Investment Constraints Research Articles

How does environmental policy affect operations and supply chain management: A literature review

Governments in many countries actively carry out various policies to tackle pollution problems sourced from operations and supply chain activities, aiming at reducing adverse environmental impact and promoting sustainable development. This study provides a comprehensive and well-structured review of research on operations and supply chain management within the context of environmental policy. Specifically, this study meticulously identifies 137 papers and presents the descriptive results of a bibliometric analysis on these papers. Afterwards, using a well-defined framework encompassing three types of policies and various levels of operational and supply chain management, the identified papers are further classified. For each classification, content analyses are conducted accordingly, delving into the diverse concerns and contributions of the reviewed papers from the perspectives of pricing, production planning, inventory and technology management, coordination and competition, recycling and remanufacturing activities, transportation decisions, and network design. Three aspects of sustainability performance are also considered in the analyses. Finally, this study provides future research directions from seven perspectives: research methodology, forms of regulation, dynamic games, types of contracts, sustainable target management, green behavior of supply chain members, and investment constraints. These insights in turn inform the development of policies and decision-making processes related to environmental sustainability.

Evaluating the Efficiency of Capital Enhancement and Investment Constraints in Life Insurance Supervision

Abstract This study addresses the critical public concern of effectively mitigating solvency risks in insurance companies through supervisory intervention. Building upon existing research, we extend our analysis to incorporate the effects of foreign investment and examine the impact of varying degrees of risk aversion among insurers. Furthermore, we investigate the optimal expected terminal utility in relation to initial insurance company premiums, integrating interest and exchange rates into our analytical framework. Additionally, we evaluate the impact of regulatory interventions, specifically capital injection schemes and risk-constrained asset allocation, on insurers’ default risk and certainty equivalent wealth using a utility-based approach. Our findings indicate that: (1) when policyholders exhibit higher degrees of risk aversion, a reduced propensity to allocate funds to riskier assets, such as stock index funds and foreign assets, is observed; (2) when exchange rates offer higher returns, an increased allocation to foreign assets is advisable; (3) conversely, when exchange rate returns exhibit greater uncertainty, a reduction in foreign asset allocation is recommended; (4) in scenarios where foreign risk-free rates yield higher returns, an increased allocation to foreign assets is suggested; and (5) under conditions of capital minimization, the influence of exchange rates and foreign interest rates on optimization becomes less discernible.

Characterization of production and commercialization systems of camedor palm (Chamaedorea elegans Mart.).

Objective: To describe the cultivation systems of C. elegans through the analysis of production and commercialization in three municipalities in the high mountain region of Veracruz, Mexico. Design/Methodology/Approach: The study was conducted in Tepatlaxco, Zongolica, and Omealca, Veracruz, Mexico. From January to July 2022, a survey was administered to 84 producers of Camedor palm using convenience sampling. In addition, interviews with key informants and participant observation were conducted. Results: Camedor palm production occurs in small production units ranging from 1 ± 0.05 to 7.0 hectares, primarily managed by male producers (93%) with an average age of 48 years and 6 years of schooling. Planting densities range from 35,000 to 100,000 plants per hectare, and leaf cutting is conducted on a quarterly basis, yielding between 1000 to 3700 rolls per hectare at an average price of $14.00 Mexican pesos per roll paid to the producer. Prior to cultivating Camedor palm, 56% of producers were growing coffee. The main driving factor for cultivation is the steady generation of income. Limitations/Implications: This is a specific case study; therefore, the results are limited to descriptive statements about the study area. Findings/Conclusions: Producers utilize non-timber forest products as alternatives to crises in other crops and investment constraints within production units. The production of C. elegans is accessible, requires low investment, and is compatible with family farming. However, yields in some cases are low, and the marketing network shows high intermediation.

The Impact of Digital Finance on Corporate Operational Risk From the Perspectives of R&D Investment and Financing Constraints

The swift expansion of digital finance has profoundly influenced the operations of micro-enterprises and subsequently impacted macroeconomic stability and financial health. This study utilizes financial data from A-share listed companies from 2011 to 2020, alongside indicators of the macro digital economy, to assess how digital finance development influences business risks. It demonstrates that digital finance notably reduces these risks, with these findings holding steady even after addressing endogeneity concerns and performing various robustness tests. Analysis of the mechanisms shows that increased investments in R&D and alleviation of financing constraints are key ways digital finance reduces business risks. A heterogeneity analysis reveals that the impact of digital finance varies depending on the corporate structure and size, with state-owned and large-scale enterprises benefiting more, likely due to the digital divide. Furthermore, this study explores the effects of digital finance on reducing corporate fraud and litigation risks, discovering that it significantly lowers fraud risks, although it does not significantly affect litigation risks. This research provides important theoretical and practical insights, suggesting that digital finance can enhance risk management and contribute to superior economic and financial development.

