Input Factors Research Articles

An investigation of the surface topography of wire tool and the modelling of wire electro-spark machined ti-alloy using ANN

Abstract Titanium and its alloys are in demand in the field of biomedical and aerospace engineering because of its high strength-to-weight ratio and anti-corrosive properties. WESM emerged as a processing method as traditional methods could not process because of strain hardening and low material conductivity; however, wire tool consumption contributes to high costs while processing with WESM. The current Article investigates the effect of input factors on wire consumption and their significance level on wire consumption. A total of 81 experimental runs were conducted while taking input factors Spark on, Spark off time, peak current and servo voltage at three levels, each using a full factorial approach. Experimental results were analysed using ANOVA and MLR (Multi-Linear regression). Moreover, ANN modelling is done to model the wire consumption using a 4-10-1 architecture that increased the coefficient of correlation (R2) up to 0.991. Lastly, after processing, the wire tool surface is evaluated using a scanning electron microscope to assess the effect of various input energy levels and find input combinations for minimum wire consumption

Measuring Total Factor Productivity in General Technical Progress Framework

The classical Solow's total factor productivity accounting assumes that technical progress is Hicks neutral, which is a special situation in the reality of world economy. This paper expands the setting of technical progress into general technical progress framework, which can cover Hicks neutral technical progress, Harrod neutral technical progress, Solow-neutral technical progress, and various factor-biased technical changes. According to the principle of statistical index number, this paper decomposes the output index into a total factor input index and a total factor productivity index, and adopts normalized CES production function with factor-augmenting technical progress to derive the calculation formulas of the total factor input index and the total factor productivity index, and constructs a new economic growth accounting system, and finds the counteraction and compensation mechanism for diminishing marginal returns. If the factor substitution elasticity is 1 or there is no technical progress bias and factor allocation bias, then the new accounting equation degenerates into the classic Solow growth accounting equation. The new accounting system can measure the influence of total factor input and total factor productivity to economic growth, but also can measure the influences of factor input intensity and factor allocation bias in the growth rate of total factor input, and the influences of technical progress intensity and technical progress bias in the growth rate of total factor productivity. Therefore it is more precise and accurate than classical method.

Evaluation of liquefaction potential in central Taiwan using random forest method

AbstractLiquefaction is a significant geotechnical hazard in seismically active regions like Taiwan, threatening infrastructure and public safety. Accurate prediction models are essential for assessing soil susceptibility to liquefaction during seismic events. This study evaluates liquefaction potential in central Taiwan using the random forest (RF) method. The RF models were developed with a dataset of 540 soil and seismic parameter sets, including depth, effective and total overburden stresses, SPT-N values, fine soil content, earthquake magnitude, peak ground acceleration, and historical liquefaction occurrences. Rigorous validation techniques, such as cross-validation and comparisons with observed liquefaction events, confirm the RF model’s effectiveness, achieving an accuracy of 98.89%. The model also quantifies predictor importance, revealing that the SPT-N value is the most critical soil factor, while peak ground acceleration is the key seismic factor for liquefaction prediction. Notably, the RF model outperforms simplified procedures in accuracy, even with fewer input factors. Our case studies show that an accuracy of over 95% can still be achieved, highlighting the RF model’s superior performance compared to conventional methods, which struggle to reach similar levels.

Process optimization and finite element modeling in magnetorheological abrasive finishing of SS-316L with surface characterization

This study explores the potential of Magnetorheological Abrasive Finishing (MRAF) for ultra-precision finishing of SS-316L using a high-performance Nd-Fe-B (N-35 grade) magnetic tool. Key parameters, including the relationship between normal force (Fn) and working gap, magnetic flux distribution, and finishing spot area, were analyzed using ANSYS 2020 R1® Magnetostatic module simulations. Material removal rate (MRR) and surface roughness (Ra) were optimized through response surface methodology (RSM) using a central composite design (CCD) with two primary input factors: SiC abrasive volume fraction (5%, 10%, 15%) and abrasive mesh size (800, 1000, 1200). Statistical analysis via ANOVA revealed a significant influence of process variables, achieving a remarkable surface finish with a minimum Ra of 52 nm in just 60 min, utilizing an MR fluid with 10% SiC abrasives. Microscopic and spectroscopic evaluations confirmed enhanced surface morphology and microstructural refinement. Moreover, bioactivity studies in simulated body fluid highlighted improved hydroxyapatite layer formation on the finished SS-316L samples, underscoring the process’s potential for biomedical applications.

