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Islamic banking has undergone significant adaption since its inception. This study aims to investigate why and how Islamic banks adapt their services, using participatory financing as evidence.

A qualitative study is designed, using working capital financing and commodity operations financing in Pakistan as analytical units. The data for each analytical unit is analyzed using a qualitative content analysis, while the findings are synthesized using a cross-case synthesis method.

Findings suggest that participatory financing has undergone extensive adaptation in the Islamic banking industry of Pakistan, in the wake of resolving constraints to participatory financing and increasing its viability. Consequently, participatory finance has emerged as an attractive and viable option in Pakistan. These findings suggest that unlike in the past, where Islamic banks used to buffer themselves from the environment and ignore the market demands, they have learned to respond effectively to the market demands and the challenges posed by the environment.

Findings suggest that the adaptation strategy is more effective than the migration strategy, because it enables the financial service systems to reduce the underlying risks by avoiding emergent threats and eradicating the inherent weaknesses.

The extant literature provides a generalized view on the adaptation process that Islamic banks undergo to comply with their environment. However, it is limited in terms of conceptualizing the adaptations and innovations in their products and the underlying structural variations. The present study fills this gap.

This study aims to describe the role of knowledge diffusion in evolving governance principles for Islamic banking.

This study develops a discursive theoretical debate using the discourse analysis method on the Sharīʿah principles related to interest (Riba), excessive uncertainty (Gharrar) and profit and loss sharing and their convergence with the conventional banking principles of profitability, solvency and liquidity.

The study proposes a novel framework that describes how knowledge diffusion bridge-up the Sharīʿah and banking principles in terms of integration of banking principles by Sharīʿah scholars, integration of Sharīʿah principles by managers and the resultant, emergent principles for the governance of Islamic banking.

The proposed framework can inform professionals on how knowledge of banking practices and Sharīʿah can help them in governing Islamic banking. The Board of Directors may adopt a holistic approach for encouraging enhanced interactions between Sharīʿah scholars and managers. Such interaction may be increasing harmony, reducing conflicts and better coordination resulting in Sharīʿah-compliant and market wise viable products and services, thus increasing banking profitability.

This is the first study, which acknowledges and illustrates the role of the knowledge diffusion process in evolving governance principles for Islamic banks. This paper contributes to the theory of corporate governance by using the knowledge, aptitude and practice theory lens to examine conceptually how Islamic banking governance principles emerged through the knowledge diffusion process.

This paper aims to conceptualize the nexus between the participatory finance and the higher ethical objectives within the Islamic moral economy, also termed as Maqasid al Shari’ah.

Insights from the extant Islamic economics and finance literature are integrated through an interpretative systematic review using the principles from critical interpretative synthesis (CIS).

A coherent framework is synthesized comprising the typology of the Maqasid al Shari’ah, the axioms of participatory finance and their nexus which is formulated by theorizing the common thread of meaning through the axioms of participatory finance and Maqasid al Shari’ah at the interpretative level. This framework postulates that the participatory finance fits well in the ethos and the value system of Islam. Moreover, “social well-being” invariably provides the nexus between the Maqasid al Shari’ah and participatory finance.

This study contributes to the Islamic economics and finance literature by integrating the dissenting views from the divergent literature related to the basic philosophy of Shari’ah and participatory finance and provides grounds for policy implications, particularly, for designing the financial products. Moreover, it demonstrates an application of interpretative systematic review in Islamic banking and finance research.

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.