Low-carbon oriented planning of shared photovoltaics and energy storage systems in distribution networks via carbon emission flow tracing

With the targets of carbon peaking and carbon neutrality, carbon emission reduction measures have become significant issues in new-type power systems. Distribution networks (DNs), which multiple power sources and flexible loads access to, play an essential role in exploiting the potential of reducing carbon emissions on the demand side. To achieve a global carbon emission reduction considering the carbon quota of each customer, shared photovoltaics (PVs) and energy storage systems (ESSs) are allocated with a centralized calculation and optimization conducted by DNs. To solve two key points in demand-side planning of shared PVs and ESSs in distribution networks, i.e., the accuracy of carbon emission flow (CEF) calculation and carbon quota-oriented optimization planning, this paper proposes a low-carbon oriented planning method for shared PVs and ESSs via CEF tracing. Firstly, a novel CEF calculation method is proposed based on the forward current sweep and backward voltage sweep algorithm, which considers the tracing of reactive power and power losses. Next, carbon emission index and economic index of allocating shared PVs and ESSs are defined. Subsequently, a bi-level optimization model is presented, considering the excess carbon emissions of each load based on carbon accounting and carbon quotas. Finally, the superiority of the proposed method is verified through a modified IEEE 33-node distribution network, and the effects of various investment constraints and carbon quotas are discussed.

Green credit, carbon emission trading and corporate green innovation: Evidence from China

Green Credit Guidelines (GCG) and Notice of the Pilot Project on Carbon Emission Trading (CET) have the dual attributes of financial and environmental policies. They can exert an important impact on firms' innovation by influencing investment, financing, and environmental constraints. Accurately detecting their firm and synergistic effects on green innovation is vital to developing new quality productivity and promoting China's green transition. Based on China's A-listed company data from 2007 to 2020, this study use multistage dynamic difference-in-differences (DID) models to carry out a multidimensional, empirical investigation of the dual policies' impacts on green innovation. The findings, displayed as both GCG and CET, have promoting effects on green innovation, and their positive synergistic effects appear to be greater than the direct effects. In terms of dynamic effects, the dual policies promotion effect on green innovation decreases after five years of policies' simultaneous implementation. Mechanism analysis shows that the positive synergistic effects of GCG and CET on green innovation are mainly caused by alleviating financing constraints, they are moderated by company executives' academic background and external media's attention. The heterogeneity analysis shows that the promotion effects are more pronounced in the eastern region, state-owned enterprises, and mature enterprises. Some suggestions for policy optimization and implementation are proposed from the perspectives of both local government and firms.

Evaluation of constraints for investment in NOx emission technologies: case study on Greek bulk carrier owners

PurposeThe maritime industry is the transport mode that contributes most to air pollution. The International Maritime Organization (IMO) identified the reduction of air pollution by ships as a crucial issue. Since 1 January 2020, ships have had to adopt strategies and new technologies to eliminate air pollution. However, ship compliance with nitrate oxide (NOx) emission restrictions is more challenging. This paper aims to identify shipowners' challenges in investing in new technologies.Design/methodology/approachThis paper applied a hybrid methodology combining a survey, a balanced scorecard and fuzzy analytic hierarchy process (F-AHP) to identify and evaluate constraints and weights in investment decision-making for NOx technologies. A survey was carried out to validate constraints.FindingsA survey was carried out, representing 5.1% of Greek-owned ships by deadweight capacity. The findings provide a weighted list of seven crucial technical and economic constraints faced by ship operators. The constraints vary from ship retrofit expenditure to crew training and waste management. Additionally, NOx emission technologies were compared. It was found that liquefied natural gas is the preferred investment option for the survey participants compared with selective catalytic reduction, exhaust gas recirculation and batteries.Originality/valueSeveral studies have dealt with the individual technical feasibility of NOx reduction technologies. However, apart from technical feasibility for a shipowner, the selection of a NOx technology has several managerial and safety risks. Therefore, the originality of this paper is to reveal those constraints that have a higher weight on shipowners. With this cost-benefit approach, investment challenges for ship operators are revealed. Policymakers can benefit from the results of the employed methodology.