Effect of Oxide’s Thermophysical Properties on 2205 Duplex Stainless Steels ATIG Welds

Duplex stainless-steel grade 2205 (2205 DSS) is the most widely used of the current duplex materials. The duplex steel alloy is characterized by high strength and high corrosion resistance through enhancing nitrogen and molybdenum contents. The activated tungsten inert gas (ATIG) welding technique uses the same equipment as tungsten inert gas (TIG), but prior to the welding operation, a thin layer of flux is deposited. Activation fluxes are known to influence the shape and energy characteristics of the arc. They promote the change in shapes and dimensions of the welds, namely, increasing the depth and narrowing the weld width. This work is dedicated to investigate the influence of the thermophysical properties of individual metal oxide fluxes on 2205 DSS welding morphology. It helps also to identify the recommended flux properties in order to perform full penetrated ATIG welds. Thirteen kinds of oxides (SiO2, TiO2, Fe2O3, Cr2O3, ZnO, Mn2O3, V2O5, MoO3, Co3O4, SrO, ZrO2, CaO, and MgO) have been tested and three current intensity levels (120, 150 and 180 A) have been considered. The results showed that the main input factors affecting the weld depth (D) were the welding current intensity with a contribution of up to 53.36%, followed by the oxides enthalpy energy with 15.05% and then by the difference between the oxides and the base metal of 2205 DSS (BM 2205 DSS) melting points with a contribution of 9.71% of the data variance. The conditions on individual oxides’ thermophysical properties to achieve full penetrated weld beads have been also revealed.

User Views on Online Sexual Health Symptom Checker Tool: Qualitative Research.

Delayed diagnosis and treatment of sexually transmitted infections (STIs) contributes to poorer health outcomes and onward transmission to sexual partners. Access to best-practice sexual health care may be limited by barriers such as cost, distance to care providers, sexual stigma, and trust in health care providers. Online assessments of risk offer a novel means of supporting access to evidence-based sexual health information, testing, and treatment by providing more individualized sexual health information based on user inputs. This developmental evaluation aims to find potential users' views and experiences in relation to an online assessment of risk, called iSpySTI (Melbourne Sexual Health Center), including the likely impacts of use. Individuals presenting with urogenital symptoms to a specialist sexual health clinic were given the opportunity to trial a web-based, Bayesian-powered tool that provides a list of 2 to 4 potential causes of their symptoms based on inputs of known STI risk factors and symptoms. Those who tried the tool were invited to participate in a once-off, semistructured research interview. Descriptive, action, and emotion coding informed the comparative analysis of individual cases. Findings from interviews with 14 people who had used the iSpySTI tool support the superiority of the online assessment of STI risk compared to existing sources of sexual health information (eg, internet search engines) in providing trusted and probabilistic information to users. Additionally, potential users reported benefits to their emotional well-being in the intervening period between noticing symptoms and being able to access care. Differences in current and imagined urgency of health care seeking and emotional impacts were found based on clinical diagnosis (eg, non-STI, curable and incurable but treatable STIs) and whether participants were born in Australia or elsewhere. Online assessments of risk provide users experiencing urogenital symptoms with more individualized and evidence-based health information that can improve their health care-seeking and provide reassurance in the period before they can access care.

Inequality in agricultural greenhouse gas emissions intensity has risen in rural China from 1993 to 2020.

Reducing greenhouse gas (GHG) emissions in crop production while ensuring emission equity is crucial for sustainable agriculture in China, yet long-term large-scale data on GHG emissions intensity (GEI) are limited. Using an extensive dataset based on surveyed farm households (n > 430,000 households) from 1993 to 2020, we reveal that 2015 was a turning point for GEI levels, which dropped 16% in 2020, while inequality-measured as average GHG emissions per unit planted area-increased 13%. The key driving forces behind such trends included farmland input, all other inputs, agricultural labour input and total factor productivity but not capital input. Notably, farmland input and all other inputs contributed to 80% of the inequality, while contribution of total factor productivity gradually declined and was replaced by migration-induced agricultural labour input differences. Reducing GEI levels and guarding against widening inequality require optimizing production factor inputs.

