Journal articles on the topic 'Market segmentation Mathematical models'
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Peng, Chih-Piao, Chiu-Chi Wei, Hsien-Hong Lin, and Su-Hui Chen. "Artificial Intelligence in Market Segment Portfolio for Profit Maximization." Engineering Economics 33, no. 4 (October 26, 2022): 386–97. http://dx.doi.org/10.5755/j01.ee.33.4.29543.
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This paper proposes an approach to select a market segment portfolio to maximize overall profit. The study first uses artificial intelligence algorithms to select the market segments with high profitability. The mathematical programming model is then used to identify the most profitable market segment portfolio. The single-objective programming model is used to find the optimal profit for the baseline condition, and a sensitivity analysis is performed to understand the impact of the variable changes on the results. Then, a multi-objective programming model helps to identify the best profit when the evaluated items reach extreme values. A sensitivity analysis is conducted to reveal the impact of the variable changes on the results. The above results are compared with those of the scoring method. It is found that the artificial intelligence algorithm combined with mathematical programming models can indeed find the market segmentation portfolio with better profits than the conventional methods.
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Raza, Syed Asif. "The impact of differentiation price and demand leakage on a firm’s profitability." Journal of Modelling in Management 10, no. 3 (November 16, 2015): 270–95. http://dx.doi.org/10.1108/jm2-07-2013-0035.
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Purpose – The purpose of this paper is to study the impact of differentiation price which has been utilized to segment demand, but results in imperfect segmentation. The use of a differentiation price is among the most widely used Revenue Management (RM) techniques to segment a firm’s demand to augment profitability. Design/methodology/approach – Mathematical models are developed for a firm’s RM which use a differentiation price to categorize its market demand into two segments. Three distinct demand situations are considered: price-dependent deterministic demand, price-dependent stochastic demand whose distribution is known and price-dependent stochastic demand whose distribution is unknown. Models are analyzed to determine optimal joint control of a firm’s pricing and inventory decisions for each market segment. Findings – The analysis of the firm’s RM model has shown that revenue is jointly concave in pricing and order quantity. In most demand situations, closed-form mathematical expressions for optimal pricing and inventory are obtained. Research limitations/implications – In RM models developed in this paper, a firm only selects a differentiation price. Thus, an optimal selection of the differentiation price along with the pricing and inventory decisions may lead to an additional profitability which has not been explored in this research. Practical implications – The findings reported are relevant to RM managers and practitioners and help them to calibrate their optimal revenues by segmenting markets using a differentiation price. Social implications – This paper provides a quantitative perspective of a firm’s decision on the use of the differentiation price and the market response. Originality/value – The paper provides a firm’s optimal decision on pricing and inventory when it experiences demand leakage due to categorizing its market demand into two segments using a differentiation price.
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van Hattum, Pascal, and Herbert Hoijtink. "Market Segmentation Using Brand Strategy Research: Bayesian Inference with Respect to Mixtures of Log-Linear Models." Journal of Classification 26, no. 3 (December 2009): 297–328. http://dx.doi.org/10.1007/s00357-009-9040-1.
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Savelyeva, Irina, Dmitry Kandaurov, Natalia Pravdina, and Natalya Dzenzelyuk. "WORLD COMMERCIAL SPACE MARKET: POSITIONING OF COUNTRIES AND SEGMENTS OF THE SATELLITE INDUSTRY." Bulletin of the South Ural State University series "Economics and Management" 16, no. 1 (2022): 149–64. http://dx.doi.org/10.14529/em220115.
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The commercialization of space activities is becoming an increasingly significant trend in the development of the global space industry. This trend necessitates the transformation of the Russian space market as well. One of the topical issues of this transformation from an economic point of view is the question of finding the most promising market niches for domestic actors in the space industry. The article is devoted to the analysis of trends in the global space market and the identification of the most promising market segments. The theoretical basis of the study are the general provisions of marketing theory, including the concept of consumer segmentation and the concept of market positioning. The methodological basis of the study includes general scientific methods (analysis, synthesis, deduction, logical approach, comparative analysis), special economic methods of analysis (graphical method), as well as the use of qualitative and quantitative methods of collecting information. The sources of information in the work are the current regulations, data from reputable foreign research organizations, publications of foreign and Russian specialists in the field of space activities. Based on the market segmentation adopted in world practice, trends in the development of the world market in various segments are determined. The fastest growing segments of satellite services are identified, including the segment of remote sensing of the Earth, the broadband segment and the segment of mobile satellite communications, which includes data transmission services for the Internet of things. Based on an estimate of revenue, revenue growth rates and the rate of satellite renewal in orbit, the positions of countries in the respective segments were determined, the leader among which are the United States. The results of the study can become the basis for further study of the mechanisms and business models used by various countries in the field of commercial space, as well as for orienting existing participants in space activities to the most promising market segments.
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Mohammed, Hussam J., Shumoos Al-Fahdawi, Alaa S. Al-Waisy, Dilovan Asaad Zebari, Dheyaa Ahmed Ibrahim, Mazin Abed Mohammed, Seifedine Kadry, and Jungeun Kim. "ReID-DeePNet: A Hybrid Deep Learning System for Person Re-Identification." Mathematics 10, no. 19 (September 28, 2022): 3530. http://dx.doi.org/10.3390/math10193530.
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Person re-identification has become an essential application within computer vision due to its ability to match the same person over non-overlapping cameras. However, it is a challenging task because of the broad view of cameras with a large number of pedestrians appearing with various poses. As a result, various approaches of supervised model learning have been utilized to locate and identify a person based on the given input. Nevertheless, several of these approaches perform worse than expected in retrieving the right person in real-time over multiple CCTVs/camera views. This is due to inaccurate segmentation of the person, leading to incorrect classification. This paper proposes an efficient and real-time person re-identification system, named ReID-DeePNet system. It is based on fusing the matching scores generated by two different deep learning models, convolutional neural network and deep belief network, to extract discriminative feature representations from the pedestrian image. Initially, a segmentation procedure was developed based on merging the advantages of the Mask R-CNN and GrabCut algorithm to tackle the adverse effects caused by background clutter. Afterward, the two different deep learning models extracted discriminative feature representations from the pedestrian segmented image, and their matching scores were fused to make the final decision. Several extensive experiments were conducted, using three large-scale and challenging person re-identification datasets: Market-1501, CUHK03, and P-DESTRE. The ReID-DeePNet system achieved new state-of-the-art Rank-1 and mAP values on these three challenging ReID datasets.
