another objective of the work is to reduce the false positive rate during the classification.
Design/methodology/approach
In this work, a hybrid method using convolutional neural networks (CNNs), extreme gradient boosting (XGBoost) and long-short-term memory networks (LSTMs) has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. To extract features from non–small cell lung carcinoma images, a three-layer convolution and three-layer max-pooling-based CNN is used. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types. The accuracy of the proposed method is 99.57 per cent, and the false positive rate is 0.427 per cent.
Findings
The proposed CNN–XGBoost–LSTM hybrid method has significantly improved the results in distinguishing between adenocarcinoma and squamous cell carcinoma. The importance of the method can be outlined as follows: It has a very low false positive rate of 0.427 per cent. It has very high accuracy, i.e. 99.57 per cent. CNN-based features are providing accurate results in classifying lung carcinoma. It has the potential to serve as an assisting aid for doctors.
Practical implications
It can be used by doctors as a secondary tool for the analysis of non–small cell lung cancers.
Social implications
It can help rural doctors by sending the patients to specialized doctors for more analysis of lung cancer.
Originality/value
In this work, a hybrid method using CNN, XGBoost and LSTM has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. A three-layer convolution and three-layer max-pooling-based CNN is used to extract features from the non–small cell lung carcinoma images. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types.
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Masoud Shayganmehr, Anil Kumar, Jose Arturo Garza-Reyes and Edmundas Kazimieras Zavadskas
In this study, a novel framework was proposed to assess the trust in e-government (e-Gov) services under an uncertain environment. The proposed framework was applied in Iranian…
Abstract
Purpose
In this study, a novel framework was proposed to assess the trust in e-government (e-Gov) services under an uncertain environment. The proposed framework was applied in Iranian municipality websites of e-Gov services to evaluate the readiness score of trust in e-Gov services.
Design/methodology/approach
A unique hybrid research methodology was proposed. In the first phase, a comprehensive set of indices were determined from an extensive literature review and finalized by employing the fuzzy Delphi method. In the second phase, interval-valued intuitionistic fuzzy set (IVIFS) -was utilized to model the problem's uncertainty with analytic called IVIFS- hierarchy process (AHP) to determine the importance of indices and indicators by assigning the weights. In the third phase, the fuzzy evaluation method (FEM) is followed for assessing the readiness score of indices in case studies.
Findings
The findings indicated that “Trust in government” is the most significant index affecting citizen's trust in e-Gov services while “Maintenance and support” has the least impact on user's intention to use e–Gov services.
Research limitations/implications
The study contributes by introducing a unique research methodology that integrates three phases, including fuzzy Delphi, IVIFS AHP and fuzzy evaluation method. Moreover, the fuzzy sets theory helps to reach a more accurate result by modeling the inherent ambiguity of indicators and indices. Interval-valued intuitionistic fuzzy models the ambiguity of experts' judgments in an interval.
Practical implications
The study helps policy makers to monitor wider aspects of trust in e-Gov services as well as understanding their importance. The study enables policy makers to apply the framework to any potential case studies to evaluate the readiness score of indices and recognizing strengths and weakness of trust dimensions as well as recommending advice for improving the situation.
Originality/value
The study is one of the few to indicate significant indices of trust in e-Gov services in developing countries. The study shows the importance of indicators and indices by assigning a weight. Additionally, the framework can assess the readiness score of various case studies.
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Yuvika Gupta, Farheen Mujeeb Khan, Anil Kumar, Sunil Luthra and Maciel M. Queiroz
With the emergence of big data analytics and the importance of analytics-driven decisions, the travel industry is swiftly jumping on and adopting the bandwagon. However, research…
Abstract
Purpose
With the emergence of big data analytics and the importance of analytics-driven decisions, the travel industry is swiftly jumping on and adopting the bandwagon. However, research in this domain is limited. Accordingly, the present research seeks to understand how big data analytics capabilities (BDAC) add value to tourism supply chains (TSCs) and can dynamic capabilities (DC) improve the triple bottom line.
Design/methodology/approach
Data from 218 valid responses were collected from different Indian tourism industry units. Confirmatory factor analysis (CFA) was applied to confirm the constructs, followed by partial least squares structural equation modelling (PLS-SEM) to check the mediating effect of DC on TSCs performance.
Findings
The findings show that BDAC significantly influence the performance of TSCs and that DC plays a critical role in strengthening the impact of BDAC on TSCs' economic performance. These results corroborate that DC plays a key moderating role.
Research limitations/implications
This study contributes significantly to the tourism sector in India, where tourism is a key contributor to the country's gross domestic product. Theoretically, this study contributes to the resource-based view (RBV) and practically encourages professionals in the tourism sector to promote the use of BDAC to enhance the performance of TSCs.
Originality/value
The originality of the study is that it has tried to comprehend the moderating role of dynamic capabilities which impact BDAC to improve TSC performance.
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Manpreet Kaur, Amit Kumar and Anil Kumar Mittal
In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered…
Abstract
Purpose
In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.
Design/methodology/approach
To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.
Findings
The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.
Originality/value
To the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.
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Sonika Jha, Anil Kumar Singh and Sriparna Basu
The purpose of this paper is to provide a systematic review of literature on corporate engagement with start-ups (CEWS) by identifying the modes, contexts, antecedents, barriers…
Abstract
Purpose
The purpose of this paper is to provide a systematic review of literature on corporate engagement with start-ups (CEWS) by identifying the modes, contexts, antecedents, barriers and outcomes. As an emerging field, CEWS presently has no such review available which will help in building consensus within the field and shape future research directions.
Design/methodology/approach
The study followed a two-phased systematic review of literature. Three research databases (i.e. Web of Science, ScienceDirect and SCOPUS) were accessed to gather and conduct the review. Of the total 379 papers retrieved, 63 total relevant papers were studied and analysed. The exhaustive review of literature helped to uncover the contexts, perspectives, antecedents, outcomes and barriers reported across the different modes of CEWS.
Findings
The study highlighted the five prominent modes of CEWS favoured by large corporations and start-ups. It found that the large corporations and start-ups associate with one another on the basis of complementarities of activities, resources and motives to pursue their strategic orientations. The engagements also face barriers on the ground, such as incompatibility of goals, power imbalances, cultural differences and weak engagement plans. Most important contexts seen were the high-technology industries in the developed economies like the USA and Europe. It also found that ecosystem creation, accessing innovation and corporate strategy have been preferred as the most productive modes of CEWS in the literature.
Practical implications
This review provides practitioners with a detailed list of the modes and drivers of CEWS. Subsequently, the barriers that need to be managed to successfully execute a specific mode of engagement. This shall enable the practitioners in developing and adopting the best practices while engaging with the start-ups to better facilitate the outcomes of CEWS.
Originality/value
To the best of the authors’ knowledge, there is no systematic literature review available in the domain of CEWS – thus, this study makes an important methodological contribution to the field. By consolidating the fragmented yet growing knowledge on CEWS, the study presents a detailed understanding of what drives and obstructs the engagement between large corporations and start-ups.
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