Parameter Sensitivity Analysis for Diesel Spray Penetration Prediction Based on GA-BP Neural Network

Machine learning has started to be used in engine research to optimize combustion and predict fuel spray characteristics. This paper presents the development of a machine learning model using a Genetic Algorithm-Backpropagation (GA-BP) neural network to predict spray penetration. The GA-BP neural network was selected for its ability to optimize neural network weights and biases, thereby improving model convergence and avoiding local minima, which are common challenges in complex, non-linear problems such as spray prediction. The model was trained using experimental data from diesel injector spray tests, and its accuracy was evaluated through parametric sensitivity analysis, examining the influence of various input factors. A comparison between the machine learning model and the traditional empirical formulas of spray penetration revealed that the machine learning model achieved greater accuracy. In terms of the sensitivity to inputs, it is interesting to find that the cognition of machines is different from that of humans. When an input parameter does not have any functional relationship with other input parameters, the absence of this input parameter will lead to a significant decrease in the accuracy of the output result. The results demonstrate that the machine learning approach offers higher accuracy and better generalizability compared to traditional empirical methods. This study recommends the ways to get better results of penetration prediction with BP neural networks, which is efficient in training and utilizing Artificial Neural Networks (ANNs).

Measurement of the Macro-Efficiency of Hydropower Plants in Nigeria Using Transfer Functions and Data Envelopment Analysis

Persistent power challenges plague Nigeria, with hydropower constituting a vital component of the country's energy mix. This study assessed the macro efficiency of three operational hydropower plants in Nigeria using three distinct methods: Constant Return to Scale Method of Data Envelopment Analysis (DEA-CRS), Input-oriented Variable Return to Scale Method of Data Envelopment Analysis (DEA-VRS), and System's Coefficient of Performance Methodology (SCOPM) employing transfer functions. Input factors included man-hours, plant capacity, and water flow, while energy generation was the output. DEA-VRS revealed Shiroro and Jebba as the most efficient, while Kainji was the least. DEA-CRS indicated Shiroro as the most efficient and Kainji as the least. SCOPM indicated Jebba as the most efficient and Shiroro as the least. SCOPM's higher standard deviation suggests better discrimination among plants. DEA-VRS result showed that the scale of the biggest power plant, Kainji, has some effects on its efficiency. The study recommends the adoption of SCOPM by electricity utility operators and regulators for performance improvement due to its robust results. The contributions of the research are significant because of the novel application of DEA and SCOPM to measure the efficiency of power plants in Nigeria, and SCOPM for measurement of macro efficiency of power plants.

Can companies' input of data factor eliminate investors' home biases?

Data as a new production factor plays an increasingly crucial role in influencing investor decision-making. This paper investigates how the data factor input, measured using a two-stage regression decomposition approach, impacts investors' home biases. Analyzing a sample of Chinese listed companies from 2012 to 2022, we find that: 1) companies' input of data factor effectively reduces investors' home biases; 2) this reduction is primarily driven by a decrease in information asymmetry; 3) the mitigating effect of data factor input is further strengthened by external factors such as heightened attention from analysts and stronger corporate governance. Additionally, our results reveal that this mitigating effect is more pronounced among companies facing high financing constraints, possessing low reputations, operating in low-technology industries, or located in regions with underdeveloped digital financial infrastructure. These findings offer new insights into underscore the critical role of data factor in shaping capital markets.

Surface Mechanical Property Prediction and Process Optimization of 18CrNiMo7-6 Carburized Steel Stator Guide Based on Radial Basis Function Neural Network and NSGA-II Algorithm

The carburizing process is a key technology that affects the mechanical properties of the surface of the hydraulic motor stator guide rail, and the related process parameters have an important influence on surface hardness, the thickness of the carburized layer, and the deformation of the guide rail. However, at present, the relationship between the carburizing process parameters and the surface mechanical properties of the target is not clear. This paper proposes a “hardness prediction and process parameter optimization” method. Firstly, a finite element model is established, with carburizing time, temperature, and carbon potential as the three input factors; the optimal Latin hypercubic experimental design and sensitivity analysis are applied. Secondly, surface hardness, carburized layer thickness, and deformation are taken as the output values, and an RBF neural network is used to construct the prediction model. The results show that the RBF neural network can be accurately used for the prediction of surface hardness, the thickness of the carburized layer, and deformation, and for the optimization of process parameters. The optimized parameters of surface hardness and the thickness of the carburized layer were increased by 4.2% and 5.1%, respectively, and the deformation amount was reduced to 0.31 mm, achieving the goal of optimal design.

