Online Demand Research Articles

Effects of online-to-offline spillovers on manufacturers’ online sales mode selection

Numerous studies have confirmed the existence of two types of spillover effects (i.e., negative or positive spillover) from online sales to offline sales. The manufacturer’s chosen strategy for sales mode can be challenging and complex when considering online sales efforts and spillover effects. With positive spillover, enhancing online sales efforts not only increases online demand but also stimulates offline demand. Conversely, with negative spillover, an increase in online sales efforts boosts online sales while simultaneously cannibalizing offline sales. This paper analyzes the manufacturer’s choice between reselling and agency selling modes and investigates how both positive and negative spillover effects influence the manufacturer’s choice when considering online sales efforts. We demonstrate that, with a significant positive spillover, agency selling is the manufacturer’s optimal strategy. Counterintuitively, we show that the larger the negative spillover, the more the manufacturer prefers reselling. Furthermore, when the negative or positive spillover is relatively low, the manufacturer’s decision regarding the sales mode depends on the commission rate. Moreover, our study highlights that the optimal selling strategy for the manufacturer is affected by consumers’ sensitivity to online sales efforts. We also find that when considering online sales efforts, in scenarios where there is a significant positive spillover, the manufacturer is better off adopting agency selling. Meanwhile, the manufacturer may obtain negative profits from the online channel but can be compensated by the profits made from the offline channel. We extend the model to consider online sales efforts provided by the online platform in two selling modes, demonstrating the robustness of our main outcomes.

Analysis of government subsidy strategies for blockchain-enabled green supply chains under competition

This study uses game-theory to construct a dual-channel green supply chain consisting of a manufacturer, a blockchain-enabled platform, and a non-blockchain-enabled offline retailer, with the aim of comparing two government subsidy strategies: greenness investment cost subsidy and production subsidy, and exploring the impact of blockchain adoption. First, firms without government subsidy should adopt blockchain only when privacy costs are low. Both subsidy strategies can always motivate the manufacturer to increase greenness investment levels and achieve a triple-win situation. Second, when the government controls the greenness investment level, the demand, profits, and subsidy amount under cost subsidy are always lower than those under production subsidy. Third, when the government controls the subsidy amount, the greenness investment level under cost subsidy is always higher than that under production subsidy. The offline demand and retailer’s profit are higher under production subsidy, while the online demand and platform’s profit are higher under cost subsidy. The manufacturer’s profit is higher under cost subsidy only when the subsidy level is low. Additionally, regardless of governmental goals, the platform’s profit is always higher than the retailer’s profit, indicating that downstream firms under government subsidy should always adopt blockchain.

Online demand peak shaving with machine‐learned advice in digital twins

AbstractAs the use of physical instruments grows, control algorithms are being increasingly deployed to enhance efficiency and reliability through digital twin technology. Demand load management is central to energy systems within digital twins, which significantly impacts operational costs. Peak demand loads can lead to substantial monthly utility expenses without proper management. AMPAMOD, a randomised online algorithm incorporating machine‐learned insights is introduced to optimise battery operations and mitigate peak demand loads. AMPAMOD leverages limited‐bit information from machine learning models to inform its online decision‐making process for cost‐effective load management. We provide theoretical evidence demonstrating that AMPAMOD maintains minimal advice complexity, has a linear computational cost, and achieves a bounded competitive ratio. Extensive trace‐driven experiments with real‐world household data reveal that AMPAMOD successfully reduces peak loads by over 90%, outperforming other benchmarks by at least 50%. These experimental findings align with our theoretical assertions, showcasing the effectiveness of AMPAMOD.

Online consumer demand during and after natural disasters: Evidence from the COVID-19 pandemic

Natural disasters arouse threats of health and economic damage, altering consumer behavior in both the short term and the long term. By adapting coping theory to health and economic threats, this study examines the heterogeneous impacts of epidemic disasters, in the COVID-19 pandemic context, on online consumer demand by product type and their dynamics after stabilization. Our results show that the local epidemic outbreak initially increased (decreased) the online demand for essential (discretionary) goods, but the increased purchasing of essential goods diminished as the associated risks were alleviated, whereas the decreased demand for discretionary goods persisted. Additional analyses reveal that engaging more with online consumers during disastrous periods helps retain a specific type of consumer even after stabilization. Our findings highlight the role of threats as psychological drivers in changing online demand, providing managerial implications for online retailers on how to convert threat-driven consumers into regular customers afterward.

