Profit Improvement Research Articles

Does green innovation increase enterprise value?

AbstractUsing data from 3212 enterprises in China's A‐share market during the 2007–2018 period, this paper investigates the relationship between green innovation (GI) and enterprise value by using ordinary least squares (OLS) and two‐stage least squares (2SLS) regression. We find that for every 1% increase in the proportion of the number of the enterprise's green patent applications, the enterprise value (Tobin's Q) will increase by 0.023. Moreover, GI still has a positive lag effect on enterprise value after 2 to 6 years. However, the positive effect of GI on enterprise value may only exist in young non‐state‐owned enterprises. At the same time, GI has better performance in heavy pollution industries and non‐high‐tech industries. Moreover, we prove that the improvement of profitability may be a mechanism for GI to affect enterprise value, with a mediating effect accounting for 26.1% of the total effect. The finding on the causal relationship between GI and enterprise value in this study is of great significance for further understanding of the practical value of GI and has certain reference value for the formulation of incentive policies for different industries and enterprises.

Clariant significantly increased profitability in 3Q 2021 on the back of double-digit sales growth: 3Q 2021 – Particularly strong, double-digit sales growth facilitates significant profitability improvement

Clariant significantly increased profitability in 3Q 2021 on the back of double-digit sales growth: 3Q 2021 – Particularly strong, double-digit sales growth facilitates significant profitability improvement

A genetic algorithm for order acceptance and scheduling in additive manufacturing

ABSTRACT We consider the problem of order acceptance and scheduling faced by an additive manufacturing facility consisting of multiple build chambers and postprocessing operations for support removal and surface finishing. We model each build chamber as a batch processing machine with processing times determined by the nesting and orientation of parts within the chamber. Due to the difficulty of developing an explicit functional relation between part batching, batch processing time, and postprocessing requirements we develop random-keys based genetic algorithms to select orders for complete or partial acceptance and produce a high-quality schedule satisfying all technological constraints, including part orientation and rotation within the build chamber. Extensive computational experiments show that the proposed approaches yield significant improvements in profit over the situation where all orders must be accepted, and produce solutions that compare favourably to statistically estimated bounds.

Sustainable banking regulations pre and during coronavirus outbreak: the moderating role of financial stability

With the worldwide dispersion of COVID-19, banking sector, among others, needs to adapt to unexpected challenges. For this purpose, this study examines the impact of sustainable banking regulations on bank-specific characteristics pre and during COVID-19 period in Pakistan for the period spanning from 2006 to 2020. Moreover, financial stability is employed to test its moderating role on sustainable banking regulations. The dynamic estimator, named the system-Generalized Method of Moments, is used to analyze the endogenous nature of the data. Findings suggest that capital adequacy ratio, deposit ratio, and loan ratio are positive whereas leverage ratios are negatively related to profitability and market return. Overall, findings reveal that sustainable banking regulations influenced the bank-specific characteristics substantially. Importantly, the year-wise averages of variables reveal that Pakistani banks have made significant improvements in profitability, market return, capital adequacy, and deposit ratio pre and during pandemic era. Additionally, the financial stability significantly moderates the relationship highlighting lower default risk and the effectiveness of sustainable banking operations. Practically, despite global lockdowns, economic and trade restrictions during COVID-19, State Bank of Pakistan, sustained health of banking sector through its well-regulated monitoring mechanism.

Price, online coupon, and store service effort decisions under different omnichannel retailing models

This paper explores three omnichannel retailing models—that is, a Buy-Online, Pick-Up In-Store (BOPS) model, a Showroom model, and an Interactive model (a model that combines the characteristics of BOPS and Showroom)—under which the retailer offers coupons online and invests in service efforts offline. For each omnichannel retailing model, three coupon distribution scenarios (i.e. no coupon, coupon with a common value, and coupon with a different value) are discussed to investigate coupon promotion policies and omnichannel operation strategies. Profits in the omnichannel system and under each model are compared by deriving three decision values: price, coupon value, and service effort. The key findings show that the retailer achieves profit improvement when the incremental purchasing reaches a large value. In addition, a great service effort coefficient leads to a high price, service effort, and retailer profit. The service effort and coupon value in the Showroom model are the largest among the three omnichannel models, while its profit is the lowest. Conversely, although the retailer invests less in service effort, and the price and coupon value are not too large in the BOPS model, the retailer still gains the highest profit. Additionally, in the BOPS model, it is profitable for the retailer to distinguish the coupon value for the online-only and BOPS channels when consumers are less sensitive to the online-only channel’s coupon. However, in the Interactive model, the retailer derives increasing profit by providing coupons with a common value for the online-only and BOPS channels.

