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Nowadays, online retailers are offering a variety of delivery options consisting of varying combinations of delivery attributes. This study investigates how consumers value these delivery attributes (e.g., delivery speed, time slot, daytime/evening delivery, delivery date, and delivery fee) when selecting a delivery option for their online purchases. Mental accounting theory is used to frame the research and to suggest how mental accounts for money, time, and convenience influence consumer preferences for online delivery options. Specifically, the results of a conjoint analysis show that the most important attribute in shaping consumer preferences is the delivery fee, followed by nonprice delivery attributes. For individual attributes, significant differences are found in consumer preferences between gender and income groups. Cluster analysis reveals three consumer segments that show distinct preference structures: We identify a "price‐oriented," a "time‐ and convenience‐oriented," and a "value‐for‐money‐oriented" consumer segment. This study has practical implications for online retailers when implementing suitable delivery strategies and designing effective delivery options to maximize consumer satisfaction.

Patients with hematological malignancies are susceptible to life-threatening infections after chemotherapy. The current study aimed to evaluate whether management of such patients in dedicated inpatient and emergency wards could provide superior infection prevention and outcome. MotivationPatients with hematological malignancies are susceptible to life-threatening infections after chemotherapy. The current study aimed to evaluate whether management of such patients in dedicated inpatient and emergency wards could provide superior infection prevention and outcome.MethodsWe have developed an approach allowing to retrieve infection-related information from unstructured electronic medical records of a tertiary center. Data on 2,330 adults receiving 13,529 chemotherapy treatments for hematological malignancies were identified and assessed. Infection and mortality hazard rates were calculated with multivariate models. Patients were randomly divided into 80: 20 training and validation cohorts. To develop patient-tailored risk-prediction models, several machine-learning methods were compared using area under the curve (AUC).ResultsOf the tested algorithms, the probit model was found to most accurately predict the evaluated hazards and was implemented in an online calculator. The infection-prediction model identified risk factors for infection based on patient characteristics, treatment and history. Observation of patients with a high predicted infection risk in general wards appeared to increase their infection hazard (p = 0.009) compared to similar patients observed in hematology units. The mortality-risk model demonstrated that for infection events starting at home, admission through hematology services was associated with a lower mortality hazard compared to admission through the general emergency department (p = 0.007). Both models show that dedicated hematological facilities and emergency services improve patient outcome post-chemotherapy. The calculated numbers needed to treat were 30.27 and 31.08 for the dedicated emergency and observation facilities, respectively. Infection hazard risks were found to be non-monotonic in time.ConclusionsThe accuracy of the proposed mortality and infection risk-prediction models was high, with the AUC of 0.74 and 0.83, respectively. Our results demonstrate that temporal assessment of patient risks is feasible. This may enable physicians to move from one-point decision-making to a continuous dynamic observation, allowing a more flexible and patient-tailored admission policy. This research was supported by the ISRAEL SCIENCE FOUNDATION (grant No. 1955/15).

While the de-marketing of smoking among teenagers has received wide attention in the literature, few have examined the issue of whether messages should be uniform across cultures. Globally, the vast majority of anti-smoking messages are based on fear appeals to the negative effects on the (potential) smoker him/herself. This research suggests that such a global strategy may be suboptimal. Specifically, while ads portraying the negative consequences of smoking to oneself may work for teens from individualist cultures, they are less effective in collectivist cultures. In contrast, messages orientated towards the adverse effects on other people are more effective in collectivist environments. Given the astronomical amounts spent on anti-tobacco advertising, this finding offers significant advantages for creating effective anti-smoking messages.

Solid major accident prevention management is characterized by efficient and effective risk assessments. As a means of addressing the efficiency aspect, decision support analysis software is becoming increasingly available. This paper discusses the results of a survey of decision support tools for investigating (internal and external) major hazards in the chemical industry. The most significant features, such as the usability and the functionality of the toolkits are discussed. Toolkit characteristics are analyzed in the light of the different major risk decision process stages. Consequently, valuable supportive information for company decision makers purchasing such software is given. Furthermore, conclusions are drawn and recommendations are formulated for establishing priorities for future risk toolkits developments.

Based on an experiment among more than 2000 students in nine culturally diverse countries, this article investigates how the cultural characteristic of uncertainty avoidance moderates the impact of valence and intensity on the effectiveness of antismoking messages. The results show that adolescents with high uncertainty avoidance respond more favorably to loss-framed advertisements than to benefit-framed advertisements, whereas the opposite holds for those with low uncertainty avoidance.