The effect of prosocial behavior and its intensity on doctors’ performance in an online health community

Online health communities (OHCs) are encouraging doctors to engage in prosocial behaviors to promote sustainable development of OHCs and benefit the user base, which may further influence doctor-level performance. However, it is unclear how prosocial behavior and its intensity affect doctor-level performance, such as doctor-patient relationship, online reputation, and online demand. The purpose of this study is to test the effects of prosocial behavior and its intensity on doctor-patient relationship, online reputation, and online demand. We conducted a series of empirical analyses on 103,208 doctor-level samples from a leading OHC in China. Initially, we applied the propensity score matching (PSM) method to obtain a more balanced sample, then, we employed a negative binomial regression model to analyze the sample after PSM, and finally, we performed a robustness test using a zero-inflated negative binomial regression model and changing the time window. Our research findings indicate that prosocial behavior positively affects the doctor-patient relationship, doctors’ online reputation, and online demand. The intensity of prosocial behavior has an inverted U-shaped relationship with the doctor-patient relationship, online reputation, and online demand. The doctor’s clinical title negatively moderates the effect of prosocial behavior on the doctor-patient relationship and online demand. Therefore, OHC operators can draw on our findings to encourage more doctors to engage in prosocial behavior while suggesting they consider the role of the intensity of prosocial behavior and clinical title.

Overcoming Youth Online Isolation in China by Supporting Neighbourhood Community Interactions through Co-Created Outdoor Personal Spaces

The fast pace of urban life, the high cost of living, and the complexity of interpersonal relationships are triggering social isolation among urban residential youths in China. Furthermore, online demand is causing social isolation due to a lack of physical human interactions which leads to poorer mental and physical health. Therefore, there is a need to get the youth generation to go offline to enjoy natural living and live healthily in society. A desktop survey was conducted as part of a larger study to improve community engagement in urban residential outdoor environments for youth. The results found offline community engagement could improve the well-being of young residents by taking advantage of its positive physical and mental effects. Using visual tools for exploring the functional and aesthetic landscape needs and motivation for co-creating personalized spaces in a residential community is proposed. After the introduction of the background problem, this proposal will present the literature survey results on youth generation in China, urban residential outdoor environment, and improved community engagement. This study contributes to the development of an offline community engagement model for youths in residential communities. The proposed model is expected to alleviate the current social isolation situation for the youth generation. Future studies are recommended to include the identification of key characteristics of the youth generation and the lifestyles they prefer, applications of the new co-design mode in the design phase, and application of new visual technology in landscape design practice.

Joint optimization of inventory replenishment and rationing policies for an omnichannel store with both in-store and online demands

Boosted by the COVID-19 pandemic, omnichannel retail has become a popular retail model and is expected to further grow. We study inventory replenishment and rationing of a product in an omnichannel store that fulfils both in-store and online demands. Both demands follow Poisson process. As each in-store demand must be satisfied immediately whereas each online order has a delivery time window, the two demand classes have different behaviors facing stockout: offline demand is lost while online order is backlogged with backorder cost charged only if it is delivered late. The inventory replenishment and rationing between the two demand classes in this omnichannel inventory system are controlled by a continuous review (Q, r) policy and a critical level policy, respectively. The intertwined nature of inventory replenishment and rationing in this system makes its analytical cost evaluation and its joint optimization of the two policies challenging. After deriving an analytical formula for evaluating the expected cost of the system, two heuristic algorithms are proposed to jointly optimize the two policies. Numerical experiments on randomly generated instances demonstrate that the multi-start local search algorithm and the scatter search algorithm can obtain a near-optimal solution quickly with percentage cost gap no larger than 0.81% and 0.18%, respectively, and the critical level policy can yield cost savings up to 10.91% compared to the first-come, first-served policy. Finally, a sensitivity analysis is conducted to evaluate the influences of system parameters on the optimal critical level.

DLSTM-MSF: Distributed LSTM Models for Multimedia Streaming Workload Forecasting Based on Kafka Environment