Examining the contribution of lithology and precipitation to the performance of earthquake-induced landslide hazard prediction

Earthquake-induced landslides (EQIL) are one of the most catastrophic geological hazards. Immediate and swift evaluation of EQIL hazard in the aftermath of an earthquake is critically important and of substantial practical value for disaster reduction. The selection of influencing factor layers is crucial when using machine learning methods to predict EQIL hazard. As important input factors for EQIL hazard models, lithology and precipitation are extensively employed in forecasting EQIL hazard. However, few work explored whether these layers can improve the accuracy of EQIL hazard predictions. With Random Forest (RF) models, we employed a traditional and a state-of-the-art sampling strategy to assess EQIL modelling with and without lithology and precipitation data for the 2022 Luding earthquake in China. First, by excluding both factors, we used eight other influencing factors (land use, slope aspect, slope, elevation, distance to faults, distance to rivers, NDVI, and peak ground acceleration) to generate a landslide hazard map. Second, lithology and precipitation were separately added to the original EQIL hazard models. The results indicate that neither lithology nor precipitation have positive effects on the prediction of EQIL for both sampling strategies. The high-risk areas (or low-risk areas) tend to cluster within certain lithology types or precipitation ranges, which significantly affects the accuracy of the hazard map. Additionally, the model with the state-of-the-art sampling strategy deteriorates more than the model with the traditional sampling strategy. We believe this is very likely due to the strong spatial clustering of negative sample points caused by the latest sampling strategy. Our findings will contribute to the assessment of post-earthquake landslide hazards and the advancement of emergency disaster mitigation efforts.

Prediction of the Relative Resource Abundance of the Argentine Shortfin Squid Illex argentinus in the High Sea in the Southwest Atlantic Based on a Deep Learning Model

To analyze the impact of the marine environment on the relative abundance of Illex argentinus (high and low categories) in the southwest Atlantic, this study collected logbook data from Chinese pelagic trawlers from December 2014 to June 2024, including vessel position data and oceanographic variables such as sea surface temperature, 50 m and 100 m water temperature, sea surface salinity, sea surface height, chlorophyll-a concentration, and mixed layer depth. Vessel positions were used to enhance the logbook data quality, allowing an analysis of the annual trends in the resource center of this squid at a spatial resolution of 0.1° × 0.1° and a temporal resolution of ten days. The findings showed that the resource center is primarily located around 42° S in the north and between 45° S and 47° S in the south, with a trend of northward movement during the study period. Additionally, we constructed two ensemble learning models based on decision trees—AdaBoost and PSO-RF—aiming to identify the most critical environmental factors that affect its resource abundance; we found that the optimal model was the PSO-RF model with max_depth of 5 and n_estimators of 46. The importance analysis revealed that sea surface temperature, mixed layer depth, sea surface height, sea surface salinity, and 50 m water temperature are critical environmental factors affecting this species’ resources. Given that deep learning models generally have shorter running times and higher accuracy than other models, we developed a CNN-Attention model based on the five most important input factors. This model achieved an accuracy of 73.6% in forecasting this squid for 2024, predicting that the population would first appear near the Argentine exclusive economic zone around mid-December 2023 and gradually move east and south thereafter. The predictions of the model, validated through log data, maintained over 70% accuracy during most periods at a time scale of ten days. The successful construction of the resource abundance forecasting model and its accuracy improvements can help enterprises save fuel and time costs associated with blind searches for target species. Moreover, this research contributes to improving resource utilization efficiency and reducing fishing duration, thereby aiding in lowering carbon emissions from pelagic trawling activities, offering valuable insights for the sustainable development of this species’ resources.

Does the Adjustment of Industrial Structure Restrain the Income Gap between Urban and Rural Areas

Abstract Industrial structure adjustment is a process that involves reconfiguring input factors to enhance productivity and efficiency. A crucial aspect of this adjustment is the transformation of the labor force composition, which subsequently affects the employment structure. Consequently, variations in productivity levels lead to the cross-departmental migration of workers, resulting in changes to the income disparities among employees. This article examines the impact of China’s industrial structure adjustment and labor mobility on the urban–rural income gap between 1990 and 2019. Employing an empirical approach, the study investigates the influence of industrial structure adjustment and the direction of labor mobility on the urban–rural income gap. The findings indicate that the quality of industrial structure adjustment contributes to the widening of the urban–rural income gap, whereas labor mobility helps to narrow it. In regions experiencing a net influx of labor, superior industrial structure adjustment amplifies the urban–rural income gap, whereas labor migration diminishes it. Conversely, in regions witnessing a net outflow of labor, superior industrial structure adjustment reduces the income gap, while labor mobility widens the urban–rural income disparity. Notably, in more developed areas of China, the quality of industrial structure adjustment has widened the income gap, whereas labor mobility has narrowed the urban–rural income gap. However, in less developed regions, labor mobility exacerbates the urban–rural income gap.