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Khan, Mohammad Farhan, Farnaz Haider, Ahmed Al-Hmouz, and Mohammad Mursaleen. "Development of an Intelligent Decision Support System for Attaining Sustainable Growth within a Life Insurance Company." Mathematics 9, no. 12 (June 12, 2021): 1369. http://dx.doi.org/10.3390/math9121369.
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Consumer behaviour is one of the most important and complex areas of research. It acknowledges the buying behaviour of consumer clusters towards any product, such as life insurance policies. Among various factors, the three most well-known determinants on which human conjecture depends for preferring a product are demographic, economic and psychographic factors, which can help in developing an accurate market design and strategy for the sustainable growth of a company. In this paper, the study of customer satisfaction with regard to a life insurance company is presented, which focused on comparing artificial intelligence-based, data-driven approaches to classical market segmentation approaches. In this work, an artificial intelligence-based decision support system was developed which utilises the aforementioned factors for the accurate classification of potential buyers. The novelty of this paper lies in developing supervised machine learning models that have a tendency to accurately identify the cluster of potential buyers with the help of demographic, economic and psychographic factors. By considering a combination of the factors that are related to the demographic, economic and psychographic elements, the proposed support vector machine model and logistic regression model-based decision support systems were able to identify the cluster of potential buyers with collective accuracies of 98.82% and 89.20%, respectively. The substantial accuracy of a support vector machine model would be helpful for a life insurance company which needs a decision support system for targeting potential customers and sustaining its share within the market.
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Andereck, Kathleen L., and Linda L. Caldwell. "Variable Selection in Tourism Market Segmentation Models." Journal of Travel Research 33, no. 2 (October 1994): 40–46. http://dx.doi.org/10.1177/004728759403300207.
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G. Budeva, Desislava, and Michael R. Mullen. "International market segmentation." European Journal of Marketing 48, no. 7/8 (July 8, 2014): 1209–38. http://dx.doi.org/10.1108/ejm-07-2010-0394.
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Purpose – This paper aims to investigate the influence of economic and cultural factors, separately and combined, on international country segments and to reveal the stability of factors and country segments over time. Design/methodology/approach – Principal component analysis is used to develop three economic factors and two cultural factors borrowed from the World Value Survey. Cluster analysis is used to form country clusters based on the economic and cultural factors, separately, and then combined, to detect whether both economics and culture need to be included as bases for macro-country segmentation. Further, the authors look at these issues across time, the beginning of the decade (1990) and then at the end of the decade (1999). Findings – Results support the hypotheses that economics and culture are both necessary for country-level segmentation but reject the hypothesis of cultural convergence as a consequence of technological development and industrialization. The authors confirm that cultural values and beliefs, although persistent, may change gradually under the influence of environmental forces such as economic development. The results support the instability of country segment membership when analyzed over one decade. Economic changes in some countries lead to their movement across segments. Practical implications – Results suggest that managers concerned with international segmentation should include both economic and cultural variables and reevaluate country segment membership continuously rather than relying on results obtained in a single period. Originality/value – Many international segmentation studies have used macro-level, secondary data to identify country clusters based on similarities in political, economic, geographic or cultural variables for a single period. This study extends existing international segmentation models by examining economic and cultural variables (separately, and then combined), and segment membership over time.
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Bassi, Francesca. "Longitudinal models for dynamic segmentation in financial markets." International Journal of Bank Marketing 35, no. 3 (May 15, 2017): 431–46. http://dx.doi.org/10.1108/ijbm-05-2016-0068.
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Purpose Dynamic market segmentation is a very important topic in many businesses where it is interesting to gain knowledge on the reference market and on its evolution over time. Various papers in the reference literature are devoted to the topic and different statistical models are proposed. The purpose of this paper is to compare two statistical approaches to model categorical longitudinal data to perform dynamic market segmentation. Design/methodology/approach The latent class Markov model identifies a latent variable whose states represent market segments at an initial point in time, customers can switch to one segment to another between consecutive measurement occasions and a regression structure models the effects of covariates, describing customers’ characteristics, on segments belonging and transition probabilities. The latent class growth approach models individual trajectories, describing a behaviour over time. Customers’ characteristics may be inserted in the model to affect trajectories that may vary across latent groups, in the author’s case, market segments. Findings The two approaches revealed both suitable for dynamic market segmentation. The advice to marketer analysts is to explore both solutions to dynamically segment the reference market. The best approach will be then judged in terms of fit, substantial results and assumptions on the reference market. Originality/value The proposed statistical models are new in the field of financial markets.
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Grover, Rajiv, and V. Srinivasan. "A Simultaneous Approach to Market Segmentation and Market Structuring." Journal of Marketing Research 24, no. 2 (May 1987): 139–53. http://dx.doi.org/10.1177/002224378702400201.
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The authors define a market segment to be a group of consumers homogeneous in terms of the probabilities of choosing the different brands in a product class. Because the vector of choice probabilities is homogeneous within segments and heterogeneous across segments, each segment is characterized by its corresponding group of brands with “large” choice probabilities. The competitive market structure is determined as the possibly overlapping groups of brands corresponding to the different segments. The use of brand choice probabilities as the basis for segmentation leads to market structuring and market segmentation becoming reverse sides of the same analysis. Using panel data, the authors obtain the matrix of cross-classification of brands chosen on two purchase occasions and extract segments by using the maximum likelihood method for estimating latent class models. An application to the instant coffee market indicates that the proposed approach has substantial validity and suggests the presence of submarkets related to product attributes as well as to brand names.
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Montinaro, Mario, and Ivan Sciascia. "Market Segmentation Models to Obtain Different Kinds of Customer Loyalty." Journal of Applied Sciences 11, no. 4 (February 1, 2011): 655–62. http://dx.doi.org/10.3923/jas.2011.655.662.
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Поляков and K. Polyakov. "Hedonic Function Estimation for Cars in Terms of Market Segmentation." Economics of the Firm 2, no. 3 (February 4, 2014): 74–81. http://dx.doi.org/10.12737/2500.
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It is shown how to specify and estimate hedonic function for well segmented markets, as exemplified by the new foreign-made cars market of this country. It has been proved that taking in consideration current market segmentation along with very few functional attributes of goods can dramatically improve accuracy of foreign-made cars’ market price approximation. The analysis of interclass difference in implicit prices on a good’s functional attributes has also been performed. Its results confirm, that system of implicit prices correctly reflects market segments positioning. As a mathematical apparatus technique for hedonic function analysis semi log switching regression is used.
