This research explores the effectiveness of employer branding campaigns within the IT industry through an eye-tracking analysis of the visual engagement of potential employees. The study tries to figure out what resonates in terms of branding through messages or images and, later, influences decision-making towards the brand by IT professionals and job seekers. The experiment was conducted with a randomized block design approach in which the subjects were divided into two groups: those working in IT at that time and others seeking a career in IT for the first time. All the subjects were exposed to six employer branding campaigns launched by top IT companies, and their eye movements were recorded with the help of eye-tracking devices. Heatmaps and scan paths resulted from the eye tracking analysis confirmed that, the areas that interested users the most were those text sections that dealt with career development, work-life balance, and job stability. Corporate imagery received relatively low engagement, especially from those participants looking for clear benefits. It was found that the text-laden sections were engaged with for significantly more time by the participants, especially those that were to derive practical benefits concerning the themes of career growth and flexibility in jobs. To sum up, this study states that eye-tracking technology offers objective insights into the visual interaction of IT professionals with branding campaigns in a way that is highly valuable for the optimization of employer branding strategies. Such results can afford companies with more precise and specific campaigns to target the information that will attract potential employees based on their needs and interests. The study can be used as a new contribution to the field of employer branding by testing how effective neuromarketing tools are in refining recruitment strategies based on a pattern of visual engagement. Thus, it gives an evidence-based approach to improving efforts of talent acquisition in the IT sector.
Marketing. Distribution of products, Economics as a science
In the early 2000s, Thailand's hypermarket sector experienced rapid growth, drawing significant foreign direct investment. However, increasing competition from local retailers, the rise of convenience stores and community malls, and evolving consumer shopping habits have contributed to a post-pandemic slowdown in sales growth. Understanding how consumers evaluate hypermarkets' marketing practices is crucial for both academics and managers, as their strategies may depend on the strength of consumer attitudes toward these practices. Guided by the stimulus-organism-response (S-O-R) paradigm, this study investigates how consumers’ attitudes toward marketing practices, including business provision, deceptive advertising, fair price, and retail services—affect consumer satisfaction, which, in turn, influences overall attitudes and loyalty toward a hypermarket retailer. To test the hypotheses, data (n = 414) were collected through intercept surveys at various locations in a mid-sized city in central Thailand. The study found that consumers’ attitudes toward various aspects of marketing efforts affect consumer satisfaction, which subsequently influences overall attitudes toward hypermarket retailers and behavioral loyalty. Furthermore, a relationship between overall attitudes toward hypermarket retailers and behavioral loyalty was identified. This research contributes to theory by extending the S-O-R model within the context of hypermarkets and consumer behavior in developing countries, a field that has been less frequently studied. Specifically, it emphasizes the unique cultural and economic factors in Thailand that influence consumer behavior in the hypermarket sector. Practically, the findings offer valuable insights for managers in the hypermarket sector, providing evidence-based strategies to enhance consumer satisfaction and foster long-term loyalty. Directions for future research are also suggested.
We present NNN, an experimental Transformer-based neural network approach to marketing measurement. Unlike Marketing Mix Models (MMMs) which rely on scalar inputs and parametric decay functions, NNN uses rich embeddings to capture both quantitative and qualitative aspects of marketing and organic channels (e.g., search queries, ad creatives). This, combined with its attention mechanism, potentially enables NNN to model complex interactions, capture long-term effects, and improve sales attribution accuracy. We show that L1 regularization permits the use of such expressive models in typical data-constrained settings. Evaluating NNN on simulated and real-world data demonstrates its efficacy, particularly through considerable improvement in predictive power. In addition to marketing measurement, the NNN framework can provide valuable, complementary insights through model probing, such as evaluating keyword or creative effectiveness.
We analyze the performance of heterogeneous learning agents in asset markets with stochastic payoffs. Our main focus is on comparing Bayesian learners and no-regret learners who compete in markets and identifying the conditions under which each approach is more effective. Surprisingly, we find that low regret is not sufficient for survival: an agent can have regret as low as $O(\log T)$ but still vanish when competing against a Bayesian with a finite prior and any positive prior probability on the correct model. On the other hand, we show that Bayesian learning is fragile, while no-regret learning requires less knowledge of the environment and is therefore more robust. Motivated by the strengths and weaknesses of both approaches, we propose a balanced strategy for utilizing Bayesian updates that improves robustness and adaptability to distribution shifts, providing a step toward a best-of-both-worlds learning approach. The method is general, efficient, and easy to implement. Finally, we formally establish the relationship between the notions of survival and market dominance studied in economics and the framework of regret minimization, thus bridging these theories. More broadly, our work contributes to the understanding of dynamics with heterogeneous types of learning agents and their impact on markets.