The Competitive Pickup and Delivery Orienteering Problem for Balancing Car-Sharing Systems

Competition between one-way car-sharing operators is currently increasing. Fleet relocation as a means to compensate demand imbalances constitutes a major cost factor in a business with low profit margins. Existing decision support models have so far ignored the aspect of a competitor when the fleet is rebalanced for better availability. We present mixed-integer linear programming formulations for a pickup and delivery orienteering problem under different business models with multiple (competing) operators. Structural solution properties, including existence of equilibria and bounds on losses as a result of competition, of the competitive pickup and delivery problem under the restrictions of unit-demand stations, homogeneous payoffs, and indifferent customers based on results for congestion games are derived. Two algorithms to find a Nash equilibrium for real-life instances are proposed. One can find equilibria in the most general case; the other can only be applied if the game can be represented as a congestion game, that is, under the restrictions of homogeneous payoffs, unit-demand stations, and indifferent customers. In a numerical study, we compare different business models for car-sharing operations, including a merger between operators and outsourcing relocation operations to a common service provider (coopetition). Gross profit improvements achieved by explicitly incorporating competitor decisions are substantial, and the presence of competition decreases gross profits for all operators (compared with a merger). Using a Munich, Germany, case study, we quantify the gross profit gains resulting from considering competition as approximately 35% (over assuming absence of competition) and 12% (over assuming that the competitor is omnipresence) and the losses because of the presence of competition to be approximately 10%.

Optimal dynamic allocation of rental and sales inventory for fashion apparel products

There is a growing trend towards renting rather than permanent ownership of various product categories such as designer clothes and accessories. In this article, we study an emerging retail business model that simultaneously serves rental and sales markets. Specifically, we consider a retailer that primarily focuses on renting while also selectively meeting incidental sales demand. Once a unit is sold, the firm forgoes potentially recurring rental revenues from that unit during the remaining periods. Therefore, it is critical for a retailer to dynamically decide how much of its inventory to allocate for sales and rentals at each period. We first develop a consumer choice model that determines the fraction of the market that chooses renting over purchasing. We characterize the optimal inventory allocation policy and explore how market characteristics and prices impact inventory allocation. We discuss the value of dynamic allocation and observe that the profit improvement can be substantial. In addition, we propose a simple and efficient heuristic policy. Finally, we extend our analysis to study the optimal allocation policies for (i) a retailer that is primarily a seller that selectively meets rental demand, and (ii) a retailer that does not enforce any prioritization between rental and sales demand.

Incentivizing Farmers to Invest in Quality through Quality‐Based Payment

We consider a for‐profit cooperative that sets a quality‐based payment to its risk‐averse farmers in order to incentivize them to invest in quality improvements. The quality of the farmer's harvest is affected by his investments during the growing season. To satisfy both the farmer and the cooperative, the payment must be competitive with the open‐market prices. Cooperatives often set payments that mimic the open‐market prices; however, this practice fails to incentivize farmers to invest in quality. Our work is motivated by the evidence of farmer underinvestment in crop quality in the olive oil industry. We define and analyze a model of this system where farmers operate under random yield, quality, and open‐market price. We find that farmers consistently underinvest in crop quality under the payment policy that mimics the open‐market prices because (1) the cooperative can command a higher retail price than a farmer and (2) farmers are risk averse. We propose two alternative payment policies that are new to the agricultural literature, both of which can coordinate farmer decisions with the system but differ in terms of ease of implementation and susceptibility to risk aversion. We identify an easy‐to‐implement policy that can lead to meaningful gains when introduced in conjunction with crop insurance. We calibrate our model using data from the olive oil industry in Turkey and find a profit improvement of 10–15% over the current open‐market payment approach.