This paper introduces DLSTM-MSF, a distributed approach designed to address the challenge of demand forecasting in multimedia streaming workloads. DLSTM-MSF leverages the power of multi-LSTM networks, each tailored to predict data demand for a specific type of multimedia streaming workload. The central problem addressed in this research is the accurate prediction of workload demand in a dynamic and diverse multimedia streaming environment. To achieve specialization, the training time series set for each LSTM network comprises examples with targets belonging exclusively to the workload type it is designed to predict. This specialization ensures that each LSTM network becomes proficient at capturing the unique demand patterns associated with its designated workload category. The methodology of the proposed approach is based on building the best forecasting model for each multimedia streaming workload type by exploring various combinations of LSTM hyper-parameters using the grid search method. This enables the proposed approach to effectively capture nonlinear patterns in time series data. Furthermore, the implementation of DLSTM-MSF incorporates Apache Kafka for online demand prediction, utilizing the best-developed model for each workload type. Experimental evaluations of DLSTM-MSF compare the performance of two ensemble-learning LSTM models (Ensemble V1 and Ensemble V2) with a single LSTM model. The results unequivocally highlight the superiority of Ensemble V1, with reductions of 71.85% and 74.88% in RMSE and MAE values, respectively, compared to the single LSTM model. Index Terms— Multimedia streaming, LSTM, Ensemble learning, Forecasting, Workload Demand, Big data.

Integrated inventory replenishment and online demand allocation decisions for an omnichannel retailer with ship-from-store strategy

Retailing has changed dramatically from single-channel brick-and-mortar stores to multi-channel and omnichannel retailers over the last few decades. Omnichannel retailers employ different strategies to integrate online and offline sales channels as well as order fulfillment processes. Among these strategies, the ship-from-store is the most popular and widely accepted among retailers. It enables retailers to use inventory from store locations to fulfill online demand. An omnichannel retailer with a distribution center and a retail store has to make important, interlinked decisions — (1) how much inventory to keep at the retail store, and (2) where to fulfill the online demand from and how much. In this work, we model the integrated inventory replenishment and online demand allocation decisions for an omnichannel retailer employing the ship-from-store strategy. We analyze this problem for both single-period and multi-period settings. We extend the analytical framework of the single-period problem by providing a finite-horizon Markov decision process (MDP) formulation for the multi-period problem. Our findings suggest that for a single-period setting, decentralized inventory replenishment and demand allocation system maximizes the profit of the omnichannel retailer for low values of the incentive for fulfilling the online demand through store inventory, while for sufficiently high values of the incentive, a pooled system provides the optimal profit. An increment in the discount factor has the same effect on the optimal decisions in a multi-period setting as that of salvage value in a single-period setting for a given value of the incentive for the ship-from-store strategy. We also provide several extensions (such as cross selling, endogenous and correlated demand streams) of our analytical framework for the multi-period problem.

Examining Audiences’ Information-Seeking Behavior Surrounding the Super Bowl and Sex Trafficking: Insights From Google Trends Data

Sex trafficking is a prominent human rights issue that has been increasingly associated with the hosting of large-scale sport events. Despite insufficient evidence demonstrating a causal or correlative link, event stakeholders have implemented antitrafficking efforts in attempts to prevent and promote awareness of sex trafficking. Using Google Trends data to measure audiences’ information-seeking behavior online and Twitter data as a proxy for antitrafficking efforts on social media, we employed a difference-in-differences approach to estimate the change in online demand for sex-trafficking information among the residents of Miami-Dade, the host city of Super Bowl LIV (54). Findings highlight an increase in the online demand for sex-trafficking information in the host city during and after the event. This increased demand attributed to the Super Bowl may offer support for host communities utilizing sport events to promote awareness of pressing social issues.

Online Demand Response Characterization Based on Variability in Customer Behavior

Online Demand Response Characterization Based on Variability in Customer Behavior

The Use of Collaborative Robots as an Internal Logistics Solution and the Effect of These Robots on Increased Operational Efficiency

It is noteworthy that operational transactions demonstrated a corresponding increase with the burgeoning e-commerce activity during the COVID19 pandemic period. The unanticipated nature of the pandemic event has revealed the limitations of many companies in coping with the surging online demands. These limitations manifest as delays in order fulfillment, inaccuracies and discrepancies in delivered orders, resulting in substantial reductions in both customer and personnel satisfaction, internal disruptions within the organizations, and an inadequacy in the workforce. To mitigate these challenges, several companies have embarked on the utilization of Collaborative Robots. 
 The present study aims to set out the specific parameters and the extent to which operational efficiency within the organization is enhanced through the adoption of Collaborative Robots.