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Morley, Clive L. "Tourism Demand: Characteristics, Segmentation and Aggregation." Tourism Economics 1, no. 4 (December 1995): 315–28. http://dx.doi.org/10.1177/135481669500100401.
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This paper aims to advance the understanding of the micro-economic foundations of tourism demand theory, particularly through bringing out some of the implications of the tour characteristics approach to utility analysis, and also through explicitly linking the micro-economic theory of the individual tourist to the aggregate level demand models as actually estimated. Important implications of the tour characteristics theory are that it yields theoretical rationales for the importance of market segmentation of tourism demand, and for tourism product differentiation. Consideration of the aggregation issues in the particular context of tourism demand shows aggregation to be justified and feasible when the market is segmented. Market segmentation is shown to be theoretically important for good tourism demand models, supporting its generally observed practical usefulness. Alternatively, aggregation can be justified through a random utility argument that assumes independence of individuals' tour decisions.
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TAKEDA, Yasuhiko. "An Introduction to Mathematical models for Drosophila Axis formation and Segmentation." Seibutsu Butsuri 32, no. 2 (1992): 89–94. http://dx.doi.org/10.2142/biophys.32.89.
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Chen, Kuan-Wei, Kang-Ling Liao, and Chih-Wen Shih. "The kinetics in mathematical models on segmentation clock genes in zebrafish." Journal of Mathematical Biology 76, no. 1-2 (May 25, 2017): 97–150. http://dx.doi.org/10.1007/s00285-017-1138-1.
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Kubiczek, Jakub, and Bartłomiej Hadasik. "Segmentation of Passenger Electric Cars Market in Poland." World Electric Vehicle Journal 12, no. 1 (February 10, 2021): 23. http://dx.doi.org/10.3390/wevj12010023.
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Striving to achieve sustainable development goals and taking care of the environment into the policies of car manufacturers forced the search for alternative sources of vehicle propulsion. One way to implement a sustainable policy is to use electric motors in cars. The observable development of the electric car market provides consumers with a wide spectrum of choices for a specific model that would meet their expectations. Currently, there are 53 different electric car models on the primary market in Poland. The aim of the article was to present the performed market segmentation, focused on identifying the similarities in the characteristics of electric car models on the Polish market and proposing their groupings. Based on the classification by the hierarchical cluster analysis algorithm (Ward’s method, squared Euclidean distance), the market division into 2, 3, and 4 groups was proposed. The Polish EV market segmentation took place not only in terms of the size and class of the car but primarily in terms of performance and overall quality of the vehicle. The performed classification did not change when the price was additionally included as a variable. It was also proposed to divide the market into 4 segments named: Premium, City, Small, and Sport. The segmentation carried out in this way helps to better understand the structure of the electric car market.
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Bassi, Francesca. "Latent class factor models for market segmentation: an application to pharmaceuticals." Statistical Methods and Applications 16, no. 2 (January 30, 2007): 279–87. http://dx.doi.org/10.1007/s10260-006-0036-2.
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Viselgaitė, Daiva, and Mantas Vilys. "PECULIARITIES IN CONSTRUCTION OF SEGMENTATION MODELS: THEORY AND PRACTICE." Business, Management and Education 9, no. 2 (November 28, 2011): 171–84. http://dx.doi.org/10.3846/bme.2011.12.
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Strategic marketing development is a major area of Lithuanian manufacturing companies, which seeks to improve business. In order to develop a strategic marketing plan, companies encompassing several business models are faced with the need to adapt those models here and highlight the lack of skills to carry out marketing activities for the sharp divide in the business models in the marketing literature. In order to give the latter companies a theoretical foundation for development of strategic marketing for sustainable business, which highlights the need for business model adaptation in the process of segmentation, it is worthwhile to analyze the scientific segmentation models and prepare recommendations for model construction. The scientific article is based on marketing research in window manufacturing and mounting business that enables to create a step-by-step market segmentation model, which is based on adaptation of different business models. The main tendencies identified in the sector (high market and technological uncertainty, intense competition) makes it a very characteristic example of a business, which seeks to improve competitiveness.
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Zhang, Chenyang, Rongchun Zhang, Sheng Jin, and Xuefeng Yi. "PFD-SLAM: A New RGB-D SLAM for Dynamic Indoor Environments Based on Non-Prior Semantic Segmentation." Remote Sensing 14, no. 10 (May 19, 2022): 2445. http://dx.doi.org/10.3390/rs14102445.
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Now, most existing dynamic RGB-D SLAM methods are based on deep learning or mathematical models. Abundant training sample data is necessary for deep learning, and the selection diversity of semantic samples and camera motion modes are closely related to the robust detection of moving targets. Furthermore, the mathematical models are implemented at the feature-level of segmentation, which is likely to cause sub or over-segmentation of dynamic features. To address this problem, different from most feature-level dynamic segmentation based on mathematical models, a non-prior semantic dynamic segmentation based on a particle filter is proposed in this paper, which aims to attain the motion object segmentation. Firstly, GMS and optical flow are used to calculate an inter-frame difference image, which is considered an observation measurement of posterior estimation. Then, a motion equation of a particle filter is established using Gaussian distribution. Finally, our proposed segmentation method is integrated into the front end of visual SLAM and establishes a new dynamic SLAM, PFD-SLAM. Extensive experiments on the public TUM datasets and real dynamic scenes are conducted to verify location accuracy and practical performances of PFD-SLAM. Furthermore, we also compare experimental results with several state-of-the-art dynamic SLAM methods in terms of two evaluation indexes, RPE and ATE. Still, we provide visual comparisons between the camera estimation trajectories and ground truth. The comprehensive verification and testing experiments demonstrate that our PFD-SLAM can achieve better dynamic segmentation results and robust performances.
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CARMONA, RENÉ, and SERGEY NADTOCHIY. "TANGENT MODELS AS A MATHEMATICAL FRAMEWORK FOR DYNAMIC CALIBRATION." International Journal of Theoretical and Applied Finance 14, no. 01 (February 2011): 107–35. http://dx.doi.org/10.1142/s0219024911006280.
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Motivated by the desire to integrate repeated calibration procedures into a single dynamic market model, we introduce the notion of a "tangent model" in an abstract set up, and we show that this new mathematical paradigm accommodates all the recent attempts to study consistency and absence of arbitrage in market models. For the sake of illustration, we concentrate on the case when market quotes provide the prices of European call options for a specific set of strikes and maturities. While reviewing our recent results on dynamic local volatility and tangent Lévy models, we present a theory of tangent models unifying these two approaches and construct a new class of tangent Lévy models, which allows the underlying to have both continuous and pure jump components.
