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Studies a non‐homogeneous Poisson process software reliability model with failure rate based on Zipf’s law. Discusses the rate function, mean value function and the estimation of parameters. The proposed model can be used to analyse the reliability growth. The results of applying the proposed model and Duane model to several actual failure data sets show that the model with failure rate observed from Zipf’s law can fit not only in operating software but also in testing software. The result also indicates that the proposed model has better long‐term predictive capability than the Duane model for failure data sets with power law’s failure rates

The problem of optimal software reliability design is considered. Allocation models are usually used to compute the target reliability for each module of a software system to maximize the overall system reliability. This objective can also be achieved by employing redundancy, e.g. N‐version programming technique (NVP). A method bridging the allocation model and redundancy approach is derived. The proposed model simultaneously determines both the optimal amount of redundancy and target reliability for each module to achieve the best reliability while the total cost stays within the budget.

Drawing on the fairness theory, this paper aims to propose a conceptual framework that investigates how co-creation in the failed service delivery (coproduction intensity) and co-creation in the service recovery affect customers’ evaluation of the firm’s competence, justice and ethicalness, and ultimately their willingness to co-create in the future.

Tax services were chosen as the research context. A consumer panel consisting of individuals who live in the USA and have used tax preparation services within the past year was recruited. The first study explores what happens to customers’ ethical perceptions during a failed co-created service encounter. A secondary study investigates what happens to customers’ ethical perceptions in the event that the failed co-created service is recovered.

The findings show that customers’ perceptions of the firm’s abilities and ethics are impeded by coproduction intensity but favorably influenced by co-creation of recovery.

A sense of ethicalness and fairness is violated when co-created service failure occurs, but fortunately, practitioners can count on engaging customers in the service recovery process as co-creators of the solution to positively alter perceived ethicalness and fairness.

Failed co-created services represent an under-researched area in the marketing literature. Current investigations of co-created service failures have largely approached the notion of fairness from a perceived justice perspective without referencing ethical judgments. However, fairness is grounded in basic ethical assumptions of normative treatment. This research is among the first to highlight the importance of perceived ethicalness in the context of co-created service failure and recovery.

Governments around the world have used social distancing methods to slow the spread of COVID-19. Some people, however, have ignored repeated warnings about the need to maintain social distance. The purpose of this study was to segment individuals based on their perceptions of social distancing with respect to shared constructs, such as attitudes and demographic profiles. The findings can assist social marketing efforts to target specific groups for health campaigns.

This study used a priori methods, meaning that the type and number of segments were determined in advance. Amazon’s Mturk was used to collect data from an online sample of US residents (n = 759) in May 2020, in the midst of the COVID-19 pandemic.

Individuals’ perceptions of social distancing were segmented as follows: Segment 1 = majority social distancing followers; Segment 2 = social distancing inbetweeners; and Segment 3 = minority social distancing rebels. Interestingly, some of these segments were strongly affiliated with political parties. In addition, the results show attitudes toward social distancing appear to be influenced by individuals’ beliefs regarding their susceptibility to coronavirus and the potential severity of the symptoms or disease’s impact on their lives.

This study makes several theoretical and practical contributions to the literature on these issues. In particular, it involved the application of the health belief model to the context of attitudes toward social distancing, which were found to be influenced by individuals’ beliefs regarding whether they are susceptible to coronavirus infection and whether the symptoms or disease could have a significant effect on their lives.

The results of this study will assist public health researchers, social marketers and policymakers in efforts to improve the effectiveness of health campaigns. Public health campaigns in the USA need to be bi-partisan. The finding that the social distancing rebels were mostly Republicans is consistent with an earlier report that those who identify with this party were less convinced than those who identified with the Democratic party regarding the efficacy of maintaining social distancing measures and more concerned about the adverse effects of these measures on the economy.

Only a few studies have segmented populations based on their perceptions of social distancing. This study was designed to understand the distinguishing features of such segments to enhance health messaging and content and convince those reluctant to engage in social distancing to view the issue from the perspective of marketing and medical practitioners.

This paper seeks to present the use of Rough Sets (RS) theory as a processing method to improve the results in customer satisfaction survey applications.

The research methodology is to apply an innovative tool to discover knowledge on customer behavior patterns instead of using conventional statistical methods. The RS theory was applied to discover the voice of customers in market research. The collected data contained 422 records. Each record included 20 condition attributes as well as two decision attributes. The important attributes that ensured high quality of classification were generated first. Then decision rules for classifying high and low overall satisfaction and loyalty categories were derived.

Three important facts were found: the important product and service attributes that lead to overall satisfaction and loyalty; the percentage of latently dissatisfied customers; and customer decision rules.

The study is limited by the case company and its experience. These rules were presented to the company's sales and marketing managers who believed that they provided them with valuable information for creating strategies to increase customer satisfaction and retention.

RS theory provides a mathematical tool to discover patterns hidden in survey data. The paper describes a new attempt of applying a RS‐based method to analyze overall customer satisfaction and loyalty behavior through regular satisfaction questionnaire surveys.