Fulfillment Flexibility Strategy for Dual-channel Retail Networks

Flexibility design has been widely adopted in practice as a competitive strategy to respond effectively to uncertainties. In this paper, we analyze the fulfillment flexibility for dual-channel retail networks, in which the firms should fulfill both online demands from retailing platforms and in-store offline demands. In particular, by setting the order of fulfillment, we find that a dual-channel retail network can be equivalently transformed to an online retail network with stochastic inventory and demand. By implementing a copositive programming, we obtain an asymptotic robust lower bound for the ratio of expected sales to fully flexible expected sales under a K-chain design. This bound only depends on the partial moment information and support set of demands, rather than the complete demand distribution information. Interestingly, we derive the optimality of K-chain in symmetric balanced networks and the performance of K-chain under different distributions is robust. In addition, numerical experiments are conducted to further deliver some insights for practitioners. The uncertainties of in-store demand or inventory will reduce the expected sales while both fulfillment flexibility and safety inventory can be used to enhance the performance of a retail system. Finally, we find that the correlation coefficient between in-store demand and online demand will affect the decision-making significantly.

Mission Possible: Blogging Russian Orthodox Priests

The present paper dwells on the modern phenomenon of the clergy going online and exploring new audiences. The empirical study conducted by the author concerned the activities of popular Russian-speaking Orthodox bloggers whose heightening media presence is aimed at digital missionary work and catechism. The research was organized in accordance with the theoretical framework of the concept of communicative figurations that was coined by Andreas Hepp. This constructivist approach implies that mediatization blurs the borders between previously disentangled actors and encourages the growth of their interactions and, thus, a tighter social reality. To embody a communi-cative figurations-oriented study, the author lays down methodological foundations that are able to express the nature of personal practices and the reflections on them. For this purpose, the methods used consisted of case studies, expert and field interviews, and online text analysis. The findings can be set out in the following manner. Online media activity and social networking allow greater transparency and a wider audience. Despite stereotype and politicized doxa, the online demand for a specific niche of purely catechetic Orthodox priest blogging has existed for a decade and a half. Over the years, media practices of missionary work, catechism, and preaching have been formed, mainly on such social networks as VK.com, LiveJournal and Instagram, and on YouTube channels. This dynamic has been growing: priest blogs have acquired the audiences of tens of thousands of subscribers. This is because priests use contemporary language when addressing the public for the purpose of missionary work and catechism. They attract an audience of the Russian-speaking network of actors that is diverse in age, gender, and country of residence. Seeing and aiming beyond the conservative confines of an offline parish and church, blogging priests have the opportunity to create their own audience – to reach out to a particular generation, choose the style and content of a sermon or testimony of faith. In turn, the audiences choose priest bloggers according to their interests and the preferred methods of religious participation. Orthodox blogger priests strive to consolidate their efforts, to promote various forms of testimony of faith in the digital space. The central direct consequence of the mediatization of catechism and missionary practices is the promotion of a new image of the priest and a new version of priest–layman interaction, both contributing to a new church construct.

Omni-channel inventory management of perishable products under transshipment and substitution

We consider a retailer selling two substitutable products through an offline and online channel. The products are stored at separate inventory locations and transshipments from the offline channel to online customers are possible, i.e. we propose a two-location inventory model incorporating customer-driven inventory interactions (demand substitution initiated by the customer) and retailer-driven inventory interactions (transshipment, i.e. demand substitution initiated by the retailer). We compare this model to a system incorporating customer-driven substitution only and use empirical data about customers’ preferences and perceptions of grocery shopping in online and offline channels to understand the trade-offs between various substitution forms in omni-channel retailing. In a numerical study, we discuss the optimal order quantities and resulting expected profits and quantify the value of a retailer-driven transshipment strategy. Although the empirical results reveal that online demands are relatively low and product substitution rates are particularly high, our results confirm that a transshipment system can achieve higher expected profits than when considering customer substitution only. Finally, we discuss the performance of policies that ignore product and/or channel substitution. We find that having complete information about product substitution is crucial for a grocery retailer’s omni-channel order quantity decision.

Benefit of Consumer Showrooming for a Physical Retailer: A Distribution Channel Perspective

Consumer showrooming has become a common phenomenon in the retail industry, but little is known about its influence on the interplay between an upstream supplier and a downstream retailer in a distribution channel. Our study examines such an influence by considering an upstream supplier who sells a product through a downstream physical retailer based on a wholesale contract. At the same time, based on an agency contract, the supplier sells the product on an online retail platform where a nonneglectable amount of consumer returns occurs due to product misfits. We find that, on the one hand, consumer showrooming enables the supplier to take a greater share of the offline channel profit by exploiting the physical retailer’s deterrence of showrooming. On the other hand, consumer showrooming as a mixed-channel shopping method makes the supplier less capable of adjusting the direct online demand according to its marginal cost of handling returns. Such a return-loss-propagation-moderating effect protects the downstream physical retailer from the upstream supplier’s strategic pricing that intends to shift sales from offline to direct online when the supplier finds it cost efficient to handle online product returns. Our study also shows that the retailer can replace the wholesale contract with the store-within-a-store model with a rental fee contract to leverage the upstream supplier’s incentive to mitigate the adverse effect of showrooming. The findings inform the physical retailer that the more critical issue may not be showrooming itself but aligning with the upstream supplier to promote offline sales in the presence of showrooming. This paper was accepted by Kartik Hosanagar, information systems. Funding: S. Kumar thanks Temple Center for International Business Education and Research for partially supporting this research. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2020.01990 .