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Tase, Manjola. "Demand Segmentation in the Federal Funds Market." Finance and Economics Discussion Series, no. 2022-071 (November 2022): 1–31. http://dx.doi.org/10.17016/feds.2022.071.
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This paper outlines a model of demand segmentation in the federal funds market with two types of borrowers - the "interest on reserves (IOR) arbitrage'' type and the "regulatory'' type - which have different reservation prices and cannot always be separated. When fed funds trade above IOR, the "regulatory" type is revealed and consequently pays an interest rate closer to its real reservation price, pushing the fed funds rate further up. When fed funds trade below IOR, a decrease in the fed funds rate encourages entry in the market for IOR arbitrage purposes thus counteracting the downward pressure on the fed funds rate. We use probit regression models and daily data for the period April 2018 to February 2020 to provide empirical support for this model. We find the following: 1) When fed funds trade above IOR, there is, on average, a 10 percentage points increase in the probability that the fed funds rate increases the following period. Furthermore, analysis using confidential bank-level data shows that this increase in the probability is higher for banks that report their liquidity profile daily and that were present all trading days during this period. 2) When the fed funds trade below IOR, the probability of a decrease in the fed funds rate decreases with the widening of the spread between the fed funds rate and IOR.
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Stikhova, Olga. "Mathematical Estimation Methods and Models for Industrial Companies." EPJ Web of Conferences 248 (2021): 03001. http://dx.doi.org/10.1051/epjconf/202124803001.
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The collateralized debt obligations and credit default swaps applications are shown in this paper. The industry obligations secondary market risk estimation methods are considered in this work. The new methods taking into account statistically significant parameters for industrial credit derivatives portfolio are offered for single-name investment risks numerical experiments realization. The mathematical estimation of tranche were shown. The single and multiple name default obligations necessary mathematical modeling methods and formulae for the industrial materials manufacturers derivative credit tools market are shown. It is determined that the portfolio of synthetic debt tools is made of the given parameters. The task of a loss derivative tranches mathematical estimation is solved. Late defaults raise the equity tranches payment required sums with high spreads, early defaults reduce. Also the functional characteristics required for an estimation huge debts problem solving are partly considered in this paper. The problem of the default modeling for market tools and numerical simulation of the obligations influence on conditions of current bistability mode are shown here. Some credit derivatives of industrial manufacturers are demonstrated in the modeling process of default as an example. It is found that the model is an additional factor help us to estimate the default opportunity.
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Chevhanova, V., and V. Vasiuta. "The role of market segmentation in consumer behavior investigation." Galic'kij ekonomičnij visnik 71, no. 4 (2021): 116–22. http://dx.doi.org/10.33108/galicianvisnyk_tntu2021.04.116.
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The essence of market segmentation is investigated and its role in consumer behavior investigation is defined in this paper. Market segmentation is an important element of the company's marketing strategy, where various methods and approaches are used in order to define customer segments. It can have various models that establish the relationship of consumer behavior with the main criteria. According to these models, consumers are divided into target groups (segments). As the result, we obtain description of the process of analyzing consumers in relation to the basis of market segmentation. The main criteria for the segmentation of the market are indicated. The analysis of consumers of the network of grocery stores «Ridne selo» is carried out. The segmentation of individual consumers of the network and its competitors is performed. Based on the segmentation, socio-demographic portrait of consumers is obtained. The analysis of consumers of the network of grocery stores «Ridne selo» make it possible to identify the main target groups that can be targeted when developing a marketing strategy in the future. It is proved that market segmentation allows us to solve the main problems in consumer behavior investigation, since each product can be successfully sold only in certain market segments. At the same time, the competitive advantage lies in more complete satisfaction of the needs of a certain segment of consumers with minimal resource costs for servicing this segment. It is noted that the principle of doing business of the world's leading companies is that they produce only those goods that are needed by the consumer. The likelihood of selling these products is confirmed by market research based on market segmentation. The need for continuous analysis of consumers, identification of target segments for making effective decisions on sales and production and for domestic enterprises is noted. This will express the essence of the formation of effective enterprise marketing strategy. Market segmentation in the development of marketing strategy will contribute to the constant search for strategic target groups of consumers and the concentration of the seller's (manufacturer's) activities precisely on meeting their needs. This will provide them with competitive advantage and improved performance in the long term.
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Chevhanova, V., and V. Vasiuta. "The role of market segmentation in consumer behavior investigation." Galic'kij ekonomičnij visnik 71, no. 4 (2021): 116–22. http://dx.doi.org/10.33108/galicianvisnyk_tntu2021.04.116.
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The essence of market segmentation is investigated and its role in consumer behavior investigation is defined in this paper. Market segmentation is an important element of the company's marketing strategy, where various methods and approaches are used in order to define customer segments. It can have various models that establish the relationship of consumer behavior with the main criteria. According to these models, consumers are divided into target groups (segments). As the result, we obtain description of the process of analyzing consumers in relation to the basis of market segmentation. The main criteria for the segmentation of the market are indicated. The analysis of consumers of the network of grocery stores «Ridne selo» is carried out. The segmentation of individual consumers of the network and its competitors is performed. Based on the segmentation, socio-demographic portrait of consumers is obtained. The analysis of consumers of the network of grocery stores «Ridne selo» make it possible to identify the main target groups that can be targeted when developing a marketing strategy in the future. It is proved that market segmentation allows us to solve the main problems in consumer behavior investigation, since each product can be successfully sold only in certain market segments. At the same time, the competitive advantage lies in more complete satisfaction of the needs of a certain segment of consumers with minimal resource costs for servicing this segment. It is noted that the principle of doing business of the world's leading companies is that they produce only those goods that are needed by the consumer. The likelihood of selling these products is confirmed by market research based on market segmentation. The need for continuous analysis of consumers, identification of target segments for making effective decisions on sales and production and for domestic enterprises is noted. This will express the essence of the formation of effective enterprise marketing strategy. Market segmentation in the development of marketing strategy will contribute to the constant search for strategic target groups of consumers and the concentration of the seller's (manufacturer's) activities precisely on meeting their needs. This will provide them with competitive advantage and improved performance in the long term.
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Novak, Thomas P. "Log-Linear Trees: Models of Market Structure in Brand Switching Data." Journal of Marketing Research 30, no. 3 (August 1993): 267–87. http://dx.doi.org/10.1177/002224379303000301.
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Log-linear trees restrict the log-linear model of quasi-symmetry so that parameters are interpretable as arc lengths in an additive tree. The tree representation can be interpreted further in terms of consumer heterogeneity, affording a dual interpretation in terms of both market structure and opportunities for market segmentation.
