A compositional method for reliability analysis of workflows affected by multiple failure modes

We focus on reliability analysis for systems designed as workflow based compositions of components. Components are characterized by their failure profiles, which take into account possible multiple failure modes. A compositional calculus is provided to evaluate the failure profile of a composite system, given failure profiles of the components. The calculus is described as a syntax-driven procedure that synthesizes a workflows failure profile. The method is viewed as a design-time aid that can help software engineers reason about systems reliability in the early stage of development. A simple case study is presented to illustrate the proposed approach

A cultural approach to brand equity: The role of brand mianzi and brand popularity in China

International marketers face a challenge in applying Western-derived theory in emerging markets such as China where there has been rapid economic growth and sociocultural transition. This study develops “culturally contextualized” determinants of brand equity in China. A qualitative approach consisting of 30 interviews revealed two new factors linking to brand equity: brand popularity and brand mianzi. A quantitative questionnaire survey with a sample of 321 Chinese smartphone users was conducted to test the hypotheses. The quantitative study’s findings further revealed that brand popularity and country of origin image affect brand loyalty, brand awareness, perceived quality, and brand mianzi. Additionally, the effects of brand popularity and country of origin image on brand equity were mediated by the four determinants. Finally, brand mianzi was found to be the second most important determinant of brand equity after brand loyalty, highlighting the importance of cultural factors in branding activities in emerging markets

Microwave Reflectometry Sensing System for Low-Cost in-vivo Skin Cancer Diagnostics

Skin cancer is one of the most commonly diffused cancers in the world and its incidence rates have constantly increased in recent years. At the current state of the art, there is a lack of objective, quick and non-invasive methods for diagnosing this condition; this, combined with hospital crowding, may lead to late diagnosis. Starting from these considerations, this paper addresses the implementation of a microwave reflectometry based-system that can be used as a non-invasive method for the in-vivo diagnosis and early detection of biological abnormalities, such as skin cancer. This system relies on the dielectric contrasts existing between normal and anomalous skin tissues at microwave frequencies (in a frequency range up to 3 GHz). In particular, a truncated open-ended coaxial probe was designed, manufactured and tested to sense (in combination with a miniaturized Vector Network Analyzer) the variations of skin dielectric properties in a group of volunteer patients. The specific data processing demonstrated the suitability of the system for discriminating malignant and benign lesions from healthy skin, ensuring simultaneously effectiveness, low cost, compactness, comfortability, and high sensitivity

Structural social capital and innovation. Is knowledge transfer the missing link?

Purpose: This paper aims to address the gap that, to date, no systematic review has been carried out on the role that structural social capital (SC) plays for knowledge transfer and innovation at the interpersonal, inter-unit and inter-firm levels. Individuals and organisations are becoming increasingly involved in collaboration networks to share knowledge and generate innovation. SC theory has been adopted in several areas of study to explain how individuals, groups and organisations manage relationships to generate innovation. Design/methodology/approach: This review covers studies of SC in organisational behaviour, strategy and management over a period of 20 years. Findings: The literature review shows that knowledge types and knowledge transfer processes are the missing links in the relationship between structural SC and innovation. Moreover, the paper demonstrates that seemingly opposite configurations of SC are complementary to each other (structural holes vs dense networks; strong vs weak ties) and that contextual factors should be considered when discussing the effects of SC on knowledge transfer and innovation. In addition, it is the balance of different configurations of SC which enables an individual or a company to explore, access, assimilate and combine different knowledge types, which will lead to improved innovation outcomes. Originality/value: This review facilitates understanding of the role of SC for knowledge transfer processes and the mediating role of knowledge transfer processes and knowledge types in the relationship between structural SC and innovation

What makes information in online consumer reviews diagnostic over time? The role of review relevancy, factuality, currency, source credibility and ranking score

Online consumer reviews (OCRs) have become one of the most helpful and influential information in consumers purchase decisions. However, the proliferation of OCRs has made it difficult for consumers to orientate themselves with the wealth of reviews available. Therefore, it is paramount for online organizations to understand the determinants of perceived information diagnosticity in OCRs. In this study, we investigate consumer perceptions and we adopt the Elaboration Likelihood Model to analyze the influence of central (long, relevant, current, and factual OCRs) and peripheral cues (source credibility, overall ranking scores) on perceived information diagnosticity (PID). We consider the potential moderating effect of consumer involvement, and tested the robustness of the theoretical framework across time. Based on two surveys carried out in 2011 and in 2016, this study demonstrates the dynamic nature of the antecedents of PID in e-WOM. We found that long reviews are not perceived as helpful, while relevant and current reviews as well as overall ranking scores are perceived as diagnostic information in both samples. The significance of the predicting power of review factuality and source credibility has evolved over time. Both central (review quality dimensions) and peripheral cues (ranking score) were found to influence PID in high-involvement decisions