Sales forecasting of a food and beverage company using deep clustering frameworks

The competition among Food & Beverage companies has substantially increased in today's age of digitization. Sales forecasting is one of their main challenges. Due to space limitations, employee shortages, and rising online demand, retail sales forecasting became extremely important for Food and Beverage companies. This research analyzed the sales data of a multinational Food & Beverage Company. It proposed a framework using Gaussian Mixture Model (GMM) clustering, Hierarchical Agglomerative Clustering (HAC), and Random Forest algorithm for forecasting sales. This model analyzes the impact of the weekends, holidays, promotional activities, customer sentiments, festivals, and socio-economic situations in sales data and is able to forecast sales ranging from one to 15 months. An investigation of the suggested model's performance compared to numerous cutting-edge sales forecasting techniques is carried out to show its efficacy. Here, we demonstrate that the proposed hybrid model surpasses current predicting and computing efficiency methods. The results of this study can help retail managers to allocate resources and manage inventories in well-informed ways. The findings suggest that combining many strategies may produce the most precise forecasts.

How pop-ups drive online sales: moderating effects of online promotions

PurposeThis study aims to examine how the effect of pop-ups on an omnichannel brand’s subsequent online sales is moderated by the brand’s online price and premium promotions, paid search and popularity signaling.Design/methodology/approachUsing a difference-in-differences approach, this study appraises variations in two similar Chinese apparel brands’ online sales before and after one of the brands’ implementations of its pop-ups and how the brand’s online promotions modify the pop-ups’ effect.FindingsUnique, interactive pop-ups boost brands’ subsequent online sales. Online price promotions negatively moderate the effect; online premium promotions and paid search positively moderate it. Moreover, the product’s popularity diminishes the extent to which a pop-up stimulates online demand. These findings can be partially generalized to other categories, such as utilitarian products.Practical implicationsOnly certain online strategies enhance the effect of pop-ups on brands’ online sales, so practitioners should strategically select appropriate promotion combinations when they operate pop-ups and allocate resources across channels. In addition, the moderating influence of online promotions on pop-ups depends on the type of product being promoted.Originality/valuePop-ups offer proven abilities to deliver sensory experiences to online shoppers, reinforce brand awareness and loyalty and boost online sales. This study extends prior research by examining how various online promotions moderate pop-ups’ effects.

The Impact of Distance in Retail Markets

We examine the demand-side implications of Amazon's distribution and logistics investments. Our results indicate that online demand–transactions at Amazon and its competitors–does not respond to the consumer's proximity to Amazon's upstream fulfillment distribution facilities, suggesting that their densification did not differentially improve local shipping times and on-time delivery. Instead, we find that investments in last-mile delivery facilities and services allow the company to improve shipping times more directly in the urban markets served by these facilities, simultaneously increasing demand through the rollout of same-day service options and reducing the visits to traditional brick-and-mortar retail.

Consumers' purchasing decisions of a dual-channel supply chain system under return and warranty policies

ABSTRACT With the rapid growth of internet technology, particularly during the COVID-19 pandemic, a major portion of consumers have intended to do online shopping. Also the use of eco-friendly products is essential in today's environment and human health. Thus this paper investigates the consumers' purchasing behaviour towards substitutable non-green and eco-friendly products in a dual-channel (offline and online channels) supply chain system. The manufacturer offers a novel combination of promotions such as a return policy in the online channel and a warranty policy in the offline channel depending on the position and situation of consumers. Here, the environmental burden is reduced by considering remanufacturing/refurbishing used products during the warranty period. Therefore, consumers' demand depends on price, eco-friendliness level, warranty and return agreements. The entire problem is modelled under centralised and decentralised decision-making scenarios. Finally, the profit maximisation problem is formulated and solved in the game theory framework. A series of sensitivity analyses of various parameters is conducted numerically to validate the problem. It is observed that, due to the instant return facility in the online channel, online demand is higher than the offline with a higher warranty period.