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Sarstedt, Marko. "Market segmentation with mixture regression models: Understanding measures that guide model selection." Journal of Targeting, Measurement and Analysis for Marketing 16, no. 3 (June 2008): 228–46. http://dx.doi.org/10.1057/jt.2008.9.
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Tripathi, Shreya, Aditya Bhardwaj, and Poovammal E. "Approaches to Clustering in Customer Segmentation." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 802. http://dx.doi.org/10.14419/ijet.v7i3.12.16505.
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Customer Relationship Management(CRM) has always played a crucial role as a market strategy for providing organizations with the quintessential business intelligence for building, managing and developing valuable long-term customer relationships. A number of business enterprises have come to realize the significance of CRM and the application of technical expertise to achieve competitive advantage. This study explores the importance of Customer Segmentation as a core function of CRM as well as the various models for segmenting customers using clustering techniques. The available clustering models for customer segmentation, in general, and the major models of K-Means and Hierarchical Clustering, in particular, are studied and the virtues and vices of the techniques are pointed out. Finally, the possibility of developing a hybrid solution by the combination of the above two techniques, having the ability to outperform the individual models, is discussed.
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Ou, Tsung-Yin, and Yenming J. Chen. "Optimal Segmentation over a Generalized Customer Distribution." Axioms 10, no. 2 (May 21, 2021): 98. http://dx.doi.org/10.3390/axioms10020098.
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This paper investigates the impact of consumer preferences on the intensity of competition for companies in a duopoly market. A classical Hotelling’s competition problem will be different if consumers are allowed to distribute non-uniformly. New results in competition intensity are established and conditions for the existence of a subgame perfect Nash equilibrium is identified through a model that considers generic distribution in consumer preferences. A competition strategy is demonstrated to depend on the signs of local change rates of the density function at the endpoints of market segments.
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Prokopjeva, Evgenija, Evgeny Tankov, Tatyana Shibaeva, and Elena Perekhozheva. "Behavioral models in insurance risk management." Investment Management and Financial Innovations 18, no. 4 (October 21, 2021): 80–94. http://dx.doi.org/10.21511/imfi.18(4).2021.08.
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Behavioral characteristics attributed to consumers of insurance services are a relevant factor for analyzing the current situation in the insurance market and developing effective strategies for insurers’ actions. In turn, considering these characteristics allows the insurer to be more successful in the highly competitive field, achieving mutual satisfaction in interacting with the customer. This study is aimed to develop cognitive models of the situation (frame) “Insurance”, taking into account the specifics of the Russian insurance market and systemic factors affecting participants’ behavior in the market. In this regard, the study involves systemizing risks at various levels of the economic system, generalizing factors for the motivation of insurance consumers, developing descriptive and economic-mathematical models for the behavior of economic entities in risky situations.The results obtained represent a behavioral model of interactions among insurance market entities, which determines opportunities for efficient and mutually beneficial coordination of their activities. The developed model includes the following elements: structured individual and institutional frames “Insurance”; a professional index of interest in insurance presented in the form of a mathematical model; methodology for governing the relationships among insurance participants in the digital environment.The recommendations enable predictions of the situation in the insurance market and allow most accurately defining the consumer needs in the conditions of market changes.
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Karolyi, G. Andrew, and Ying Wu. "A New Partial-Segmentation Approach to Modeling International Stock Returns." Journal of Financial and Quantitative Analysis 53, no. 2 (March 19, 2018): 507–46. http://dx.doi.org/10.1017/s0022109017001016.
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We propose a new multi-factor model for international stock returns that includes size, value, and momentum factor portfolios and that builds them in a partial-segmentation capital market framework. Accounting for externalities driven by the incomplete accessibility to stocks and stock markets, our model not only captures strong common variation in international stock returns but also achieves low pricing errors and rejection rates relative to pure segmentation and pure integration models. This partial-segmentation approach is evaluated using monthly returns for over 37,000 stocks from 46 developed and emerging market countries over 2 decades and for a wide variety of test assets.
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Golovanova, Maiya, and Vira Lebedchenko. "USING THE ECONOMIC AND MATHEMATICAL MODELS FOR DETERMINING THE MARKET CAPACITY." Innovative technologies and scientific solutions for industries, no. 1 (3) (March 23, 2018): 71–81. http://dx.doi.org/10.30837/2522-9818.2018.3.071.
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Angulo, Jesús. "POLAR MODELLING AND SEGMENTATION OF GENOMIC MICROARRAY SPOTS USING MATHEMATICAL MORPHOLOGY." Image Analysis & Stereology 27, no. 2 (May 3, 2011): 107. http://dx.doi.org/10.5566/ias.v27.p107-124.
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Robust image analysis of spots in microarrays (quality control + spot segmentation + quantification) is a requirement for automated software which is of fundamental importance for a high-throughput analysis of genomics microarray-based data. This paper deals with the development of model-based image processing algorithms for qualifying/segmenting/quantifying adaptively each spot according to its morphology. A series of morphologicalmodels for spot intensities are introduced. The spot typologies representmost of the possible qualitative cases identified from a large database (different routines, techniques, etc.). Then, based on these spot models, a classification framework has been developed. The spot feature extraction and classification (without segmenting) is based on converting the spot image to polar coordinates and, after computing the radial/angular projections, the calculation of granulometric curves and derived parameters from these projections. Spot contour segmentation can also be solved by working in polar coordinates, calculating the up/downminimal path, which is easily obtained with the generalized distance function. With this model-based technique, the segmentation can be regularised by controlling different elements of the algorithm. According to the spot typology (e.g., doughnut-like or egg-like spots), several minimal paths can be computed to obtain a multi-region segmentation. Moreover, this segmentation is more robust and sensible to weak spots, improving the previous approaches.
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Frías, E., J. Balado, L. Díaz-Vilariño, and H. Lorenzo. "POINT CLOUD ROOM SEGMENTATION BASED ON INDOOR SPACES AND 3D MATHEMATICAL MORPHOLOGY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W1-2020 (September 3, 2020): 49–55. http://dx.doi.org/10.5194/isprs-archives-xliv-4-w1-2020-49-2020.
