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The infill design approach for heritage settings is a challenging task, and it draws the attention of design professionals and residents. The extant literature has advocated for a contextual design approach for new buildings in heritage sites. However, the degree of contextualism for a new building in heritage sites is subjective, and it varies between exact replication and contrast scales. This study aims to evaluate an appropriate design approach for historic precincts of Odisha, an eastern state of India.

Two prime eastern heritage sites (Puri and Ekamra Kshetra) are selected as cases in this study. This research methodology involves identifying key architectural elements from both sites and a questionnaire (prepared by design experts) based on interviewing 400 residents and 36 design professionals on their aesthetic preferences for the different architectural styles and elements. The questionnaire was prepared by the design experts based on the identified architectural styles and elements of both sites. Descriptive statistics and correlation analysis are used to measure the significance of design approaches and elements.

This study's outcome confirms that most of the respondents (design professionals and residents) prefer the replication design approach with traditional architectural elements of Odisha. Also, documentation of the chronological development of architectural styles and elements of heritage sites of Odisha is done in this research.

This study has a few limitations: first, the land use characters (mixed, residential, commercial, etc.) of buildings in the heritage precinct are not considered in this research; second, this research has not included the financial aspect of infill design and last, the impact of respondents' socioeconomic factors on their aesthetic perceptions is not considered in this research.

The development authorities can use the outcomes of this research to implement a design strategy for infill buildings in the historical sites of Odisha.

This research article has documented traditional architectural elements of two prime heritage sites of India.

To date, no quantitative research has been done on infill design approaches in any Indian heritage precincts. This is the first quantitative research on the perception of stakeholders and users on the infill design in historical settings of Eastern India. This research has identified key architectural styles, elements and materials of the heritage sites.

This study aims to focuses on the paradox of the great resignation (GR), great layoff (GL) and moonlighting (ML) phenomena triggered by the unprecedented complexity, extreme emotional distress and uncertainty caused by the pandemic to explore ways for mitigating their impact on the intention to stay (ITS).

A systematic literature review was conducted to explore the impact of the paradoxical GR, layoffs and ML on the ITS by the text analysis of a pool of 111 published articles across 57 journals between 2012 and 2024 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach. The factors impacting the ITS, and the underlying interlinkages of the paradoxical GR, GL and ML phenomena were subsequently inductively deduced using Nvivo software.

The new theoretical framework explains the evolving dynamics of the ITS and prompts toward a concurrent approach of viewing the paradoxical GR, layoffs and ML through a “common lens” revealing novel insights.

Employees have reprioritized work-life balance, mental health, multiple jobs for varied income streams, flexible work schedules and job satisfaction. Human resource managers should prioritize these aspects and adapt to the evolving workforce dynamics to create a resilient, employee-centric organizational environment where employees choose to stay.

To the best of the authors’ knowledge, this is the first study to concurrently examine the current paradox of the GR, layoffs and ML for enhancing the ITS, necessitating a re-evaluation of traditional perceptions of unemployment and job seeking.

The presentation of all required criteria that a facade should fulfill needs to be revised, particularly considering novel perspectives on facade. This study aims to fill this need because leaving it unsolved leads the specialists to evaluate and design facades based on insufficient data resulting in imprecise decisions in both areas.

An exploratory study was conducted based on library resources to identify and extract the criteria, which were coded openly and axially, and categorized via a new standpoint. Shannon method was also used to analyze categories’ frequencies.

In total, 42 criteria were found and organized in five categories: “Environmental Aspects,” “Social Impacts,” “Economic Aspects,” “Efficiency and Effectiveness” and “Technical Issues.” This study adds 20 criteria, including “Natural Resource Depletion” and “Clients’ Preferences,” to facade assessment criteria compared to previous studies. Compared to similar research, “Technical Aspects” is augmented to this field's categorizations, whereas “Social Impacts” and “Environmental Aspects” are provided in a more extensive way.

The research proceeded until theoretical saturation; therefore, there is no claim to be the most comprehensive. The findings are instrumental to professionals for making practical decisions. The concentration and frequency of the literature will offer an insight to academics about the pioneering fields of facade development.