Regulatory framework on governing equity crowdfunding: a systematic literature review and future directions

PurposeThe purpose of this study is to comprehensively analyse and compare equity crowdfunding (ECF) regulations across 26 countries, shedding light on the diverse regulatory frameworks, investor and issuer limits and the evolution of ECF globally. By addressing this research gap and providing consolidated insights, the study aims to inform policymakers, researchers and entrepreneurs about the regulatory landscape of ECF, fostering a deeper understanding of its potential and challenges in various economies. Ultimately, the study contributes to the advancement of ECF as an alternative financing method for small and medium enterprises (SMEs) and startups, empowering them to access much-needed capital for growth.Design/methodology/approachThe study used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model for a systematic literature review on global ECF regulations. Starting with 74 initial articles from Web of Sciences and Scopus databases, duplicates were removed and language criteria applied, leaving 42 articles. After a thorough full-text screening, 20 articles were excluded, resulting in the review of 22 papers from 2016 to 2022. PRISMA’s structured framework enhances the quality of systematic reviews, ensuring transparency and accessibility of findings for various stakeholders, including researchers, practitioners and policymakers, in the field of ECF regulations.FindingsThis study examines ECF regulations across various countries. Notably, the UK has advanced regulations, while the USA adopted them later through the Jumpstart Our Business Startups Act. Canada regulates at the provincial level. Malaysia and China were early adopters in Asia, but Hong Kong, Japan, Israel and India have bans. Turkey introduced regulations in 2019. New Zealand and Australia enacted laws, with Australia referring to it as “crowd-sourced equity funding”. Italy, Austria, France, Germany and Belgium have established regulations in Europe. These regulations vary in investor and issuer limits, disclosure requirements and anti-corruption measures, impacting the growth of ECF markets.Research limitations/implicationsThis study’s findings underscore the diverse regulatory landscape governing ECF worldwide. It reveals that regulatory approaches vary from liberal to protectionist, reflecting each country’s unique economic and political context. The implications of this research highlight the need for cross-country analysis to inform practical implementation and the effectiveness of emerging ECF ecosystems. This knowledge can inspire regulatory adjustments, support startups and foster entrepreneurial growth in emerging economies, ultimately reshaping early-stage funding for new-age startups and SMEs on a global scale.Originality/valueThis study’s originality lies in its comprehensive analysis of ECF regulations across 26 diverse countries, shedding light on the intricate interplay between regulatory frameworks and a nation’s political-economic landscape. By delving into the nuanced variations in investor limits, investment types and regulatory strategies, it unveils the multifaceted nature of ECF regulation globally. Furthermore, this research adds value by comparing divergent perspectives on investment constraints and offering an understanding of their impact on ECF efficacy. Ultimately, the study’s unique contribution lies in its potential to inform practical implementation, shape legislative frameworks and catalyse entrepreneurial ecosystems in emerging economies, propelling the evolution of early-stage funding practices.

A comparative study of fuzzy multi-objective investment project portfolio selection and optimization based on financial return and different risk measurements

Competitiveness in the global market is getting more intense. Due to resource and budget constraints, firms need to achieve their expected goals and satisfy all investment constraints under uncertainty. Selecting the set of projects among other candidates to get the most efficient portfolio requires a lot of attention from the Decision Makers (DMs) as this consideration no longer relies purely on the financial term. This problem becomes a multi-objective problem under uncertainty where the financial return and risk from uncertainty are required into the trading off consideration. Due to the financial uncertainty, the chance-constrained programming has been employed in this study for defuzzifying and solving uncertain optimization problems at a specified confidence level that is defined by the DMs. Then, various kinds of investment or financial risk measures, Lower-Semi Variance Index (LSVI), the absolute deviation with the expected FNPV, and the absolute mean-Conditional Value at Risk (CVaR) gap are provided in the selection of such risk measures to show their differences in characteristics and performances in the obtained results. Since, such problems can consist of many project candidates and complex constraints, which may grow beyond the application of the exact optimization approach, a meta-heuristic method, Genetic Algorithm (GA), is introduced to optimize this problem through designing and constructing a decision support tool for the investment portfolio selection and optimization. The applicability of the proposed comparative approach and the constructed tool are illustrated through examples.