Estimating multiclass service demand distributions using Markovian arrival processes

Building performance models for software services in DevOps is costly and error-prone. Accurate service demand distribution estimation is critical to precisely modeling queueing behaviors and performance prediction. However, current estimation methods focus on capturing the mean service demand, disregarding higher-order moments of the distribution that still can largely affect prediction accuracy. To address this limitation, we propose to estimate higher moments of the service demand distribution for a microservice from monitoring traces. We first generate a closed queueing model to abstract software performance and use it to model the departure process of requests completed by the software service as a Markovian arrival process. This allows formulating the estimation of service demand into an optimization problem, which aims to find the first multiple moments of the service demand distribution that maximize the likelihood of the MAP using generated the measured inter-departure times. We then estimate the service demand distribution for different classes of service with a maximum likelihood algorithm and novel heuristics to mitigate the computational cost of the optimization process for scalability. We apply our method to real traces from a microservice-based application and demonstrate that its estimations lead to greater prediction accuracy than exponential distributions assumed in traditional service demand estimation approaches for software services

What makes hosts trust Airbnb? Antecedents of hosts’ trust towards Airbnb and its impact on continuance intention

Sharing economy platforms are growing at an unprecedented rate. Travel and tourism scholars have been focusing on customers’ sharing intention, yet the literature has largely overlooked what makes sharing service providers trust a sharing economy platform and decide to continue using it. Drawing on sociotechnical theory and the information systems success model, in conjunction with privacy concerns and economic value perspectives, this study develops an integrated model of antecedents and consequences of trust toward sharing economy platforms. Data from 606 Airbnb hosts were analyzed through structural equation modeling. Our research documents the importance of social antecedents (i.e., social value orientation and social utility), technical antecedents (i.e., system quality, service quality, and information quality), economic antecedents (i.e., monetary rewards) and privacy assurance antecedents (i.e., perceived effectiveness of privacy policy) in shaping hosts’ trust toward Airbnb, thereby enhancing their continuance intention with regard to using the platform

Why do travelers trust TripAdvisor? Antecedents of trust towards consumer-generated media and its influence on recommendation adoption and word of mouth

The proliferation of fake and paid online reviews means that building and maintaining consumer trust is a challenging task for websites hosting consumer-generated content. This study tests a model of antecedents and consequences of trust for consumer-generated media (CGM). Five factors are proposed for building consumer trust towards CGM: source credibility, information quality, website quality, customer satisfaction, user experience with CGM. Trust is expected to predict recommendation adoption and word of mouth. Data from 366 users of CGM were analyzed through structural equation modeling and the findings show that all the aforementioned factors with the exception of source credibility and user experience influence consumer trust towards CGM. Trust towards a CGM website influences travel consumers' intentions to follow other users' recommendations and fosters positive word of mouth. Findings also show that information quality predicts source credibility, customer satisfaction, and website quality

Compositional Solution Space Quantification for Probabilistic Software Analysis

Probabilistic software analysis aims at quantifying how likely a target event is to occur during program execution. Current approaches rely on symbolic execution to identify the conditions to reach the target event and try to quantify the fraction of the input domain satisfying these conditions. Precise quantification is usually limited to linear constraints, while only approximate solutions can be provided in general through statistical approaches. However, statistical approaches may fail to converge to an acceptable accuracy within a reasonable time. We present a compositional statistical approach for the efficient quantification of solution spaces for arbitrarily complex constraints over bounded floating-point domains. The approach leverages interval constraint propagation to improve the accuracy of the estimation by focusing the sampling on the regions of the input domain containing the sought solutions. Preliminary experiments show significant improvement on previous approaches both in results accuracy and analysis time

An iterative decision-making scheme for Markov decision processes and its application to self-adaptive systems

Software is often governed by and thus adapts to phenomena that occur at runtime. Unlike traditional decision problems, where a decision-making model is determined for reasoning, the adaptation logic of such software is concerned with empirical data and is subject to practical constraints. We present an Iterative Decision-Making Scheme (IDMS) that infers both point and interval estimates for the undetermined transition probabilities in a Markov Decision Process (MDP) based on sampled data, and iteratively computes a confidently optimal scheduler from a given finite subset of schedulers. The most important feature of IDMS is the flexibility for adjusting the criterion of confident optimality and the sample size within the iteration, leading to a tradeoff between accuracy, data usage and computational overhead. We apply IDMS to an existing self-adaptation framework Rainbow and conduct a case study using a Rainbow system to demonstrate the flexibility of IDMS