Reviewing, Reviewers and the Scientific Enterprise

Despite their critical importance to the scientific enterprise, reviewers receive no formal training and reviewing has become a skill that they pick up through trial and error. Additionally, because most reviewers do not receive any feedback on their performance, any bad reviewing habits become entrenched over time. This has contributed to significant and unnecessary anxiety about reviewing and to antagonistic encounters between reviewers and authors. This paper seeks to correct this situation by defining reviewers as co-creators of scholarship and the reviewing as a quality control process in the production of scientific scholarship. The paper provides three groups of activities aimed at creating the right mindset among reviewers to facilitate this co-creation and quality control perspective: relationships, commitment and honest decisions and recommendations.reviewers, reviewing, scientific enterprise, scholarship, co-creations, Teaching/Communication/Extension/Profession,

Enterprise resource planning and customer relationship management value

Ruivo, P., Oliveira, T., & Mestre, A. (2017). Enterprise resource planning and customer relationship management value. Industrial Management and Data Systems, 117(8), 1612-1631. https://doi.org/10.1108/IMDS-08-2016-0340Purpose - The purpose of this paper is to develop and test a theoretical model to measure the impact of enterprise resource planning (ERP) and customer relationship management (CRM) systems and moderating relationships of system and process integration on business value. Design/methodology/approach - ERP and CRM systems are analysed with the resource-based view theory and measured by their impact on business value, having in consideration the moderation of system and process integration. The model was tested and analysed with data collected by Microsoft, from firms that have adopted both ERP and CRM systems in their organisation. Findings - ERP system is found to be an important asset to business value, but CRM systems' impact on business value is found to be not significant. System integration as moderator of ERP or CRM system is found to be not significant but has a positive and significant impact on business value. For process integration, the study finds that it is significant only when moderating the CRM system variable. Research limitations/implications - The model shows that the moderating effects of system and process integration are important variables for understanding the joint business value of ERP and CRM. Practical implications - Adopting an ERP system and ensuring system integration provides a direct impact on business value. In order for a CRM system to have a positive impact on business value, process integration with ERP system must be ensured. Originality/value - This study provides new knowledge on how ERP and CRM systems used together may positively influence value from IT investments, and how systems integration and process integration provide business value.authorsversionpublishe

Academic Aunting: Reimaging Feminist (Wo)Mentoring, Teaching, and Relationships.

In this essay, we explore the potential of aunting relationships for rethinking feminist selves and relationships, especially in academic settings. Relationships between generations of academic feminists have often been described using mother-daughter metaphors. We suggest some limitations to framing teaching and learning across academic generations (e.g., teacher-student) and among colleagues (e.g., peer review of scholarship) using maternal imagery. We then argue that the figure of the aunt offers a powerful trope for negotiating relationships between the waves of academic feminism. Aunts provide a generative alternative to mothering and sisterhood as frameworks for feminist womentoring, teaching, and scholarly reviewing

Exploring Latent Semantic Factors to Find Useful Product Reviews

Online reviews provided by consumers are a valuable asset for e-Commerce platforms, influencing potential consumers in making purchasing decisions. However, these reviews are of varying quality, with the useful ones buried deep within a heap of non-informative reviews. In this work, we attempt to automatically identify review quality in terms of its helpfulness to the end consumers. In contrast to previous works in this domain exploiting a variety of syntactic and community-level features, we delve deep into the semantics of reviews as to what makes them useful, providing interpretable explanation for the same. We identify a set of consistency and semantic factors, all from the text, ratings, and timestamps of user-generated reviews, making our approach generalizable across all communities and domains. We explore review semantics in terms of several latent factors like the expertise of its author, his judgment about the fine-grained facets of the underlying product, and his writing style. These are cast into a Hidden Markov Model -- Latent Dirichlet Allocation (HMM-LDA) based model to jointly infer: (i) reviewer expertise, (ii) item facets, and (iii) review helpfulness. Large-scale experiments on five real-world datasets from Amazon show significant improvement over state-of-the-art baselines in predicting and ranking useful reviews

Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition

Product reviews and ratings on e-commerce websites provide customers with detailed insights about various aspects of the product such as quality, usefulness, etc. Since they influence customers' buying decisions, product reviews have become a fertile ground for abuse by sellers (colluding with reviewers) to promote their own products or to tarnish the reputation of competitor's products. In this paper, our focus is on detecting such abusive entities (both sellers and reviewers) by applying tensor decomposition on the product reviews data. While tensor decomposition is mostly unsupervised, we formulate our problem as a semi-supervised binary multi-target tensor decomposition, to take advantage of currently known abusive entities. We empirically show that our multi-target semi-supervised model achieves higher precision and recall in detecting abusive entities as compared to unsupervised techniques. Finally, we show that our proposed stochastic partial natural gradient inference for our model empirically achieves faster convergence than stochastic gradient and Online-EM with sufficient statistics.Comment: Accepted to the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2019. Contains supplementary material. arXiv admin note: text overlap with arXiv:1804.0383

Implicit norms

Robert Brandom has developed an account of conceptual content as instituted by social practices. Such practices are understood as being implicitly normative. Brandom proposed the idea of implicit norms in order to meet some requirements imposed by Wittgenstein’s remarks on rule-following: escaping the regress of rules on the one hand, and avoiding mere regular behavior on the other. Anandi Hattiangadi has criticized this account as failing to meet such requirements. In what follows, I try to show how the correct understanding of sanctions and the expressivist reading of the issue can meet these challenges