Corporate Social Responsibility and Firm’s Financial Performance: Evidence from the Banking Industry of Pakistan

The present work examines the effects of corporate social responsibility on profitability (CSR) of banking sector of Pakistan. The existing literature mainly focused on manufacturing industry of developed countries, while this study investigating the impact of CSR on financial sector of Pakistan. Secondary data were collected from 15 different banks from their annual reports for the period 2007-2016. Furthermore, study used summary statistics and two step system GMM to analyse the impact of corporate social responsibility on the financial performance of banks and found significant positive impact of CSR on financial sector performance. Moreover, the present work also observe that banks with good history of CSR spends less at present as they have established good reputation in society. Our study provides important empirical insights for regulators and other key stakeholders especially State Bank of Pakistan and banking industry of Pakistan for improvement in firm performance and profitability.

PRACTICAL REALIZATIONS OF A GENERALIZED ADVERTISING CYBERNETIC MODEL

Nowadays, a big number of various math models of advertising exists, however all of them describe only specific effects of advertising influence, thus, considering most modern advertising campaign utilize multiple ad channels for different purposes and are highly integrated, systematic and complex in its structure, math modelling of such campaigns remains a challenge for practitioners. As for today, there is no such framework that would be flexible and effective enough in solving tasks of advertising campaigns management. Hence, a framework of generalized advertising modelling and illustrations of its practical realizations for particular real cases would be a valuable addition to the research of the topic as well as a useful manual for practitioners. An objective of the current article is to illustrate various cases of generalized advertising model realizations, explain framework and algorithm of building model’s realizations for particular integrated marketing campaigns and show economic effects of proposed approach on an example of a real enterprise. Proposed framework is especially focused on modelling sequential and structured customer journeys and utilizing its systematic effects which become a modern standard of advertising activities among the enterprises. Article also includes considerations of modelling modern marketing tools such as WOMM and its integration into advertising campaigns utilizing more conventional tools which is illustrated for a first time in research literature on the topic. Proposed model supports taking into account indirect advertising tools which are not resulting in purchase directly, however, build awareness or move customers towards a purchase within a customer journey in any other way. Present research uses such methods as synthesis, generalization, case study and a real-field experiment. Results of this research can be useful for marketing practitioners as a guidance for building math models of bespoke advertising campaigns. Provided guide also shows how to turn built model into an optimization one and use it as a support for budgeting decision making process. Cases of practical application of proposed guide show improvement of profitability of advertising campaign ranging between 3.6% and 30% percent depending on market peculiarities.

Local Energy Communities in Spain: Economic Implications of the New Tariff and Variable Coefficients

The European Union advocates for legislative support to local energy communities. Measures include the promotion of dynamic energy allocation and discriminatory electricity tariffs such as the recent Spanish framework. However, the impact of these normative changes is not yet evaluated. This paper inquires into the impact of dynamic allocation coefficient and different electricity tariffs on the profitability of local energy communities. To do so, a linear optimisation model is developed and applied to real consumer data in Spain around a variable capacity photovoltaic generation plant. Comparing the economic performance of the static or variable power allocation under the effect of changing electricity tariffs. While both measures are beneficial, the new electricity tariffs result in larger profitability increases than the planned variable coefficients. The combination of measures allows for profitability improvements of up to 25% being complementary measures. However, installations that maximise the potential for electricity generation are still not as profitable due to the low purchase price of surplus energy. While discriminatory electricity price tariffs and variable allocation coefficients are positive measures, further measures are needed for these communities to install generation plants as large as the potential that each case allows.

Design of real-time cow behavior monitoring system based on wireless sensor networks and K-Mmeans clustering algorithm

Introduction: The present article is the product of the research whose code CS20.04, carried out during 2020. This work was supported by the Institute of Information Technology (IOIT), Vietnam Academy of Science and Technology (VAST).
 Problem: Animal monitoring is a significant problem in the agricultural sector. The primary purpose is to monitor the health of animals regularly. Consequently, animal welfare and product quality could be improved, leading to an improvement in profit. Cow behavior recognition system was considered as the right solution for cow monitoring. The requirements for this kind of system are economical, high performance, and real-time.
 Objective: The research objective is to design a real-time cow monitoring system based on wireless sensor networks and the K-means clustering algorithm.
 Methodology: A wireless sensor node was designed to measure the collar-mounted acceleration data using an accelerometer. Firstly, the collected data were classified into three classes based on the VeDBA (Vector of Dynamic Body Acceleration) feature using the K-means algorithm. Then, the thresholds for VeDBA in the previous step were used to classify new data.
 Results: Three behaviors (including feeding, lying, and standing) were classified in real-time with the accuracy of classification about 89%.
 