Smart Cities and Smart Supply Chain: Integration for Sustainable Urban Development.

As urbanization accelerates globally, the concept of smart cities emerges as a promising solution to address the multifaceted challenges of sustainable urban development. Smart cities leverage advanced technologies to enhance efficiency, sustainability, and quality of life for their residents. Concurrently, the evolution of supply chain management towards smart supply chains has gained traction, driven by the imperative of optimizing logistics, minimizing waste, and enhancing resilience. This paper explores the integration of smart cities and smart supply chains as a holistic approach to foster sustainable urban development. The integration of smart cities and smart supply chains presents a symbiotic relationship wherein advancements in one domain catalyze improvements in the other. Smart cities rely on seamless and efficient supply chain operations to ensure the smooth flow of goods and services, essential for meeting the needs of urban inhabitants. [1],[2] Conversely, smart supply chains benefit from the data-rich environment and infrastructure of smart cities to optimize routing, inventory management, and last-mile delivery, thereby reducing environmental impact and enhancing overall efficiency. Key components of this integration include the utilization of Internet of Things (IoT) devices, big data analytics, artificial intelligence (AI), and blockchain technology to enable real-time monitoring, predictive analytics, and transparent, secure transactions throughout the urban supply chain. By harnessing these technologies, cities can achieve significant improvements in resource utilization, energy efficiency, and carbon footprint reduction. Furthermore, the paper examines the role of public-private partnerships (PPPs) and regulatory frameworks in facilitating the integration of smart cities and smart supply chains. Collaborative efforts between government entities, industry stakeholders, and technology providers are essential to overcome barriers such as data privacy concerns, interoperability challenges, and investment constraints. In conclusion, the integration of smart cities and smart supply chains represents a promising pathway towards achieving the goals of sustainability, resilience, and prosperity in urban environments. By embracing innovative technologies and fostering collaboration, cities can pave the way for a more efficient, equitable, and environmentally conscious future.

The Impact of Financing Constraints on the Efficiency of Investment and Construction: Evidence from Municipal Infrastructure in China

Imperfect external capital market and internal soft budget constraints are important reasons for the issue of enterprise financing constraints. This paper studies the impact of financing constraints and influencing factors of financing constraints in municipal infrastructure investment and construction for local governments by using the panel data from 2003 to 2018 in China. The results show that, first, there are great financing constraints in municipal infrastructure investment and construction for local government in China. Second, the increase of self-financing capacity and external financing capacity can reduce the level and uncertainty of financing constraints, thus improving the efficiency of municipal infrastructure investment and construction. Third, the expansion of the city scale may reduce the uncertainty of financing constraints, but improve the level of financing constraints thus reducing the efficiency of municipal infrastructure investment and construction.

Home bias and the returns of strategic portfolios: Neither always so good nor so bad

Home Bias is a phenomenon that has been sufficiently addressed from many different perspectives, such as active management or structural investment constraints. However, there is little work about the economic effects of these situations. This work aims to quantify the impact of this effect on the returns of a set of strategic portfolios with the same allocation between fixed income and equity assets. The statistical information includes the daily values of all indices for the 2004–2021 period. Returns have been calculated for investment time horizons of between 5 and 15 years. Quantile regressions have been used to assess changes in returns in response to changes in portfolio composition. The results suggest that over-investment in local assets is not always positive and dependent on the local index selected.

A hybrid robust SBM-DEA, multiple regression, and MCDM-GIS model for airport site selection: Case study of Sistan and Baluchestan Province, Iran