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Abstract. Room segmentation is a matter of ongoing interesting for indoor navigation and reconstruction in robotics and AEC. While in robotics field, the problem room segmentation has been typically addressed on 2D floorplan, interest in enrichment 3D models providing more detailed representation of indoors has been growing in the AEC. Point clouds make available more realistic and update but room segmentation from point clouds is still a challenging topic. This work presents a method to carried out point cloud segmentation into rooms based on 3D mathematical morphological operations. First, the input point cloud is voxelized and indoor empty voxels are extracted by CropHull algorithm. Then, a morphological erosion is performed on the 3D image of indoor empty voxels in order to break connectivity between voxels belonging to adjacent rooms. Remaining voxels after erosion are clustered by a 3D connected components algorithm so that each room is individualized. Room morphology is retrieved by individual 3D morphological dilation on clustered voxels. Finally, unlabelled occupied voxels are classified according proximity to labelled empty voxels after dilation operation. The method was tested in two real cases and segmentation performance was evaluated with encouraging results.
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Tsarev, Andrey V. "PRINCIPLES AND MODELS OF CONSUMER SEGMENTATION IN THE BANKING PRODUCTS AND SERVICES MARKET." Statistics and Economics, no. 1 (January 1, 2015): 128–32. http://dx.doi.org/10.21686/2500-3925-2015-1-128-132.
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Chiu, Jonathan. "ENDOGENOUSLY SEGMENTED ASSET MARKET IN AN INVENTORY-THEORETIC MODEL OF MONEY DEMAND." Macroeconomic Dynamics 18, no. 2 (September 13, 2012): 438–72. http://dx.doi.org/10.1017/s1365100512000466.
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This paper studies the effects of monetary policy in an inventory-theoretic model of money demand. In this model, agents keep inventories of money, despite the fact that money is dominated in rate of return by interest-bearing assets, because they must pay a fixed cost to transfer funds between the asset market and the goods market. In contrast to exogenous segmentation models in the literature, the timing of money transfers is endogenous. As a result, the model endogenizes the degree of market segmentation as well as the magnitudes of liquidity effects, price sluggishness, and the variability of velocity. I first show that the endogenous segmentation model can generate the positive long-run relationship between money growth and velocity observed in the data, which the exogenous segmentation model fails to capture. I also show that the short-run effects of money shocks on prices, inflation, and nominal interest rates are not robust.
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Kang, Yena Christina, Hee Kyung Yang, Young Jae Kim, Jeong-Min Hwang, and Kwang Gi Kim. "Automated Mathematical Algorithm for Quantitative Measurement of Strabismus Based on Photographs of Nine Cardinal Gaze Positions." BioMed Research International 2022 (March 24, 2022): 1–7. http://dx.doi.org/10.1155/2022/9840494.
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This study presents an automated algorithm that measures ocular deviation quantitatively using photographs of the nine cardinal points of gaze by means of deep learning (DL) and image processing techniques. Photographs were collected from patients with strabismus. The images were used as inputs for the DL segmentation models that segmented the sclerae and limbi. Subsequently, the images were registered for the mathematical algorithm. Two-dimensional sclera and limbus were modeled, and the corneal light reflex points of the primary gaze images were determined. Limbus recognition was performed to measure the pixel-wise distance between the corneal reflex point and limbus center. The segmentation models exhibited high performance, with 96.88% dice similarity coefficient (DSC) for the sclera segmentation and 95.71% DSC for the limbus segmentation. The mathematical algorithm was tested on two cranial nerve palsy patients to evaluate its ability to measure and compare ocular deviation in different directions. These results were consistent with the symptoms of such disorders. This algorithm successfully measured the distance of ocular deviation in patients with strabismus. With complementation in the dimension calculations, we expect that this algorithm can be used further in clinical settings to diagnose and measure strabismus at a low cost.
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Wiener, Noé M. "Labor Market Segmentation and Immigrant Competition: A Quantal Response Statistical Equilibrium Analysis." Entropy 22, no. 7 (July 5, 2020): 742. http://dx.doi.org/10.3390/e22070742.
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Competition between and within groups of workers takes place in labor markets that are segmented along various, often unobservable dimensions. This paper proposes a measure of the intensity of competition in labor markets on the basis of limited data. The maximum entropy principle is used to make inferences about the unobserved mobility decisions of workers in US household data. The quantal response statistical equilibrium class of models can be seen to give robust microfoundations to the persistent patterns of wage inequality. An application to labor market competition between native and foreign-born workers in the United States shows that this class of models captures a substantial proportion of the informational content of observed wage distributions.
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Bassi, Francesca. "Forecasting Financial Products Acquisition via Dynamic Segmentation: An Application to the Italian Market." International Journal of Market Research 57, no. 6 (November 2015): 909–30. http://dx.doi.org/10.2501/ijmr-2015-071.
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The topic of market segmentation is still one of the most pervasive in marketing. Among clustering techniques, finite mixture models have gained recognition as a method of segmentation with several advantages over traditional methods; one variant of finite mixture models – the latent class (LC) model – is probably the most popular. The LC approach is innovative and flexible, and can provide suitable solutions to several problems regarding the definition and development of marketing strategies, because it takes into account specific features of the collected data, such as their scale of measure (often ordinal or categorical, rather than continuous), their hierarchical structure and their longitudinal component. Dynamic segmentation is of key importance in many markets where it is unrealistic to assume stationary segments due to the dynamics in consumers' needs and product choices. In this paper, a mixture latent class Markov model is proposed to dynamically segment Italian households with reference to financial products ownership. The mixture approach is compared with the standard one in terms of its ability to forecast customers' behaviour in the reference market.
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Xue, Zhaojie, Shuqing Cheng, Mingzhu Yu, and Liang Zou. "Pricing models of two-sided markets incorporating service quality." Kybernetes 48, no. 8 (September 2, 2019): 1827–50. http://dx.doi.org/10.1108/k-06-2018-0287.
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Purpose This paper aims to study the pricing problems on the two-sided market for cases of monopoly and duopoly competition, specifically investigating the impact of platform service quality on the market. Theoretical analysis and computational studies are conducted to investigate the impact of different parameters on the system outcomes. Design/methodology/approach Mathematical formulations are proposed for cases of monopoly and duopoly competition. For monopolistic market, the optimal pricing and service quality strategies are obtained using mathematical programming method. For duopolistic market, the equilibrium outcomes are derived by game theory. Sensitivity analysis and numerical studies are also adopted to investigate the impact of different parameters. Findings For monopolistic market, the platform will provide a low service quality when the service cost parameter is large. However, when the cost parameter is small, the platform provides a higher service quality and higher registration prices. Furthermore, the sum of the optimal prices is proportional to the service quality and inversely proportional to the user price sensitivity. For duopolistic market, the competitive equilibrium prices exist under a certain condition. The determinants of equilibrium prices are the gap between the service qualities of two platforms and the cross-group externalities. Originality/value For monopolistic market, this paper specifies the role of platform service quality in determining the platform’s pricing strategy. For duopolistic market, this paper presents a market sharing mechanism between two platforms and explores the equilibrium pricing strategies for platforms with different service quality level.
