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Building energy management systems use important information from indoor room temperature (IRT) forecasting to predict daily loads within smart buildings. IRT forecasting is a complex and challenging task, especially when energy demands are exponentially rising. The purpose of this paper is to review the relevant literature on indoor temperature forecasting in the past two decades and draw inferences on important methodologies with influencing variables and offer future directions.

The motivation for this work is based on the research work done in the field of intelligent buildings and energy related sector. The focus of this study is based on past literature on forecasting models and methodologies related to IRT forecasting for building energy management, with an emphasis on data-driven models (statistical and machine learning models). The methodology adopted here includes review of several journals, conference papers, reference books and PhD theses. Selected forecasting methodologies have been reviewed for indoor temperature forecasting contributing to building energy consumption. The models reviewed here have been earmarked for their benefits, limitations, location of study, accuracy along with the identification of influencing variables.

The findings are based on 62 studies where certain accuracy metrics and influencing explanatory variables have been reviewed. Linear models have been found to show explanatory relationships between the variables. Nonlinear models are found to have better accuracy than linear models. Moreover, IRT profiles can be modeled with enhanced accuracy and generalizability through hybrid models. Although deep learning models are found to have better performance for this study.

This is accuracy-based study of data-driven models. Their run-time performance and cost implications review and review of physical, thermal and simulation models is future scope.

Despite the earlier work conducted in this field, there is a lack of organized and comprehensive evaluation of peer reviewed forecasting methodologies. Indoor temperature depends on various influencing explanatory variables which poses a research challenge for researchers to develop suitable predictive model. This paper presents a critical review of selected forecasting methodologies and provides a list of important methodologies along with influencing variables, which can help future researchers in the field of building energy management sector. The forecasting methods presented here can help to determine appropriate heating, ventilation and air-conditioning systems for buildings.

In the context of a developing country, Indian buildings need further research to channelize energy needs optimally to reduce energy wastage, thereby reducing carbon emissions. Also, reduction in smart devices’ costs with sequential advancements in Information and Communication Technology have resulted in an environment where model predictive control (MPC) strategies can be easily implemented. This study aims to propose certain preemptive measures to minimize the energy costs, while ensuring the thermal comfort for occupants, resulting in better greener solutions for building structures.

A simulation-based multi-input multi-output MPC strategy has been proposed. A dual objective function involving optimized energy consumption with acceptable thermal comfort has been achieved through simultaneous control of indoor temperature, humidity and illumination using various control variables. A regression-based lighting model and seasonal auto-regressive moving average with exogenous inputs (SARMAX) based temperature and humidity models have been chosen as predictor models along with four different control levels incorporated.

The mathematical approach in this study maintains an optimum tradeoff between energy cost savings and satisfactory occupants’ comfort levels. The proposed control mechanism establishes the relationships of output variables with respect to control and disturbance variables. The SARMAX and regression-based predictor models are found to be the best fit models in terms of accuracy, stability and superior performance. By adopting the proposed methodology, significant energy savings can be accomplished during certain hours of the day.

This study has been done on a specific corporate entity and future analysis can be done on other corporate or residential buildings and in other geographical settings within India. Inclusion of sensitivity analysis and non-linear predictor models is another area of future scope.

This study presents a dynamic MPC strategy, using five disturbance variables which further improves the overall performance and accuracy. In contrast to previous studies on MPC, SARMAX model has been used in this study, which is a novel contribution to the theoretical literature. Four levels of control zones: pre-cooling, strict, mild and loose zones have been used in the calculations to keep the Predictive Mean Vote index within acceptable threshold limits.

The rapid urbanization of Indian cities and the population surge in cities has steered a massive demand for energy, thereby increasing the carbon emissions in the environment. Information and technology advancements, aided by predictive tools, can optimize this energy demand and help reduce harmful carbon emissions. Out of the multiple factors governing the energy consumption and comfort of buildings, indoor room temperature is a critical one, as it envisages the need for regulating the temperature. This paper aims to propose a mathematical model for short-term forecasting of indoor room temperature in the Indian context to optimize energy consumption and reduce carbon emissions in the environment.

A study is conducted to forecast the indoor room temperature of an Indian corporate building structure, based upon various external environmental factors: temperature and rainfall and internal factors like cooling control, occupancy behavior and building characteristics. Expert insight and principal component analysis are applied for appropriate variables selection. The machine learning approach using Box–Jenkins time series models is used for the forecasting of indoor room temperature.