This study focuses on the strategic decision-making process for selecting airport locations in developing countries, where investment constraint is a significant concern. Recognizing that airport location decisions are influenced by a multitude of factors, often in the absence of comprehensive data and amidst uncertain forecasts, there is a need for a methodology capable of adeptly handling various factors under uncertainty. The goal is to provide solutions that remain feasible and near-optimal, even with some variations in input parameters, ensuring robustness. To this end, this research introduces a decision tool incorporating Slack-Based Measurement in Robust Data Envelopment Analysis (SBM-RDEA). The Slack-based method is beneficial in cases with multiple inputs and outputs where their relationships are not strictly proportional, as in the airport location selection problem. DEA, in conjunction with Multiple Regression analysis, leverages available data on the attributes of operational regional airports to compute the significance of these criteria in shaping the success factors of these facilities. Furthermore, these weightings can be employed to evaluate the suitability of airport locations currently under development and assess their potential for contributing to regional development. Additionally, to address the uncertainty of input data, a Robust optimization approach is employed to ensure a reliable response for this strategic decision-making process. This weighting framework is utilized twice within the research methodology: first, to assess the potential of different counties in the state for hosting a new regional airport, and second, to identify the optimal location for the airport in an MCDM-GIS analysis. This methodology has been applied to a case study in Sistan and Baluchistan, Iran. The result of the proposed method has been compared with one of the most common existing methodologies. This method identifies Zahedan County as having the highest potential and selects the location (29.48° N, 60.90° E), in the east of Zahedan city, as the optimal site for airport construction. Validation results confirm the efficacy of this solution.

Protection of Foreign Investors in Foreign Investment Activities and its Implications to the Country

In the interest of increasing national and economic development, Indonesia needs various kinds of support from all aspects. One of them is the presence of foreign investors. However, Indonesia is considered to not fully support the presence of foreign investors, so there are many investment constraints that have not been addressed comprehensively. To fulfill these interests, the Indonesian government formulated policies regarding investment and legal protection provided to foreign investors through Law Number 25 of 2007 concerning Investment. This study aims to determine legal protection and the implications of foreign investment activities in Indonesia. This study uses a normative juridical approach. The primary source of legal material is Law Number 25 of 2007 concerning investment. The source of secondary legal materials uses relevant literature. Data collection were done using the library study method. Data analysis were done using deductive analysis techniques. The data presentation technique uses a qualitative descriptive technique. The results of the study stated that legal protection for foreign investment (PMA) in Indonesia according to Law Number 25 of 2007 concerning investment is the application of the principle of equality for foreign and foreign investors. Legal protection is also given for actions of nationalization, acquisition of ownership, transfer of assets, transfers, and repatriation. Foreign investment has an important role in Indonesia to increase economic growth and increase national development.
 Keywords: legal protection, investment, foreign, implication

Constrained mean-variance investment-reinsurance under the Cramér–Lundberg model with random coefficients

In this paper, we study an optimal mean-variance investment-reinsurance problem for an insurer (she) under the Cramér–Lundberg model with random coefficients. At any time, the insurer can purchase reinsurance or acquire new business and invest her surplus in a security market consisting of a risk-free asset and multiple risky assets, subject to a general convex cone investment constraint. We reduce the problem to a constrained stochastic linear-quadratic control problem with jumps whose solution is related to a system of partially coupled stochastic Riccati equations (SREs). Then we devote ourselves to establishing the existence and uniqueness of solutions to the SREs by pure backward stochastic differential equation (BSDE) techniques. We achieve this with the help of approximation procedure, comparison theorems for BSDEs with jumps, log transformation and BMO martingales. The efficient investment-reinsurance strategy and efficient mean-variance frontier are explicitly given through the solutions of the SREs, which are shown to be a linear feedback form of the wealth process and a half-line, respectively.

A novel framework for photovoltaic energy optimization based on supply–demand constraints

Introduction: Distributed power supply has increasingly taken over as the energy industry’s primary development direction as a result of the advancement of new energy technology and energy connectivity technology. In order to build isolated island microgrids, such as villages, islands, and remote mountainous places, the distributed power supply design is frequently employed. Due to government subsidies and declining capital costs, the configured capacity of new energy resources like solar and wind energy has been substantially rising in recent years. However, the new energy sources might lead to a number of significant operational problems, including over-voltage and ongoing swings in the price of power. Additionally, the economic advantages availed by electricity consumers may be impacted by the change in electricity costs and the unpredictability of the output power of renewable energy sources.Methods: This paper proposes a novel framework for enhancing renewable energy management and reducing the investment constraint of energy storage. First, the energy storage incentive is determined through a bi-level game method. Then, the net incentive of each element is maximized by deploying a master–slave approach. Finally, a reward and punishment strategy is employed to optimize the energy storage in the cluster.Results: Simulation results show that the proposed framework has better performance under different operating conditions.Discussion: The energy storage operators and numerous energy storage users can implement master–slave game-based energy storage pricing and capacity optimization techniques to help each party make the best choices possible and realize the multi-subject interests of energy storage leasing supply and demand win–win conditions.