 Conclusion: The proposed system could be adapted in monitoring cow in real-time, the behavior classification could be implemented on the microcontroller. The results confirmed the reliability of the proposed system.
 Originality: The behavior classification could be implemented on the microcontroller for the first time in monitoring cow.
 Limitations: Only three behaviors were classified in the experiment.

Novel Energy Trading System Based on Deep-Reinforcement Learning in Microgrids

Inefficiencies in energy trading systems of microgrids are mainly caused by uncertainty in non-stationary operating environments. The problem of uncertainty can be mitigated by analyzing patterns of primary operation parameters and their corresponding actions. In this paper, a novel energy trading system based on a double deep Q-networks (DDQN) algorithm and a double Kelly strategy is proposed for improving profits while reducing dependence on the main grid in the microgrid systems. The DDQN algorithm is proposed in order to select optimized action for improving energy transactions. Additionally, the double Kelly strategy is employed to control the microgrid’s energy trading quantity for producing long-term profits. From the simulation results, it is confirmed that the proposed strategies can achieve a significant improvement in the total profits and independence from the main grid via optimized energy transactions.

Impoverishing Roots Will Improve Wheat Yield and Profitability Through Increased Water and Nitrogen Use Efficiencies

AbstractMore than a 60% increase in crop production is required by the 2050's to feed a growing world population. Understanding how plant functional traits and field management affect crop yields has the potential to improve agricultural productivity, minimize economic and environmental losses, and maximize food security. We explored the influence of winter wheat root characteristics and management on winter wheat growth, yield, and profit using a mechanistic and well‐tested ecosystem and crop model, ecosys. We applied and further tested ecosys at an agricultural farm growing winter wheat in Ardmore, Oklahoma, United States. The model accurately predicted observed shoot carbon ( = 0.95), soil moisture ( = 0.67), soil temperature ( = 0.91), and yield (percent error = 17%). Numerical optimization experiments were conducted to explore potential improvements of winter wheat yield and profit by modifying root characteristics, including root radius and root:shoot carbon transfer conductance, and fertilizer inputs. Our results show the potential for simultaneously improving winter wheat yields and profits. The optimum conditions were found to be in the range of root radius between 0.1 and 0.3 mm, carbon transfer conductance between 0.004 and 0.01 , and the currently applied fertilizer rate of 112 kg . Under these conditions, improvements in yields and profits of up to approximately 25% and 110%, respectively, were modeled compared to those under baseline root traits. These improvements were achieved by impoverishing root structures, thereby increasing nutrient allocation to grains. Our results also demonstrate and motivate model structures that integrate the complex network of plant physiology, soil nutrient biogeochemistry, hydrology, and management.

Development of a Job Applicants E-government System Based on Web Mining Classification Methods

Governmental establishments are maintaining historical data for job applicants for future analysis of predication, improvement of benefits, profits, and development of organizations and institutions. In e-government, a decision can be made about job seekers after mining in their information that will lead to a beneficial insight. This paper proposes the development and implementation of an applicant's appropriate job prediction system to suit his or her skills using web content classification algorithms (Logit Boost, j48, PART, Hoeffding Tree, Naive Bayes). Furthermore, the results of the classification algorithms are compared based on data sets called "job classification data" sets. Experimental results indicated that the algorithm j48 had the highest precision (94.80%) compared to other algorithms for the aforementioned dataset.

Clariant significantly increased profitability in 2Q 2021 on the back of double-digit sales growth: 1H 2021 – Higher sales in all business areas and double-digit profitability improvement

This article contains data outlining the effects of increased storm intensity on estuarine salt marshes, previously evaluated in Pannozzo et al. (2021), using the Ribble Estuary, in North West England, as a case study. The hydrodynamic model Delft3D was used to simulate various surge height scenarios and evaluate the effects of increasing surge height on the sediment budget of the system. The data shows that an increase in storm intensity (i.e. surge height) promotes flood dominance and triggers a net import of sediment, positively contributing to the sediment budget of the marsh platform and the estuarine system. The timing of the storm surge relative to high or low tide, the duration of the surge and the presence of vegetation do not cause major changes in the sediment budget. This dataset could be used to evaluate how increased storm intensity might influence the sediment budget of estuaries in comparison to other types of coastal systems (e.g., bays) to illustrate how the response of salt marshes to increased storm intensity varies with a change in the hydrodynamics and sediment delivery dynamics of the system.