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Sisodia, Neha, and Ravi Gor. "A STUDY OF OPTION PRICING MODELS WITH DISTINCT INTEREST RATES." International Journal of Engineering Science Technologies 6, no. 2 (May 5, 2022): 90–104. http://dx.doi.org/10.29121/ijoest.v6.i2.2022.310.
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This paper analyses the effect of different interest rates on Black-Scholes’ and Heston Option Pricing Model. We discuss the concept of interest rate in the two Models. We compare the two models for the parameter –‘Interest Rate’. A mathematical tool, UMBRAE (Unscaled Mean Bounded Relative Absolute Error) is used to compare the two models for pricing European call options. NSE (National Stock Exchange) is used for real market data and comparison is done through Moneyness (which is defined as the percentage difference of stock price and strike price) and Time-To-Maturity. Mathematical software – Matlab is used for all mathematical calculations. We observe that Black-Scholes’ model is preferred for lower interest rates than Heston options pricing model and vice-versa. This study is helpful in derivatives market.
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Heras-Tang, Armando, Damian Valdes-Santiago, Ángela Mireya León-Mecías, Marta Lourdes Baguer Díaz-Romañach, José Alejandro Mesejo-Chiong, and Carlos Cabal-Mirabal. "Diabetic foot ulcer segmentation using logistic regression, DBSCAN clustering and mathematical morphology operators." ELCVIA Electronic Letters on Computer Vision and Image Analysis 21, no. 2 (September 13, 2022): 22–39. http://dx.doi.org/10.5565/rev/elcvia.1413.
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Digital images are used for evaluation and diagnosis of a diabetic foot ulcer. Selecting the wound region (segmentation) in an image is a preliminary step for subsequent analysis. Most of the time, manual segmentation isn't very reliable because specialists could have different opinions over the ulcer border. This fact encourages researchers to find and test different automatic segmentation techniques. This paper presents a computer-aided ulcer region segmentation algorithm for diabetic foot images. The proposed algorithm has two stages: ulcer region segmentation, and post-processing of segmentation results. For the first stage, a trained machine learning model was selected to classify pixels inside the ulcer's region, after a comparison of five learning models. Exhaustive experiments have been performed with our own annotated dataset from images of Cuban patients. The second stage is needed because of the presence of some misclassified pixels. To solve this, we applied the DBSCAN clustering algorithm, together with dilation, and closing morphological operators. The best-trained model after the post-processing stage was the logistic regressor (Jaccard Index $0.81$, accuracy $0.94$, recall $0.86$, precision $0.91$, and F1 score $0.88$). The trained model was sensitive to irrelevant objects in the scene, but the patient foot. Physicians found these results promising to measure the lesion area and to follow-up the ulcer healing process over treatments, reducing errors.
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Kryńska, Elżbieta, and Danuta Kopycińska. "Wages in Labour Market Theories." Folia Oeconomica Stetinensia 15, no. 2 (December 1, 2015): 177–90. http://dx.doi.org/10.1515/foli-2015-0044.
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Abstract Already classical economists took interest in the role of wages and wage formation mechanisms, as well as in their influence on other components of the labour market. This article aims to systematise contemporary approaches to wages as one of the labour market components that have been developed within major economic theories. The systemization will serve as a basis for identifying main interactions between wages and other labour market components, such as labour supply and demand and labour market disequilibrium. The article presents major concepts formulated within neo-classical and Keynesian theories, labour market segmentation theories, efficiency wage theory, rent-sharing and rent-extraction theories, theory of job search, and search-and-matching models. One of the conclusions arising from the discussion is that the evolution of contemporary labour markets is a challenge for researchers seeking wage formation models adequately describing the real-life circumstances.
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Choi, Jae Young, Jungwoo Shin, and Jongsu Lee. "Strategic Management of New Products: Ex-Ante Simulation and Market Segmentation." International Journal of Market Research 55, no. 2 (March 2013): 289–314. http://dx.doi.org/10.2501/ijmr-2013-024.
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Among various methodologies for demand forecasting of new products, the random-coefficient discrete-choice model using stated preference data is considered to be effective because it reflects heterogeneity in consumer preference and enables the design of experiments in the absence of revealedpreference data. Based on estimates drawn from consumer preference data by structural hierarchical Bayesian logit models, this study develops the overall, strategic, demand-side management for new products by combining market share simulation and a rigorous clustering methodology, the Gaussian mixture model. It then applies the process to the empirical case of electronic payment instruments.
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Li, Wei Ping, De Qing Quan, and Jun Cai. "Application of Data Mining in Sports in the Consumer Market Segmentation." Applied Mechanics and Materials 631-632 (September 2014): 280–83. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.280.
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This paper combines the data mining technology and the rich sports consumption data resources of city household survey. By using the K-Means fast cluster method, sports consumer market models were constructed based on the different variables. Research shows, choosing sports consumption content as variables to establish clustering model is better than choosing the demographic , sports consumption content ,consumer psychology and way of life as variables to establish clustering model. According to the results of clustering, the city residents are divided into four kinds of consumer groups in accordance with the different features of sports consumption.
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Michaels, R. Gregory, and V. Kerry Smith. "Market segmentation and valuing amenities with hedonic models: The case of hazardous waste sites." Journal of Urban Economics 28, no. 2 (September 1990): 223–42. http://dx.doi.org/10.1016/0094-1190(90)90052-o.
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Popov, Evgeny, Anna Veretennikova, and Sergey Fedoreev. "The Model of OTC Securities Market Transformation in the Context of Asset Tokenization." Mathematics 10, no. 19 (September 22, 2022): 3441. http://dx.doi.org/10.3390/math10193441.