ARIMAX model, with lagged forecasted and explanatory variables, is found to be the best-fit model. A predictive short-term hourly temperature forecasting model is developed based upon ARIMAX model, which yields fairly accurate results for data set pertaining to the building conditions and climatic parameters in the Indian context. Results also investigate the relationships between the forecasted and individual explanatory variables, which are validated using theoretical proofs.

The models considered in this research are Box–Jenkins models, which are linear time series models. There are non-linear models, such as artificial neural network models and deep learning models, which can be a part of this study. The study of hybrid models including combined forecasting techniques comprising linear and non-linear methods is another important area for future scope of study. As this study is based on a single corporate entity, the models developed need to be tested further for robustness and reliability.

Forecasting of indoor room temperature provides essential practical information about meeting the in-future energy demand, that is, how much energy resources would be needed to maintain the equilibrium between energy consumption and building comfort. In addition, this forecast provides information about the prospective peak usage of air-conditioning controls within the building indoor control management system through a feedback control loop. The resultant model developed can be adopted for smart buildings within Indian context.

This study has been conducted in India, which has seen a rapid surge in population growth and urbanization. Being a developing country, India needs to channelize its energy needs judiciously by minimizing the energy wastage and reducing carbon emissions. This study proposes certain pre-emptive measures that help in minimizing the consumption of available energy resources as well as reducing carbon emissions that have significant impact on the society and environment at large.

A large number of factors affecting the indoor room temperature present a research challenge for model building. The paper statistically identifies the parameters influencing the indoor room temperature forecasting and their relationship with the forecasted model. Considering Indian climatic, geographical and building structure conditions, the paper presents a systematic mathematical model to forecast hourly indoor room temperature for next 120 h with fair degree of accuracy.

The purpose of this paper is to validate through a case study involving an organization in India, a five-phase Define-Identify-Build-Assess-Review methodology proposed for designing and implementing knowledge management capability (KMC) in an organization from a holistic perspective.

This paper adopts the case study approach, using semi-structured interviews and survey questionnaires to gauge KMC in the organization. Exploratory factor analysis and multiple regressions are applied to determine the impact of the chosen factors on KMC of the organization. Further, interpretive structural modelling is used to determine impact of selected variables on the business performance.

KMC of the organization is predominantly based on the “embedded routines”, “knowledge base” and its “shared utilization” in the organization. The KMC is primarily driven through improved learning and rich explicit knowledge.

The study is confined to a specific business process in the organization. As the focus of study is based on a single organization, the generalization of the results to other organizations needs to be proven.

The periodical monitoring of the identified KMCs leads to enterprises making corrections and adjustments on the knowledge assets accordingly.

Introspection of the KMCs of the organization by the management in a holistic manner and bridging the operational gap by developing performance metrics.

This paper attempts to explore and identify the growing segments of online buyers of premium brands in the context of the post-pandemic market in India. The multi-dimensional trait of fashion orientation has been used as the psychographic construct for segmenting young urban consumers who shop on e-commerce platforms.

An online study across major cities resulted in a sample of 555 urban consumers of premium apparel and accessories brands. Hierarchical, two-step and k-means cluster analysis were conducted to identify diverse consumer segments and arrive at a demographic and usage-based profiling of the clusters. Furthermore, one-way analysis of variance was conducted to assess the key drivers for an online purchase among the obtained segments.

The pioneering use of fashion orientation as a base for segmentation helped identify three distinct clusters of socially conscious fashionistas, fashion involved and fashion indifferent buyers. The study identified significant differences in the demographic composition as well as their usage patterns and purchase motivations to shop online.

The study looks at an extremely important but neglected category of premium brands. The distinct clusters of premium brand buyers highlighted by the study adds theoretical value as well as managerial insights for the premium brand marketer as they seek to target consumers in Asian economies.

The subject area is strategy and business.

The case can be used for MBA students. This is equally effective in short courses meant for low-to-mid-level working executives. The case is suited for classes in strategy, general marketing, media management and family business courses.

Dainik Jagran – a vernacular daily – is the most read newspaper in India. Under the banner of Jagran Prakashan Ltd.; which is one of the leading media houses in India, the success of Dainik Jagran has been an outcome of the strategic marketing decisions taken by its founder and his successors in the post-independence era. With extensive circulation, it created a large readership base and took bold decisions to launch multi editions to its daily through a series of acquisitions, mergers and consolidations from 1975 to 2010, enabling it to step into product diversification. Readership surveys, investments in technology, advertising, regular branding events and smart phone applications are a few tools that helped. While the group has diversified into other industries, there is an underlying anxiety about the future prospects of its newspaper business. With the onslaught of online news dailies, will Dainik Jagran be able to expand and maintain its readership base using its previous business and marketing strategies? Or is it time to change strategies for businesses in the newspaper and allied media industry in India?