Do green policies catalyze green investment? Evidence from ESG investing developments in China

This study examines the heterogeneous performance of ESG investing in China before and after 2016, when the “Guidelines for Establishing a Green Financial System” was announced. In the portfolio analysis, high ESG portfolios earn significantly higher abnormal returns compared to low ESG portfolios in the post-2016 context, but this is not the case pre-2016. This outperformance is not closely related to common firm characteristics. Next, the stock analysis also shows that good ESG profiles predict higher future excess returns on average post-2016. The rising performance of good ESG stocks largely depends on equity cost advantage other than profitability improvement.

Optimal Operation and Financing Decisions in Green Supply Chain with a Capital-Constrained Manufacturer

Capital constraint, immensely existing in practice, became major stressors for manufacturers during the green research and development (R & D) triggered by managers integrating green concept into their business models. Considering the initial capital of a capital-constrained manufacturer, this paper formulates a Stackelberg game model comprising a manufacturer and a retailer, to discuss the optimal operation and financing decisions under the bank financing channel and trade credit financing channel, to detect the relationship between the manufacturer’s initial capital and green R & D investment, and to find which financing channel is better by comparing the two financing channels when the same initial capital is set. According to the above analysis, the results find that the capital-constrained manufacturer prefers financing only when meeting certain conditions. Furthermore, financing might be detrimental to the manufacturer but always beneficial to the retailer. Especially, under trade credit financing channel, the profit improvement of the retailer is higher than the manufacturer in the same financing channel, which suggests that the retailer has strong internal motivation to cooperate with the manufacturer from the perspective of financing.

The anti-collusion dilemma: Information sharing of the supply chain under buyback contracts

While previous studies did not consider that buyback contracts in collusive research of supply chains and the buyback contract particularly strengthens collusion between retailers in the supply chain, we address a new research question: What is the effect of different retailer collusive patterns and information sharing mechanisms on the benefits of each member in supply chains with buyback contracts? To answer this research question, we develop four two-echelon supply chain models. The model consists of one supplier and two competing retailers. We consider the two dimensions of collusion mode and information sharing type and derive the optimal equilibrium solutions for each member. The theoretical analysis demonstrates the following results: (i) With explicit collusion, retailers are less willing to vertically share information with the supplier. (ii) Tacit collusion with low demand uncertainty motivates information sharing because the wholesale price can correctly convey the supplier’s information to retailers. (iii) Increasing the probability of high demand or reducing the buyback rate can benefit all members in the supply chain. (iv) Different from the wholesale price and revenue sharing contract, in the buyback contract, the whole supply chain sustains losses from vertical information sharing under the condition of collusion with high demand uncertainty. A numerical experiment shows interesting insights: Although explicit collusion generally brings profit improvement to retailers, they prefer to choose tacit collusion to effectively identify price signals when the demand uncertainty is lower. We check the robustness of results and find that the main results will not be changed in the extended model where the buyback price is lower than the wholesale price.

Pelayanan Elektronik dan Keputusan Berwisata: Studi Pada Obyek Pariwisata Edukasi Museum

Tourism is one of the strengths in accelerating and supporting regional profitable improvement. The purpose of this study was to determine whether the parties involved in realizing the decision to visit tourist thourgh tourism services. This research is quantitative research conducted using quantitative methods and data collection techniques are carried out through observation, interviews, and recording literartur to managers and tourists in the tourist attraction of the central jawa ranggawarsita museum. The results of testing the product mix coefficient and the decision to visit show the value of t count > t table (10.591>1.661), so Ho is rejected, indicating that there is a significant influence between the product mix and the decision to visit tourists in the central java museum ranggawarsita thus the hypothesis is answered from the research on the effect of the museum product mix on the decision to visit tourists at the ranggawarsita central java museum. The manager of a tourist attraction requires efforts to increase the accesibility and facilities contained in these tourist objects, so that tourists feel comfortable, considering that tourists are one of the sources of income for the community. Keywords: E-Commerce and decisions visiting