In this paper, we present an empirical study of typical spatial augmentation techniques used in self-supervised representation learning methods (both contrastive and non-contrastive), namely random crop and cutout. Our contributions are: (a) we dissociate random cropping into two separate augmentations, overlap and patch, and provide a detailed analysis on the effect of area of overlap and patch size to the accuracy on down stream tasks. (b) We offer an insight into why cutout augmentation does not learn good representation, as reported in earlier literature. Finally, based on these analysis, (c) we propose a distance-based margin to the invariance loss for learning scene-centric representations for the downstream task on object-centric distribution, showing that as simple as a margin proportional to the pixel distance between the two spatial views in the scence-centric images can improve the learned representation. Our study furthers the understanding of the spatial augmentations, and the effect of the domain-gap between the training augmentations and the test distribution.
Marketing optimization plays an important role to enhance user engagement in online Internet platforms. Existing studies usually formulate this problem as a budget allocation problem and solve it by utilizing two fully decoupled stages, i.e., machine learning (ML) and operation research (OR). However, the learning objective in ML does not take account of the downstream optimization task in OR, which causes that the prediction accuracy in ML may be not positively related to the decision quality. Decision Focused Learning (DFL) integrates ML and OR into an end-to-end framework, which takes the objective of the downstream task as the decision loss function and guarantees the consistency of the optimization direction between ML and OR. However, deploying DFL in marketing is non-trivial due to multiple technological challenges. Firstly, the budget allocation problem in marketing is a 0-1 integer stochastic programming problem and the budget is uncertain and fluctuates a lot in real-world settings, which is beyond the general problem background in DFL. Secondly, the counterfactual in marketing causes that the decision loss cannot be directly computed and the optimal solution can never be obtained, both of which disable the common gradient-estimation approaches in DFL. Thirdly, the OR solver is called frequently to compute the decision loss during model training in DFL, which produces huge computational cost and cannot support large-scale training data. In this paper, we propose a decision focused causal learning framework (DFCL) for direct counterfactual marketing optimization, which overcomes the above technological challenges. Both offline experiments and online A/B testing demonstrate the effectiveness of DFCL over the state-of-the-art methods. Currently, DFCL has been deployed in several marketing scenarios in Meituan, one of the largest online food delivery platform in the world.
In machine learning applications, distribution shifts between training and target environments can lead to significant drops in model performance. This study investigates the impact of such shifts on binary classification models within the Real-Time Bidding (RTB) market context, where selection bias contributes to these shifts. To address this challenge, we apply the Exponential Tilt Reweighting Alignment (ExTRA) algorithm, proposed by Maity et al. (2023). This algorithm estimates importance weights for the empirical risk by considering both covariate and label distributions, without requiring target label information, by assuming a specific weight structure. The goal of this study is to estimate weights that correct for the distribution shifts in RTB model and to evaluate the efficiency of the proposed model using simulated real-world data.
As an important psychological state, the sense of power has a widespread impact on consumers’ cognition, emotion, and behavior. In recent years, there has been a gradual increase in research on the impact of consumer sense of power. However, despite this growing literature, there has been no systematic review of work in this field. Therefore, there is a need for organization of previous research findings and clarification of future research topics. This paper reviews previous research based on the following five streams: (1) sense of power and compensatory consumption, (2) sense of power and communication effects of advertising, (3) sense of power and traveling, (4) sense of power and sustainable development goals (SDGs), and (5) sense of power and social crowding. The paper proposes that future research should (a) examine compensatory word of mouth (WOM), (b) focus on the location and visual elements of advertisements, and (c) consider the diversity of crowded environment settings.
Ashwin De Silva, Rahul Ramesh, Carey E. Priebe
et al.
We expect the generalization error to improve with more samples from a similar task, and to deteriorate with more samples from an out-of-distribution (OOD) task. In this work, we show a counter-intuitive phenomenon: the generalization error of a task can be a non-monotonic function of the number of OOD samples. As the number of OOD samples increases, the generalization error on the target task improves before deteriorating beyond a threshold. In other words, there is value in training on small amounts of OOD data. We use Fisher's Linear Discriminant on synthetic datasets and deep networks on computer vision benchmarks such as MNIST, CIFAR-10, CINIC-10, PACS and DomainNet to demonstrate and analyze this phenomenon. In the idealistic setting where we know which samples are OOD, we show that these non-monotonic trends can be exploited using an appropriately weighted objective of the target and OOD empirical risk. While its practical utility is limited, this does suggest that if we can detect OOD samples, then there may be ways to benefit from them. When we do not know which samples are OOD, we show how a number of go-to strategies such as data-augmentation, hyper-parameter optimization, and pre-training are not enough to ensure that the target generalization error does not deteriorate with the number of OOD samples in the dataset.
