Purpose: Property management in commercial real estate (CRE) is an important operational function that needs to be managed because it brings large cost implications to the organization. As India aspires to become a developed real estate market, analysis of the growing importance of automating property services and technology acceptance by stakeholders are two key concerns that need to be explicitly addressed. This study aims to examine the extent of property technology (PropTech) adoption in India and propose a technology-enabled stakeholder management model in Indian CRE.Design/methodology/approach: The research is qualitative in nature and follows the grounded theory approach. Research data were collected by conducting a series of semi-structured interviews with 18 property management professionals from different prominent Indian companies using PropTech.Findings: The findings suggested the nine most typical automated property management functions in Indian CRE. The result of this research is the automated property services model for stakeholder management in CRE. The model demonstrates the value of implementing technology in property services in India.Practical implications: The study provides useful insights into how artificial intelligence (AI) in property management can be applied to address property-related challenges, various stakeholder needs and improve property performance in accordance with energy efficiency policies.Originality/value: This paper attempts to add to the limited body of literature on technology in the property management domain. The model demonstrates how automated property services meet the needs of different stakeholders in CRE and provides remote working procedures within the COVID-19 pandemic context.

Purpose: The aim of this study is to perform a systematic literature review on retail agglomeration literature and present an agenda of future research in this domain.Design/methodology/approach: To synthesize and evaluate the retail agglomeration literature, the study adopts a structured systematic literature review approach. Additionally, the study employs the Theory-Context-Characteristics-Methodology (TCCM) framework to present future research directions in the retail agglomeration domain.Findings: This review proposes a conceptual framework showing the relationships between the antecedents, mediators and consequences reported in the retail agglomeration literature. Lexicometric analysis shows that the key themes of retail agglomeration research are retail store selection and retail performance, retail location strategy and store format, customer perceptions, behavior and expectations.Research limitations/implications: Specific criteria used for the inclusion of literature limits articles considered for the current systematic review. Also, only those articles published in English were considered.Practical implications: Based on the proposed model, this review presents strategies to enhance the performance of retail agglomeration.Originality/value: This study has systematically synthesized the retail agglomeration literature to explore its development over time and proposes a research framework which provides a comprehensive understanding of retail agglomeration literature.

Purpose: Many corporates in India are constantly adapting real estate benchmarks to reduce the workspace maintenance cost. However, anecdotally benchmarking the experience of clients while designing the workspace maintenance policies is not adequately taken into consideration in India. The focus of this study is on benchmarking workspace usage based on client usability.Design/methodology/approach: The research is descriptive in nature. A structured questionnaire was sent to Information Technology (IT) companies in India to collect data through SurveyMonkey. Stratified sampling was used to collect a sample of 697 respondents which was also verified using G* software. The data collected was analysed using descriptive statistics and partial least square–structured equation modeling (PLS-SEM) to investigate the mediating effect of benchmarking the workspace usage on portfolio optimization and client satisfaction.Findings: The structural model results obtained through the bootstrapping technique show that benchmarking workspace usage for real estate management positively impacts client satisfaction in the Indian IT workspace. The findings of this study support the full mediation effect (97%) and indicate that benchmarking practices are necessary for developing strategies for optimal portfolio asset utilization and are essential to survive in the current competitive business environment.Research limitations/implications: The findings of this study were influenced by the feedback from the top 100 IT clients in India. The research findings vary according to the cost-benefit analysis of adopting benchmarking measures in small and medium-sized IT companies which still benchmark the workspace usage based on cost-saving measures. Also, very sparse research has been conducted in the workspace management domain of IT firms, so the results of this study can further be used as a reference to explore this area.Practical implications: The study provides useful insights into how benchmarking in the workspace management domain of the CRE industry can be applied to address portfolio-related challenges, divergent client needs and improve workspace usability following energy-efficient policies. Practitioners can use this study as a guide to develop more effective workspace management policies.Social implications: This study may guide other firms to benchmark their current workspace usage and evaluate the impact of their workspace management policies based on the theoretical framework of value-added balanced benchmarking criteria.Originality/value: This research adds value to the limited literature available on the impact of technology-enabled portfolio optimization techniques through benchmarking which can reduce workspace usage and enhance the usability of the workspace.

Traditional approaches to education seem to be a legacy of the industrial revolution which are fast facing value erosion in the context of Industry 4.0. Today, in the VUCA world of disruptions, it is needless to say that the emergence of big data, artificial intelligence, and machine learning have facilitated the emergence of new pedagogies in learning like personalised learning and outcome- based approach to education. The prime focus of outcome-based education (OBE) is on the learning outcomes, which are expected to be attained at the completion of a course by a student rather than how they have attained the desired outcomes. The chapter seeks to provide an insight as to how the teaching learning processes and practices in business schools (management institutions) need to be further revamped to become outcome based to build the necessary skillsets and competencies in learners to increase their employability and develop an innovative and creative mindset in them to effectively meet the challenges of the workplace in future in the context of Industry 4.0. The chapter would draw the attention of policymakers, administrators, and academic practitioners and help them to create and support learning spaces within the academic ecosystem without compromising the individuality, creativity, and innovation of the learners.

The research aims to examine the unexpected changes in stock prices due to external shocks given to the macroeconomic variables to forecast future stock market returns. The study applies two econometric models such as «Variance Decomposition» (VDC) and «Impulse Response Function» (IRF) for examining the exogenous shocks in macroeconomic variables respond to changes in stock prices. Monthly time series data of five significant macroeconomic variables Real Exchange Rate, Interest Rate, Consumer Price Index (CPI), Crude Oil Prices, and Trade Openness, taken as independent variables and BSE SENSEX as a dependent variable. The research period is from Jan 2009 to Dec 2019. The study has taken the responsibility to reveal a few strong evidences for changes in stock prices due to exogenous shocks in Exchange Rate, Trade Openness, Inflation, and Interest rate along with crude oil prices. According to the results, changes in the stock market are due to external factors like changes in dividend policy or capital loss, and some changes in the stock market are due to its own innovative shocks. This study suggests to reduce unexpected changes in stock prices frequently, companies should control capital loss and focus on stable return/dividend policies. There are divergent views in the literature review in the context of measures of these variables, however no research has been done on exogenous shocks in macroeconomic variables to BSE SENSEX for the Indian stock market with this particular data set and duration.

The purpose of this research is to look into the relationship between the NSF. Nifty and the Gross Domestic Product. During the Covid-19 epidemic, changes in the relationship between the NSF. Nifty and the GDP are investigated. For the years 2000-2001 to 2020-2021 and Q4:2018-19 to Q1:2021-22 of Gross Domestic Product (GDP) and NSF. Nifty, both annual and quarterly data are used. Unit root tests, Johansen cointegration tests, the Vector error correction model (VECM), the Wald test, the Granger Causality test, and the Karl-Pearson coefficient of correlation were all utilized. The Johansen cointegration test indicates that the NSE Nifty and GDP have a long-run relationship. Similarly, the results of the vector error correction model demonstrate that the NSE Nifty Index has a positive impact on GDP. According to the results of the Granger causality test, the NSF. Nifty is the most important indicator of GDP during the Covid-19 period. From the time of the Pre-Covid-19 pandemic until the time of the Covid-period, the strength of the link has grown stronger.