This study presents a comprehensive and up-to-date set of facade assessment criteria based on new approaches that see facade as a multifunctional component that must address various requirements beyond aesthetics.

This study aims to investigate the psychological mechanisms underpinning the impact of branded memes on brand love on social media.

Using a self-reported online survey, data from 479 verified Facebook users were analyzed using structural equation modeling to examine the interrelationships among sense of liberation, perceived trendiness, hedonic engagement, perceived humor, self-brand connection and brand love.

The results indicate that a sense of liberation significantly influences self-brand connection but does not directly affect perceived humor. Perceived trendiness is a key predictor of both perceived humor and self-brand connection. However, hedonic engagement has no substantial effect on perceived humor or self-brand connections. Importantly, perceived humor plays a crucial role in strengthening self-brand connections and fostering brand love.

To the best of the authors’ knowledge, this study is the first empirical exploration to showcase the consumer brand love formation process facilitated by branded memes, offering valuable insights into meme-marketing strategies. This highlights the importance of perceived humor and trendiness in enhancing brand love through social media platforms.

Heterogeneous graphs, composed of diverse nodes and edges, are prevalent in real-world applications and effectively model complex web-based relational networks, such as social media, e-commerce and knowledge graphs. As a crucial data source in heterogeneous networks, Node attribute information plays a vital role in Web data mining. Analyzing and leveraging node attributes is essential in heterogeneous network representation learning. In this context, this paper aims to propose a novel attribute-aware heterogeneous information network representation learning algorithm, AAHIN, which incorporates two key strategies: an attribute information coverage-aware random walk strategy and a node-influence-based attribute aggregation strategy.

First, the transition probability of the next node is determined by comparing the attribute similarity between historical nodes and prewalk nodes in a random walk, and nodes with dissimilar attributes are selected to increase the information coverage of different attributes. Then, the representation is enhanced by aggregating the attribute information of different types of high-order neighbors. Additionally, the neighbor attribute information is aggregated by emphasizing the varying influence of each neighbor node.

This paper conducted comprehensive experiments on three real heterogeneous attribute networks, highlighting the superior performance of the AAHIN model over other baseline methods.

This paper proposes an attribute-aware random walk strategy to enhance attribute coverage and walk randomness, improving the quality of walk sequences. A node-influence-based attribute aggregation method is introduced, aggregating neighboring node attributes while preserving the information from different types of high-order neighbors.

This study aims to develop an index to evaluate the extent to which key performance indicators (KPIs) align with the principles of integrated thinking.

Prior academic research, complemented by reporting and governance standards, is used to develop an index to gauge the extent to which KPIs take an integrated approach to performance evaluation. A mixed methods approach is adopted. A qualitative content analysis of organisations’ extra-financial reports is used to gain insights into the level of integrated thinking in performance evaluation structures using the index. Results are then calibrated using well-established integrated thinking proxies.

Ten indicators are identified, which point to an integrated approach to performance evaluation or the application of an underlying integrated thinking logic. The index is applied to a sample of companies from 2013 to 2021. The results point to companies increasingly incorporating integrated thinking into their KPIs. KPIs, which used to address only financial dimensions, are becoming more pluralistic and may be better described as “integrated performance indicators” (IPIs).

The index is applied to KPI disclosures of a sample of companies in a single jurisdiction only.

Those charged with governance and management can use the index to structure, implement and evaluate IPIs. Investors can use the index to gauge the operationalisation of integrated thinking. Assurance providers may find the index useful when conducting risk assessments, particularly given the increased focus on the assurance of extra-financial information.

Regulators can use the index for benchmarking organisations, compliance monitoring and identifying gaps between regulatory expectations and corporate behaviour. More broadly, the index could be used to promote alignment with sustainability frameworks and sustainable development goals at the national level.

This study outlines the dimensions of integrated thinking, which can be applied in the context of performance evaluation structures.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

This study aims to argue that responses in economic growth (EG) resulting from positive and negative shocks in energy consumption could be a non-linear phenomenon. Thus, the study aims to investigate the existence of non-linear long-run effects of positive and negative shocks in green and conventional energy consumption on EG for China and India. By decomposing energy consumption in positive and negative shocks, the study seeks to determine the distinct impact of positive and negative shocks in energy (conventional and green) consumption on EG of China and India.