Facing cutthroat price competition, manufacturers and retailers increasingly rely on non-price promotion techniques, such as premium promotions, where consumers receive a free gift with the purchase of a product. Despite the omnipresence of premium promotions, research to date has not fully answered the question whether and under which conditions premiums outperform plain price cuts. We use data from a large online shopping simulation with more than 2,000 consumers to model purchase behavior in response to premiums and price cuts. Our results indicate that the impact of premiums on consumers’ incidence, choice, and quantity decisions is systematically lower than that of equivalent price cuts. However, a premium’s relative underperformance in terms of purchase effects may well be offset by the premium’s cost advantage, especially in hedonic categories and for private label brands. We demonstrate that, as a result, manufacturers and retailers alike may be better off spending their promotion dollars on premiums than on price cuts.

We describe a new exact procedure for the discrete time/cost trade-off problem in deterministic activity-on-the-arc networks of the CPM type, where the duration of each activity is a discrete, nonincreasing function of the amount of a single resource (money) committed to it. The objective is to construct the complete and efficient time/cost profile over the set of feasible project durations. The procedure uses a horizon-varying approach based on the iterative optimal solution of the problem of minimising the sum of the resource use over all activities subject to the activity precedence constraints and a project deadline. This optimal solution is derived using a branch-and-bound procedure which computes lower bounds by making convex piecewise linear underestimations of the discrete time/cost trade-off curves of the activities to be used as input for an adapted version of the Fulkerson labelling algorithm for the linear time/cost trade-off problem. Branching involves the selection of an activity in order to partition its set of execution, modes into two subsets which are used to derive improved convex piecewise linear underestimations. The procedure has been programmed in Visual C++ under Windows NT and has been validated using a factorial experiment on a large set of randomly generated problem instances.

The rapid digitization of the entertainment industry urges managers to find new ways to improve marketing effectiveness and combat online piracy. Smart devices and innovative online services generate brand-related tracking data that reflect consumers’ brand engagement (e.g., digital usage rates, Facebook Likes), yet managers are unaware of how to use these big data to improve brand performance. We therefore propose a model to assess how digitally tracked engagement behaviors, namely consumer brand usage (CBU, measured by digital usage rates) and consumer brand endorsement (CBE, measured by Facebook Likes), drive the impact of promotion and new product releases and affect the role of piracy. We apply our framework to the U.S. music industry, and develop web scrapers and API mining scripts to collect data for 569 music artists over 66 weeks. Both CBU and CBE enhance marketing effectiveness and positively mediate the sales impact of piracy, thereby offsetting piracy’s cannibalization effect by 55%. However, CBU exerts a systematically stronger influence in these relationships than CBE. Using simulations, we show how our findings can help managers to improve the allocation of their marketing efforts across brands and reduce the detrimental sales impact of piracy.

Many service firms acquire customers by offering free-trial promotions. A crucial challenge is to retain customers acquired with these free trials. To address this challenge, firms need to understand how free-trial customers differ from regular customers in terms of their decision making to retain the service. This article conceptualizes how a customer’s retention decision is driven by marketing communication and usage behavior, and develops hypotheses about the impact of free-trial acquisition on this process. To test the hypotheses, the authors model a customer’s retention and usage decisions, distinguishing between usage of a flat-rate service and usage of a pay-per-use service. The model allows for unobserved heterogeneity and corrects for selection effects and endogeneity. Based on household panel data from a digital TV service, the authors find systematic behavioral differences which make the average customer lifetime value (CLV) of free-trial customers 59% lower than that of regular customers. However, free-trial customers are more responsive to marketing communication and usage rates, which offers opportunities to target marketing efforts and enhance retention rates, CLV, and customer equity.

Bundle promotions—the practice of granting consumers a discount when they buy a certain number of units from a designated range of stockkeeping units—have gained popularity among manufacturers and retailers. In this research, the authors investigate the purchase effects of bundle promotions for a category of packaged goods. Contrary to intuition, they find that promotional bundles are far more effective at inducing switching than at boosting category sales. The strong switching effects result from two mechanisms: (1) Stockkeeping units that are part of a bundle promotion appear to reinforce each other's choice probability, and (2) the bundle discount tends to attract consumers even if they do not buy enough to qualify for the price reduction. The weak category effects follow from the notion that the purchase quantity requirement is often too stringent to make consumers buy earlier and/or more in the category. The authors develop incidence, quantity, and choice models that incorporate the intricate bundle mechanisms, and they use simulations to contrast the sales impact of bundle and traditional per-unit promotions. On the basis of the model estimates, they present managerial implications and tentative guidelines for optimal bundle design.