Assessment of sowing environment for wheat cultivars and evaluation of growing degree days for phenological stages in the Northwest Himalayan region of Himachal Pradesh

BackgroundThe date of sowing is a non-monetary input factor that plays a crucial role in field crop management. Based on the sowing time, management practices of crop production affect crop growth, development, and yield. Under varied environmental conditions, this factor may be useful in addressing low yields and combating/escaping stresses either due to diseases or due to any unfavourable environment.ResultsA field experiment was conducted during the rabi season of 2017–18 and 2018–19 to study the accumulated growing degree days (GDD) and performance of six wheat cultivars (HS 562, HD 2967, HD 3086, HI 1544, MACS 6222 and WR 544) under four sowing dates (5th November, 25th November, 15th December and 5th January). The study revealed that 5th November is the best date for sowing resulting in higher pooled grain yield of 4.85 Mg ha−1 and 3.94 Mg ha−1 at Bajaura and Malan, respectively. Significant reductions in the grain yield and yield attributes were observed when the date of sowing was set beyond 5th November. Among the cultivars tested, HS 562 gave a significantly higher pooled grain yield of 4.46 and 3.75 Mg ha−1 at Bajaura and Malan, respectively. Pooled data on physiological maturity revealed that variety HS 562 took the highest calendar days (168 days) and GDD (1573.42 °C days) to attain physiological maturity at Bajaura while at Malan HI 1544 and HD 3086 took the highest calendar days (156 and 155 days, respectively) and GDD (2055.41 and 2051.81 °C days, respectively) to attain their physiological maturity.ConclusionFrom the study, it can be concluded that the early date of sowing provides the crop with ample duration to accumulate growing degree days resulting in a higher accumulation of photosynthates resulting in significantly higher yield attributes and grain yield. Therefore, 5th November is recommended as the best time for sowing of wheat crop whereas the sowing must not be delayed beyond 25th November. HS 562 was superior among the cultivars and is recommended for Malan and Bajaura regions for attaining higher yield.

The carbon emission reduction benefits of the transformation of the intensive use of cultivated land in China

The transformation of the intensive use of cultivated land has an important impact on agricultural carbon emissions (ACE). However, the existing research on the impact mechanisms of the transformation of intensive use of cultivated land on carbon emissions is not clear. In view of this, this study constructs the impact mechanism of the transformation of intensive use of cultivated land on carbon emissions from the perspective of production factors substitution based on the theory of induced technological change and the theory of marginal returns. Using the Cobb-Douglas production function, this study identifies the transformation patterns of intensive use of cultivated land in China from 1980 to 2021. Furthermore, this study simulates the carbon emission reduction benefits brought about by the transformation of the intensive use of cultivated land by considering the cost of agricultural production. The results show that, first, from 1980 to 2021, the price of agricultural labor increased by 224.10%, while the price of other agricultural production factors significantly decreased. China’s intensive use of cultivated land methods has undergone four stages: agricultural labor–intensive (ALI), yield–enhancing factor–intensive (YEFI), labor–saving factor–intensive (LSFI), and innovation factor–intensive (IFI). In 2021, most of China’s provinces were in the YEFI or ALI stages. Second, between 1993 and 2021, the transformation of China’s intensive use of cultivated land brought about an 87.48% increase in carbon emissions, while the spatial clustering effect of carbon emissions weakened. Furthermore, the results show that, the transition of the use of cultivated land from ALI to YEFI and YEFI to LSFI will reduce carbon emissions by 7.89%, and input factors costs will increase by 23.85%. If all regions, except for those in the IFI stage, transition to the LSFI model, it will result in a 17.56% reduction in carbon emissions, with a corresponding increase of 33.40% of the input factors input costs. This research enriches the theoretical foundation of literature about the transformation of the intensive use of cultivated land. It also expands the research perspective on agricultural carbon emissions. This research also helps provide references for the direction of the transformation of the intensive use of cultivated land in China and the achievement of the dual carbon goals.

