This paper examines the direct and indirect relationships between the pillars of total quality management (TQM) and total productive maintenance (TPM) and investigates their effects on manufacturing performance (MPR) using structural equation modeling. Three structural models were developed with their corresponding hypotheses. Data were then collected from thirty industrial firms in Jordan. A descriptive statistical analysis was followed by an analysis of variance (ANOVA). The structural models were analyzed to draw conclusions about the model hypotheses and identify the most influential TPM and TQM pillars on MPR. Significant variations were noticed among firms in the implementation levels of the TPM, TQM, and MPR pillars. Further, it was found that TPM directly and indirectly influences MPR. Furthermore, TPM pillars have a larger impact on MPR than TQM. Research hypotheses are suggested to be tested individually on small-, middle-, and large-sized firms. This research provides valuable information on top management in the industrial sector, on the significant relationships between the pillars of TPM, TQM, and MPR and supports them in identifying important TQM/TPM pillars which they should focus on in order to enhance MPR.

The purpose of this research is to give an insight into the Turkish real estate investment funds (T-REIFs) by comparing their risk-return performances with the main benchmark investment tool Istanbul Stock Exchange-100 (BIST-100) Index. This study evaluated the performance of T-REIFs in four different periods between January 2017 and December 2020 (2017m1–2017m12, 2018m1–2018m12, 2019m1–2019m12 and 2020m1–2020m12) including the Coronavirus Disease (Covid-19) period by applying the Sharpe and Treynor ratios. In a well-diversified portfolio both ratios give the same results, but in the presence of non-systematic risk and the portfolio is poorly diversified, the Treynor ratio is a better indicator than the Sharpe ratio. The findings of this study show that rankings of Sharpe and Treynor ratios may differ for each period. These results also support the fact that the portfolios of funds in the Turkish real estate market are not well diversified. By providing corporate tax exemptions, and by enabling the investors to diversify their investments and reduce their risks, real estate investment funds are important alternatives to direct real estate investments in Turkey. In that context, being one of the pioneer studies in this niche and a new topic in emerging markets, analyzing the return performances of T-REIFs and comparing them with the returns of the BIST-100 index is aimed to contribute to literature as well as provide insight to investors who may consider investing in the Turkish real estate capital market instruments. Keyword : real estate investment funds, return performances, emerging markets, performance measurement, Sharpe ratio, Treynor ratio, Covid-19

The Turkish residential market supported by macro-economic developments is of great importance for the country's economy and the construction industry. The main purpose of this study is to analyze the Turkish residential construction market dynamics, especially in terms of price-cost and supply-demand regimes and conduct future projections. For this purpose, analysis of construction cost and residential price indexes, total and mortgaged residential sales, and construction and occupancy permits were evaluated utilizing institutional data. In addition, positive and negative effects of the changes in the residential prices in the macro scale were examined. After, conducting the residential price and cost change analysis for between 2010-2023, future stock volume and residential market projections were carried out. As a result of the analysis, we can divide the residential market into 3 periods: positive development period between 2010 and 2015, the recession period from 2015 to 2019, and the period after 2020. In the projection, depending on the supply contraction and the results of demand changes, price and cost increases, the current residential stock volume will reach its lowest level by 2023. Following this, the last period is expected to reach a more balanced residential market shaped by need-based purchases rather than individual investments.

Raising indoor temperature setpoints is widely promoted as a practical way to reduce cooling-related energy demand in commercial buildings, yet its net carbon impact becomes uncertain once behavioral rebound effects are considered. This study develops an integrated carbon-accounting framework to evaluate the climate implications of summer indoor temperature increases of 1–3 °C in U.S. mercantile buildings. The framework combines operational energy savings from reduced cooling demand with consumption-driven emissions arising from longer customer dwell times and increased consumer spending under improved thermal comfort conditions. Carbon outcomes are quantified using sector-level electricity data and the USEEIO emission factor for retail trade. The results reveal a clear imbalance: operational carbon savings range from 0.21 to 0.64 Mt CO₂, whereas consumption-driven emissions range from 3.37 to 21.90 Mt CO₂, yielding a consistently positive net carbon impact of 3.16–21.26 Mt CO₂ across all scenarios. A break-even analysis indicates that only 1.30–3.89 billion USD in additional spending is sufficient to offset the operational savings. The findings remained robust across alternative behavioral and carbon-accounting specifications; a 10,000-iteration Monte Carlo analysis produced positive net carbon impacts in every simulation (median 8.54 Mt CO₂; P(NCI > 0) = 1.00). Overall, the results suggest that temperature-based efficiency measures may overstate their climate benefits when behavioral responses are ignored, highlighting the importance of incorporating rebound effects into building energy assessments and commercial climate policy.

Venture capital (VC) is increasingly adopting artificial intelligence (AI) strengthen data-driven decision-making. This article investigates how AI is reshaping VC investment practices, drawing on a survey of 15 professionals—including emerging fund managers, venture analysts, and asset managers—from Turkey, Lithuania, and neighboring emerging ecosystems. We present the survey methodology, review academic and industry perspectives, and highlight key findings on AI adoption, perceived benefits, barriers, and future outlook.The survey included questions on current AI use, benefits, barriers, and expectations. The results show that most respondents already use AI tools, primarily to enhance deal sourcing and due diligence efficiency. Participants identified faster data processing, improved decision quality, and reduced human bias as major benefits, echoing prior research, while citing data availability, explainability, and cultural resistance as critical barriers. In the future, respondents expect AI to become integral to VC, a competitive advantage for those who embrace it, while emphasizing that human judgment and oversight will remain essential. Overall, AI emerges as a transformative force in venture investing, augmenting human expertise and offering the potential to significantly improve efficiency and generate deeper insights across decision-making workflows, provided that the challenges of data quality, transparency, and human–AI collaboration are effectively addressed.This study contributes a novel, practice-grounded perspective by presenting original survey evidence from emerging venture ecosystems (Türkiye and Lithuania), a context largely absent from prior academic work that has focused on large, mature markets. The paper thus extends the evidence base on how investors actually employ AI across the venture lifecycle.