A monotone numerical integration method for mean–variance portfolio optimization under jump-diffusion models

We develop a highly efficient, easy-to-implement, and strictly monotone numerical integration method for Mean-Variance (MV) portfolio optimization. This method proves very efficient in realistic contexts, which involve jump-diffusion dynamics of the underlying controlled processes, discrete rebalancing, and the application of investment constraints, namely no-bankruptcy and leverage.A crucial element of the MV portfolio optimization formulation over each rebalancing interval is a convolution integral, which involves a conditional density of the logarithm of the amount invested in the risky asset. Using a known closed-form expression for the Fourier transform of this conditional density, we derive an infinite series representation for the conditional density where each term is strictly positive and explicitly computable. As a result, the convolution integral can be readily approximated through a monotone integration scheme, such as a composite quadrature rule typically available in most programming languages. The computational complexity of our proposed method is an order of magnitude lower than that of existing monotone finite difference methods. To further enhance efficiency, we propose an implementation of this monotone integration scheme via Fast Fourier Transforms, exploiting the Toeplitz matrix structure.The proposed monotone numerical integration scheme is proven to be both ℓ∞-stable and pointwise consistent, and we rigorously establish its pointwise convergence to the unique solution of the MV portfolio optimization problem. We also intuitively demonstrate that, as the rebalancing time interval approaches zero, the proposed scheme converges to a continuously observed impulse control formulation for MV optimization expressed as a Hamilton–Jacobi–Bellman Quasi-Variational Inequality. Numerical results show remarkable agreement with benchmark solutions obtained through finite differences and Monte Carlo simulation, underscoring the effectiveness of our approach.

Robust and Sparse Portfolio: Optimization Models and Algorithms

The robust and sparse portfolio selection problem is one of the most-popular and -frequently studied problems in the optimization and financial literature. By considering the uncertainty of the parameters, the goal is to construct a sparse portfolio with low volatility and decent returns, subject to other investment constraints. In this paper, we propose a new portfolio selection model, which considers the perturbation in the asset return matrix and the parameter uncertainty in the expected asset return. We define three types of stationary points of the penalty problem: the Karush–Kuhn–Tucker point, the strong Karush–Kuhn–Tucker point, and the partial minimizer. We analyze the relationship between these stationary points and the local/global minimizer of the penalty model under mild conditions. We design a penalty alternating-direction method to obtain the solutions. Compared with several existing portfolio models on seven real-world datasets, extensive numerical experiments demonstrate the robustness and effectiveness of our model in generating lower volatility.

The (Lack of) Anticipatory Effects of the Social Safety Net on Human Capital Investment

How does the expectation that a child will receive government benefits in adulthood affect parental investments in the child’s human capital? Most parents whose children receive Supplemental Security Income (SSI) benefits overestimate the likelihood that their child will receive SSI benefits in adulthood. We present randomly selected families with the predicted likelihood that their child will receive SSI benefits in adulthood. Reducing parents’ expectations that children will receive benefits in adulthood does not increase investments in children’s human capital. This zero effect is precisely estimated. Likely explanations include parents working more themselves, nonfinancial goals influencing investment, and families facing investment constraints. (JEL G52, I26, I38, J13, J24, J31)

Practice of Alternative Assessment at Tribhuvan University

Alternative assessment is a recent trend in the field of education. It helps the teachers and students to be more self reflective and responsible towards their tasks and activities. In this light, the present study aimed to explore the practice of alternative assessment system at Tribhuvan University. To accomplish the objective, the researcher adopted interpretivism as philosophical approach and narrative inquiry as research design. This research was based on the experience of university level teachers teaching at semester level. The total population of this study comprised all the teachers teaching at Tribhuvan University. However, the researcher selected only seven English language teachers from Tribhuvan Multiple Campus, Palpa using purposive sampling procedure. Similarly, the researcher made an interview guide, and interviewed with the sampled teachers using recording devices to obtain in-depth information. The results revealed that most of the teachers prefer to use traditional paper pencil test rather than using alternative assessment tools for student evaluation. They had their reluctance to use alternative assessment tools because of the time, resource and investment constraints. They were not properly updated and trained towards alternative assessment. However, they highlighted the importance of such tools for their students’ evaluation. The present study can be useful to the teachers of university level for gaining insights of the alternative assessment tools for student evaluation.