Digital agriculture’s impact on carbon dioxide emissions varies with the economic development of Chinese provinces

Digital empowerment is a dynamic process of complementary digital technology and production factors and plays a critical role in achieving the dual carbon objectives of the carbon peak by 2030 and carbon neutrality by 2060. Here, we use the input-output method, employing panel data on environmental and social factors from 30 provincial-level regions in China from 2012 to 2021. We explore the impact of digital empowerment development on agricultural carbon dioxide emissions and underlying mechanisms. We found that the relationship between digital empowerment and carbon dioxide emissions is nonlinear and follows an inverted U-curve. The carbon dioxide emissions increase as digital empowerment increases and decrease when digital empowerment crosses the inflection point of 0.0862. Digital empowerment reduces agricultural carbon emissions by optimizing carbon-intensive factor inputs such as fertilizers and improving factor allocation efficiency. Our research provides evidence for policymakers to enable the promotion of digital empowerment of farmers across Chinese provinces.

Hydrological classification of mine pit lakes using modelling experiments

With the growing global prevalence of open-pit mining activities, there is an increasing necessity for sustainable mine life cycle plans with an early outlook towards mine closure. A major consideration in mine closure planning is the potential formation of lakes in the mine void and how these “pit lakes” can be managed to minimise risks and, if possible, create benefits. Understanding the long-term interactions between pit lakes, groundwater, and surface water systems is essential for that purpose. While numerous site-specific studies have been undertaken, there have been no studies that aim to provide a general, broadly applicable understanding of how pit lake hydrology relates to geographical context, and no efforts to hydrologically classify pit lakes using geographical criteria. This research employs an integrated generic pit lake water balance model to examine mine pit lake interactions with the surrounding surface water and groundwater. Simulating 243 scenarios, the influence of five input factors (climate, hydraulic conductivity, regional groundwater level, catchment area and pit slope) on pit lake behaviour up to 6000 years beyond the closure of the mine was considered. The assessment focused on four pit lake hydrological attributes once the system reached equilibrium: water level, time to equilibrium, the fraction of days where the pit recharges groundwater, and the fraction of days where there is surface overflow from the lake. All scenarios were assigned to one of five hydrological classes based on the interactions between the pit lake and the surrounding surface water and groundwater. Our findings show that, in many contexts, general data on climate type and subsurface hydraulic conductivity can yield reliable predictions of a pit lake's long-term hydrological classification without having to develop a detailed, site-specific pit lake model. The classification needs improvements in non-arid climates where inter-annual variation in rainfall is pronounced. The pit lake classification is particularly valuable for first-pass risk assessments to determine whether site-specific modelling is required.

Performance support team effectiveness in elite sport: a narrative review

ABSTRACT In pursuit of competitive advantage, elite sport organizations are increasingly relying on the support of diverse sport medicine and sport science staff, who are collectively referred to as the performance support team. Whilst it has been suggested that the accumulative input from diverse multiteam systems has the potential to contribute to a resultant whole that is greater than the sum of its parts, team effectiveness is reliant on more than the mere aggregate of diverse experts. The aim of this narrative review was to appraise, summarize, and apply pertinent performance support team literature to a conceptual framework for teamwork and team effectiveness in sport. It specifically explores team effectiveness, with reference to its inputs (i.e. characteristics of individual, team, and environment) and mediators (i.e. team processes and emergent states). This review provides an insight into the individual (i.e. disciplinary knowledge, technical competency, and interpersonal qualities), team (i.e. team composition and leadership), and external (i.e. hierarchical arrangement and environmental factors) inputs that are necessary for team effectiveness, as well as the mediators (i.e. behavioral processes and emergent states) that translate such inputs into desired outcomes.

Marketing inputs and outcome heterogeneity: Using a quantile regression framework in the entertainment industry

AbstractThe success of entertainment products such as movies or books varies tremendously, and managers strive to increase the odds by deciding on the right marketing input. Aiming to improve managerial decision making, we suggest and test a quantile regression framework to detect outcome heterogeneity effects of marketing inputs in the entertainment industry. By analyzing the spread of the .9 and the .1 conditioned quantile to the .5 (median) conditioned quantile, we study how much an increase (decrease) of an input factor (star power and quality) changes the spread of the expected outcome (revenues and sales). The spread serves as an indicator for the heterogeneity effect of the input factor regarding the outcome. In two empirical studies, we show how marketing instruments increase (or decrease) outcome heterogeneity by estimating quantile regressions and provide generalizable findings regarding the outcome heterogeneity effects of star power (increases outcome heterogeneity) and quality evaluations (reduces outcome heterogeneity) in the entertainment industry.