The study has the following outcomes: application of value chain concept in businesses serving two-sided markets; application of environmental analysis, Porter’s five forces analysis and related strategy concepts; and learning to critically approach and develop a sustainable growth strategy framework for a successful family-run newspaper business in India.

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CSS 11: Strategy.

This exploratory study aims to examine the prevalent triggers that motivate a premium brand purchase in an online vs offline retail format.

A binary logit analysis is used to build a predictive model to assess the likelihood of the premium brand consumer seeking an online or an offline platform. Demographic and usage-based profile of the two set of consumers is established through a chi-square analysis.

Three hundred and forty six urban consumers of premium branded apparels residing in two Indian Metros were studied. A predictive model with 89.6% accuracy was validated for distinguishing premium brand buyers who shop at brick-and-mortar store or online platforms. Quality and finish were factors sought by the online buyer, whereas autotelic need, pleasurable shopping experience and social approval were important triggers for an in-store purchase.

The study posits divergent demographics and motivational drivers that led to an online vs offline purchase. Though interesting and directional, the study results need to be examined across geographies and categories for establishing the generalizability of the findings.

The study findings indicate that premium brand manufacturers can devise an omni-channel strategy that is largely tilted toward the online platform, as the quality conscious and brand aware consumer is confident and thus open to an online purchase. The implication for the physical outlet on the other hand is to ensure exclusive store atmospherics and knowledgeable but non-intrusive sales personnel.

The study is unique as it successfully builds a predictive model to forecast online vs offline purchase decisions among urban millennials.

Today's global business is heavily dependent on information and communication technology (ICT). The reality for most organisations is that the rate of technology change has been extremely fast. To cope with these changes, some organisations are committing a large amount of resources. Such challenges make it difficult for some companies to invest in ICT, resulting in a need to re‐think their business models. One such approach which has proved popular over the last few years is to outsource ICT. However, not all ICT outsourcing projects have been totally successful. The paper aims to explore various constructs in ICT outsourcing.

The aim is achieved by conducting studies on 11 ICT outsourcing projects within the service sector.

In future, customers will be looking for value‐added services while focusing less on outsourcing as a cost‐cutting exercise. There is also an added pressure on the customers and vendors to ensure that the original business case to justify outsourcing is robust.

The research is conducted with a limited sample of ICT outsourcing projects. For this reason, many of the conclusions in this paper are generalisations. Further research will need to be conducted in order for the lessons that emerge to be applicable across a wider business perspective.

The paper takes a longer term perspective on the interface between customers and vendors in outsourcing projects. However, globally, this sector is very fluid and it is crucial that organisations understand the complexity of the relationships. This paper does not specifically seek to add to the existing body of knowledge on ICT outsourcing, but rather it serves as an opportunity to reflect on the full complexity of ICT outsourcing.

This chapter analyzes the effects of introducing a graduated minimum wage in a model with optimal income taxation in which a government seeks to maximize social welfare. It shows that the optimal graduated minimum wage increases social welfare by increasing the low-productivity workers’ consumption and bringing it closer to the first-best. The chapter also describes how the graduated minimum wage in a social welfare optimum depends on important economy characteristics such as the government’s revenue needs, the social welfare weight of low-productivity workers, and the numbers and productivities of the different types of workers.

This chapter assesses the effects of corporate governance (CG) variables on the level of Corporate Social Responsibility Disclosure (CSRD), Shari'ah Supervisory Board Disclosure (SSBD), and Financial Disclosure (FD) for Islamic banks. This study, based on a sample of 95 Islamic banks, assessed this in 2013. The findings suggest that CG mechanisms, firm's age, auditor and shari'ah auditing department are effective in influencing SSBD, CSRD, and FD practices in Islamic banks. This chapter encourages regulators to improve CG mechanisms in their Islamic banking systems through the optimization of ownership structure (dispersed ownership) and the board's characteristics in order to promote transparency and disclosure. Moreover, the findings support theoretical arguments that firms disclose CG information in order to mitigate information asymmetry and agency costs and to improve investor confidence in the reported financial statements. The empirical evidence of this study enhances the understanding of the CG disclosure environment in Islamic banks as a promoting new financial system.