[Purpose] To better understand the online reviews and help potential consumers, businessmen, and product manufacturers effectively obtain users' evaluation on product aspects, this paper explores the distribution regularities of user attention and sentiment toward product aspects from the temporal perspective of online reviews. [Design/methodology/approach] Temporal characteristics of online reviews (purchase time, review time, and time intervals between purchase time and review time), similar attributes clustering, and attribute-level sentiment computing technologies are employed based on more than 340k smartphone reviews of three products from JD.COM (a famous online shopping platform in China) to explore the distribution regularities of user attention and sentiment toward product aspects in this article. [Findings] The empirical results show that a power-law distribution can fit user attention to product aspects, and the reviews posted in short time intervals contain more product aspects. Besides, the results show that the values of user sentiment of product aspects are significantly higher/lower in short time intervals which contribute to judging the advantages and weaknesses of a product. [Research limitations] The paper can't acquire online reviews for more products with temporal characteristics to verify the findings because of the restriction on reviews crawling by the shopping platforms. [Originality/value] This work reveals the distribution regularities of user attention and sentiment toward product aspects, which is of great significance in assisting decision-making, optimizing review presentation, and improving the shopping experience.
Haoze Wu, Teruhiro Tagomori, Alexander Robey
et al.
We consider the problem of certifying the robustness of deep neural networks against real-world distribution shifts. To do so, we bridge the gap between hand-crafted specifications and realistic deployment settings by proposing a novel neural-symbolic verification framework, in which we train a generative model to learn perturbations from data and define specifications with respect to the output of the learned model. A unique challenge arising from this setting is that existing verifiers cannot tightly approximate sigmoid activations, which are fundamental to many state-of-the-art generative models. To address this challenge, we propose a general meta-algorithm for handling sigmoid activations which leverages classical notions of counter-example-guided abstraction refinement. The key idea is to "lazily" refine the abstraction of sigmoid functions to exclude spurious counter-examples found in the previous abstraction, thus guaranteeing progress in the verification process while keeping the state-space small. Experiments on the MNIST and CIFAR-10 datasets show that our framework significantly outperforms existing methods on a range of challenging distribution shifts.
M. Ceravolo, Vincenzo Farina, Lucrezia Fattobene
et al.
PurposeThis study investigates whether colors red or blue in financial disclosure documents (Key Investor Information Documents – KIIDs) affect attention distribution toward the visual stimulus and the perception of financial attractiveness of the products.Design/methodology/approachIn order to observe and measure financial consumers' visual attention, the unobtrusive methodology of eye-tracking is used on a sample of nonprofessional investors, applying an ecological protocol, through a cross-sectional design.FindingsFinancial information processing and visual attention distribution are influenced by the color of the KIID document, as red seems to attract attention, proxied by gazing behavior, more than blue. Red color, compared to blue, is also observed to push investors to rate the products as less financially attractive, especially when the product Risk Reward Profile is high.Practical implicationsThe findings highlight the role of the basic visual properties of documents conveying financial information, prompting to investigate the unconscious and automatic mechanisms of individual's attention and its influence on decision making.Originality/valueUsing the eye-tracking tool, this study bridges neuroscience, color research, marketing and finance and provides new knowledge on the underlying neural mechanisms of financial consumers' behavior.
China and Russia are important agricultural countries in the world. Expanding exports and increasing sales of agricultural products play an important role in the economic development of both countries. To understand the current situation of agricultural exports of the two countries and formulate strategies to expand the marketing of agricultural products, this paper uses the UN Comtrade Database 2009-2018 on Chinese and Russian bilateral agricultural export sales and other trade data to calculate the (expansion margin) and (price margin) of agricultural exports, (quantity margin), to analyze the types, prices, and quantities of exported agricultural products. The results show that China exports to Russia mainly labor-intensive types of agricultural products such as processed agricultural and horticultural products, accounting for 87.46% of total agricultural exports on average. The increase in exports is mainly due to the continuous increase in the prices of exported agricultural products. Russia exports to China mainly land-intensive types of agricultural products such as animal products, grains, oilseeds and fat products, which accounted for an average of 79.07% of total agricultural exports. The increase in exports was mainly due to the continuous increase in types and quantities of agricultural products to develop the export potential of agricultural products and expand sales. In addition, China should expand the types and quantities of agricultural products exported, and Russia should increase the added value of agricultural products and raise the export prices of agricultural products.