A non-linear autoregressive distributed lag (NARDL) model based on energy-augmented environment Kuznets curve (EKC) framework is used on annual time series covering the period 1965–2021. The study uses a precise econometric methodology, starting with unit root tests to assess stationarity, moving to the estimation of the NARDL model, which resulted in the calculation of long-run coefficients and error correction terms to analyse the rate of adjustment towards equilibrium.

The empirical findings demonstrate that there exists a non-linear cointegrating relationship among EG, carbon emissions and green and conventional energy consumption for both economies. In the long run, a non-linear impact of green energy consumption (GEC) on EG is evident for China only, whereas non-linear impact of conventional energy consumption (CEC) on EG is visible for both countries.

While China and India prioritise energy diversification by embracing green energy to promote energy security and limit rising carbon emissions, it is interesting to investigate how positive and negative shocks in GEC and CEC have affected their EG. Second, this paper examines the trade-offs between EG and GEC/CEC in China and India, two high-carbon emitters. The disparities in trade-offs may indicate how well each country’s energy policies address increased EG with fewer energy-induced carbon emissions.

This study examines non-linear cointegration among the variables of interest, whereas most prior studies have focused on linear cointegration. The existence of non-linear cointegration may suggest that positive and negative shocks in GEC and CEC can result in non-linear reactions in EG. Thus, it establishes a basis for examining the non-linear long-term effects of GEC and CEC on EG. The research findings indicate significant consequences and necessitate prompt intervention to alleviate the detrimental impacts of shocks in GEC and CEC on EG in China and India and provide several important inputs to address the inherent challenges of energy transition goals.

This paper aims to introduce an innovative decision-making framework designed to optimize egress location selection in buildings, significantly enhancing safety during emergencies. By integrating social, economic and technical criteria through the fuzzy analytic hierarchy process (F-AHP), this framework effectively manages the uncertainties inherent in emergency scenarios, providing a robust tool for safeguarding lives and property.

The proposed methodology employs the F-AHP to systematically integrate and evaluate social, economic and technical criteria for egress location selection. A comprehensive case study conducted on a one-story school building demonstrates this approach’s practical application and effectiveness.

The analysis reveals that economic criteria are the most significant in determining the optimal egress location, constituting 61.9% of the overall weight, followed by technical criteria at 26.8% and social criteria at 11.3%. The findings highlight the practical application and effectiveness of the proposed approach in addressing uncertainties and optimizing egress location selection. Integrating social, economic and technical considerations gives decision-makers a robust tool to optimize safety, cost-effectiveness and evacuation efficiency.

The findings underscore the practical benefits and effectiveness of the proposed approach in managing uncertainties and improving the selection of optimal egress locations. Integrating social, economic and technical considerations gives decision-makers a robust tool to optimize safety, cost-effectiveness and evacuation efficiency. This approach equips practitioners with a comprehensive framework for enhancing emergency preparedness and resilience in various building scenarios.

This research introduces a novel framework for egress location selection that addresses the limitations of existing studies by integrating multiple criteria – social, economic and technical – into a cohesive decision-making process. Using the F-AHP in a case study illustrates the framework’s practicality and adaptability, providing a dynamic solution that enhances the overall effectiveness of emergency plans. By prioritizing safety, cost-effectiveness and efficiency, this framework ensures that emergency plans are robust and adaptable, thereby significantly enhancing resilience in the face of emergencies.

We model the relation between excess returns, fund size and industry size for active equity funds.

We study and contrast four markets – global equities, emerging markets, Australia core and Australia small caps – and use the results to investigate the extent to which funds deviate from estimated capacity.

We uncover a significantly negative relation between returns and both fund size and industry size across all markets. The estimated percentage of funds operating above versus below capacity varies both across markets and over time, as does the role played by fund size versus industry size. We find a greater prevalence of funds operating significantly below than above capacity, in contrast to findings for US equity mutual funds. Significant deviations from estimated capacity persist for a median of between two and six quarters.

Our main contribution is to show that the dynamics governing deviations from capacity for active equity funds vary across markets.