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The relevance of this study stems from the fact that the development of a market for financial instruments can significantly expand lending opportunities for small- and medium-sized businesses. While research on the impact of tokenization on financial markets is extensive, literature provides virtually no description of mathematical models that can be used in the design and development of information systems issuing tokenized financial instruments. Thus, the study aims to develop mathematical models representing the transformation of the over-the-counter (OTC) securities market induced by the tokenization of underlying assets. The development of crowdlending platforms is gradually transforming the financial market landscape. The key change trends consist in transactional fragmentation both on the demand and supply sides. This paper proposes a mathematical model of internal transformation occurring in the OTC financial market, which describes the process of managing rights to underlying assets during their issuance and circulation. The model is built by analogy with the Harrison–Ruzzo–Ullman (HRU) model, applying the same principles to the relations of economic agents in exercising access rights to underlying assets as those that regulate access rights to files. The research novelty of the presented model consists in the formalization of financial market transformation occurring in the context of asset tokenization, which significantly expands the mathematical apparatus of digital financial transactions. This paper also proposes a mathematical model of competitive tokenization-induced transformation occurring in the OTC financial market, which describes transaction costs associated with attracting investment in the OTC financial market and the market for tokenized assets. In addition, the barriers of the OTC financial market and the stock market are described indicating the supply and demand trends in the context of transformation occurring in the OTC financial market under the influence of underlying asset tokenization. The novelty of this model lies in the mathematical formalization of the investment attraction process in the market for tokenized assets. The theoretical value of the developed models consists in the confirmation of significantly expanded supply capabilities of tokenized assets on the graph showing the dependence of asset returns on invested capital.
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Usman, Hamza, Mohd Lizam, and Burhaida Burhan. "A Priori Spatial Segmentation of Commercial Property Market using Hedonic Price Modelling." Real Estate Management and Valuation 29, no. 2 (June 1, 2021): 16–28. http://dx.doi.org/10.2478/remav-2021-0010.
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Abstract The improvement of property price modelling accuracy using property market segmentation approaches is well documented in the housing market. However, that cannot be said of the commercial property market which is adjudged to be volatile, heterogeneous and thinly traded. This study, therefore, determines if the commercial property market in Malaysia is spatially segmented into submarkets and whether accounting for the submarkets improves the accuracy of price modelling. Using a 11,460 shop-offices transaction dataset, the commercial property submarkets are delineated by using submarket binary dummies in the market-wide model and estimating a separate hedonic model for each submarket. The former method improves the model fit and reduces error by 5.6% and 6.5% respectively. The commercial property submarkets are better delineated by estimating a separate hedonic model for each submarket as it improves the model fit by about 7% and reduces models’ error by more than 10%. This study concludes that the Malaysian commercial property market is spatially segmented into submarkets. Modelling the submarkets improves the accuracy and correctness of price modelling.
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Outwater, Maren L., Steve Castleberry, Yoram Shiftan, Moshe Ben-Akiva, Yu Shuang Zhou, and Arun Kuppam. "Attitudinal Market Segmentation Approach to Mode Choice and Ridership Forecasting: Structural Equation Modeling." Transportation Research Record: Journal of the Transportation Research Board 1854, no. 1 (January 2003): 32–42. http://dx.doi.org/10.3141/1854-04.
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The San Francisco Bay Area Water Transit Authority is evaluating expanded ferry service, as required by the California Legislature. As part of this process, Cambridge Systematics developed forecasts using a combination of market research strategies and the addition of nontraditional variables into the mode choice modeling process. The focus of this work was on expanding the mode choice model to recognize travelers' attitudes and different market segments. Structural equation modeling was used to simultaneously identify the attitudes of travel behaviors and the causal relationships between traveler's socioeconomic profile and traveler attitudes. Six attitudinal factors were extracted, and three of these were used to partition the ferry-riding market into eight segments. These market segments were used to estimate stated preference mode choice models for 14 alternative modes, which separated the travelers' reactions to time savings by market segment and which recognized that mode choices are different for market segments that are sensitive to travel stress or the desire to help the environment. The new mode choice models were applied within the framework of the Metropolitan Transportation Commission's regional travel model and calibrated to match modal shares, modes of access to each ferry terminal, ridership by route and time period, and person trips by mode at screening line crossings. Additional validation tests of significant changes in ferry service in recent years were used to confirm the reasonableness of the stated preference model. The model has been applied for three future year alternatives and to test the sensitivities of pricing, service changes, and alternative transit modes.
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Deng, Jinyang, and Rogelio Andrada. "Visitors' Spatial Movement Patterns and Market Segmentation in Washington, DC." Tourism Analysis 25, no. 1 (March 3, 2020): 1–20. http://dx.doi.org/10.3727/108354220x15758301241576.
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Visitors' movement patterns can provide important information on popular sites visited and the timing of visits. Such information can be used for transportation planning, appropriate use and management of tourism resources/facilities, and market segmentation. Traditional market segmentation methods typically use one or more nonspatial variables, which cannot reflect the spatial consumption of a destination if the spatial movement patterns are not considered. While studies on visitors' spatial movements in an urban area have recently gained popularity, few, if any, have investigated visitors' spatial movements in relation to urban forests (i. e., parks, gardens, and green spaces in an urban area). In view of this, this study segments visitor markets in Washington, DC based on dominant movement patterns of 1,090 visitors. General log-linear models are used to identify dominant movement patterns and poLCA in R Studio is used for segmentation analysis. Ten significant movement patterns are identified, including seven two-ward patterns and three three-ward patterns, with the National Mall as the most visited area in the city. Findings of this study are useful for the maintenance of urban forests, the design of visitor itineraries, and the effective marketing and management of attractions and facilities in the city.
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Chen, Jun, Gloria DeGrandi-Hoffman, Vardayani Ratti, and Yun Kang. "Review on mathematical modeling of honeybee population dynamics." Mathematical Biosciences and Engineering 18, no. 6 (2021): 9606–50. http://dx.doi.org/10.3934/mbe.2021471.
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<abstract><p>Honeybees have an irreplaceable position in agricultural production and the stabilization of natural ecosystems. Unfortunately, honeybee populations have been declining globally. Parasites, diseases, poor nutrition, pesticides, and climate changes contribute greatly to the global crisis of honeybee colony losses. Mathematical models have been used to provide useful insights on potential factors and important processes for improving the survival rate of colonies. In this review, we present various mathematical tractable models from different aspects: 1) simple bee-only models with features such as age segmentation, food collection, and nutrient absorption; 2) models of bees with other species such as parasites and/or pathogens; and 3) models of bees affected by pesticide exposure. We aim to review those mathematical models to emphasize the power of mathematical modeling in helping us understand honeybee population dynamics and its related ecological communities. We also provide a review of computational models such as VARROAPOP and BEEHAVE that describe the bee population dynamics in environments that include factors such as temperature, rainfall, light, distance and quality of food, and their effects on colony growth and survival. In addition, we propose a future outlook on important directions regarding mathematical modeling of honeybees. We particularly encourage collaborations between mathematicians and biologists so that mathematical models could be more useful through validation with experimental data.</p></abstract>