This paper presents a detailed analysis of how liquid money market instruments -- sterling bills of exchange -- were produced during the first globalisation. We rely on a unique data set that reports systematic information on all 23,493 bills re-discounted by the Bank of England in the year 1906. Using descriptive statistics and network analysis, we reconstruct the complete network of linkages between agents involved in the origination and distribution of these bills. Our analysis reveals the truly global dimension of the London bill market before the First World War and underscores the crucial role played by London intermediaries (acceptors and discounters) in overcoming information asymmetries between borrowers and lenders on this market. The complex industrial organisation of the London money market ensured that risky private debts could be transformed into extremely liquid and safe monetary instruments traded throughout the global financial system.
We study the value distribution of the Sudler product $\prod_{n=1}^N |2 \sin (πn α)|$ and the Diophantine product $\prod_{n=1}^N (2e\| n α\|)$ for various irrational $α$, as $N$ ranges in a long interval of integers. At badly approximable irrationals these products exhibit strong concentration around $N^{1/2}$, and at certain quadratic irrationals they even satisfy a central limit theorem. In contrast, at almost every $α$ we observe an interesting anticoncentration phenomenon when the typical and the extreme values are of the same order of magnitude. Our methods are equally suited for the value distribution of Birkhoff sums $\sum_{n=1}^N f(n α)$ for circle rotations. Using Diophantine approximation and Fourier analysis, we find the first and second moment for an arbitrary periodic $f$ of bounded variation, and (almost) prove a conjecture of Bromberg and Ulcigrai on the appropriate scaling factor in a so-called temporal limit theorem. Birkhoff sums also satisfy a central limit theorem at certain quadratic irrationals.
Ivan Gryshchenko, Olga Chubukova, Olena Bilovodska
et al.
A strategy combines directions ranging from product development, also includes directly the products production and distribution, as well as strategies aimed at improving products, interacting with consumers, finding and expanding new markets, etc. So the formation and evaluation of strategy of distribution management in logistics which ensures the achievement of consumer’s needs and requests is an important stage for the increase the company’s efficiency. The purpose of the paper is to evaluate the strategies of distribution management for innovative products in logistics by the chemical companies according to the consumer’s perspectives. To achieve that, the main indicators on the chemical industry in the global market are analyzed. Also the business trends of strategic activities in the chemical company on the one of the basic company for this industry are systemized. The authors combined them into four main groups: innovation, digitalization, integration and ecologization. Based on the author’s methodical approach the strategy of distribution management for innovative products in logistics by the chemical company is evaluate. According to the evaluation results it is determined that the strategy needs to be improved by growing rate of the benefit indicator compared to the rate of price growth. Also the complex of recommendations to improve the strategy of distribution management for innovative products in logistics according to consumer’s perspectives which forms the basis for the methodology of the selection the most objective strategic alternative in the chemical company.
М.А. Bayandin, G.B. Sarsembayeva, G.М. Makhmutova
et al.
This article sets out the author’s position on the possibility of using integrated accounts in crop production to keep track of the costs of agricultural activities in the industry in order to help the administration in making effective management decisions.The aim of the study is to develop a system of analytical accounts for the process accounting of production costs of the crop industry.The methodological basis of the work was general scientific principles and research methods: analytical, logical, monographic methods, as well as groupings and comparisons.From the perspective of an integrated approach, the article used the research of modern scientists in the field of managerial accounting in agricultural enterprises, as a result, the author developed and presented the structure of the codes of the system of accounts for cost accounting, the analytical levels in the application of the normative cost accounting system in grain production, the classification of industry costs for variables and fixed elements, as well as codes of accounts of deviation accounting for monitor-ing the execution of estimates for the purposes of management accounting eta.The results of the study are of theoretical and practical significance and can be recommended for use in the organization of management accounting in grain production, and the same showed that grain production is of strategic importance for ensuring the economic security of the state, therefore, great attention should be paid to the key problems of increasing its efficiency – increasing production grain and improve its quality.
It is concluded that the application of the proposed system of synthetic and analytical cost account-ing for agrotechnical measures will provide more detailed information about each responsibility center, which in the future will significantly help in making managerial decisions aimed at improving the efficiency of grain farms
Economics as a science, Marketing. Distribution of products
Vineeth S. Varma, Samson Lasaulce, Julien Mounthanyvong
et al.
In this paper, we consider a network of consumers who are under the combined influence of their neighbors and external influencing entities (the marketers). The consumers' opinion follows a hybrid dynamics whose opinion jumps are due to the marketing campaigns. By using the relevant static game model proposed recently in [1], we prove that although the marketers are in competition and therefore create tension in the network, the network reaches a consensus. Exploiting this key result, we propose a coopetition marketing strategy which combines the one-shot Nash equilibrium actions and a policy of no advertising. Under reasonable sufficient conditions, it is proved that the proposed coopetition strategy profile Pareto-dominates the one-shot Nash equilibrium strategy. This is a very encouraging result to tackle the much more challenging problem of designing Pareto-optimal and equilibrium strategies for the considered dynamical marketing game.