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The discipline's most common uses for literature reviews—identifying gaps, developing research agendas, and categorizing the literature—too often fail to challenge, change or advance theoretical perspectives. The authors offer guidance to theorization through literature reviews. The key to theory advancement is consistency between the state of theory and the chosen review type.

A conceptual approach is taken. The authors identify shortcomings in literature reviews of logistics and supply chain management (L&SCM) research and develop a framework to aid theorization from literature.

Literature review types are categorized as inductive theory building, contextualized explanations, theory testing and interpretive sensemaking. The authors argue that the effectiveness of a review type depends on the prior state of theory, which ranges from nascent, to intermediate, to mature. The authors propose the interpretive sensemaking review as a novel review type rooted in the interpretive paradigm.

This study should be of immediate interest and value to logistics and supply chain management scholars—as well as scholars in other fields—because it offers a pathway to theory development through literature reviews. Appropriate applications of the proposed review types will result in more comprehensive theories.

This article lays down arguments for the need to change the way L&SCM scholars use literature reviews. It extends earlier work from the authors (Durach et al., 2017; A New Paradigm for Systematic Literature Reviews in Supply Chain Management, Journal of Supply Chain Management) by outlining four review types, and offering further insights to theorization, as is typically the goal in the synthesis step of literature reviews.

Challenges and opportunities arise from the significantly different perspectives of context-specific versus context-free researchers and the literatures they contribute to. Reviews of one type or the other or both types of literatures may provide different understandings of the state of the art in a particular area of health care management. Suggestions for writing quality reviews are also included along with suggested topics for future reviews.

The purpose of this study is to investigate how book group recommendations can be used as a meaningful way to suggest suitable books to users, given the increasing number of individuals engaging in sharing and discussing books on the web.

The authors propose reviews fine-grained classification (CFGC) and its related models such as CFGC1 for book group recommendation. These models can categorize reviews successively by function and role. Constructing the BERT-BiLSTM model to classify the reviews by function. The frequency characteristics of the reviews are mined by word frequency analysis, and the relationship between reviews and total book score is mined by correlation analysis. Then, the reviews are classified into three roles: celebrity, general and passerby. Finally, the authors can form user groups, mine group features and combine group features with book fine-grained ratings to make book group recommendations.

Overall, the best recommendations are made by Synopsis comments, with the accuracy, recall, F-value and Hellinger distance of 52.9%, 60.0%, 56.3% and 0.163, respectively. The F1 index of the recommendations based on the author and the writing comments is improved by 2.5% and 0.4%, respectively, compared to the Synopsis comments.

Previous studies on book recommendation often recommend relevant books for users by mining the similarity between books, so the set of book recommendations recommended to users, especially to groups, always focuses on the few types. The proposed method can effectively ensure the diversity of recommendations by mining users’ tendency to different review attributes of books and recommending books for the groups. In addition, this study also investigates which types of reviews should be used to make book recommendations when targeting groups with specific tendencies.

The purpose of this paper is to map the evidence provided on the review types, and explain the challenges faced by classification techniques in sentiment analysis (SA). The aim is to understand how traditional classification technique issues can be addressed through the adoption of improved methods.

A systematic review of literature was used to search published articles between 2002 and 2014 and identified 24 papers that discuss regular, comparative, and suggestive reviews and the related SA techniques. The authors formulated and applied specific inclusion and exclusion criteria in two distinct rounds to determine the most relevant studies for the research goal.

The review identified nine practices of review types, eight standard machine learning classification techniques and seven practices of concept learning Sentic computing techniques. This paper offers insights on promising concept-based approaches to SA, which leverage commonsense knowledge and linguistics for tasks such as polarity detection. The practical implications are also explained in this review.

The findings provide information for researchers and traders to consider in relation to a variety of techniques for SA such as Sentic computing and multiple opinion types such as suggestive opinions.

Previous literature review studies in the field of SA have used simple literature review to find the tasks and challenges in the field. In this study, a systematic literature review is conducted to find the more specific answers to the proposed research questions. This type of study has not been conducted in the field previously and so provides a novel contribution. Systematic reviews help to reduce implicit researcher bias. Through adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, systematic reviews effectively force researchers to search for studies beyond their own subject areas and networks.

This paper aims to report a study of researchers' preferences in selecting information from cited papers to include in a literature review, and the kinds of transformations and editing applied to the selected information.

This is a part of a larger project to develop an automatic summarization method that emulates human literature review writing behaviour. Research questions were: how are literature reviews written – where do authors select information from, what types of information do they select and how do they transform it? What is the relationship between styles of literature review (integrative and descriptive) and each of these variables (source sections, types of information and types of transformation)? The authors analysed the literature review sections of 20 articles from the Journal of the American Society for Information Science and Technology, 2001‐2008, to answer these questions. Referencing sentences were mapped to 279 source papers to determine the source sentences. The type of information selected, the sections of source papers where the information was taken from, and the types of editing changes made to include in the literature review were analyzed.

Integrative literature reviews contain more research result information and critique, and reference more information from the results and conclusion sections of the source papers. Descriptive literature reviews contain more research method information, and reference more information from the abstract and introduction sections. The most common kind of transformation is the high‐level summary, though descriptive literature reviews have more cut‐pasting, especially for information taken from the abstract. The types of editing – substitutions, insertions and deletions – applied to the source sentences are identified.

The results are useful in the teaching of literature review writing, and indicate ways for automatic summarization systems to emulate human literature review writing.

Though there have been several studies of abstracts and abstracting, there are few studies of literature reviews and literature review writing. Little is known about how writers select information from source papers, integrate it and present it in a literature review. This paper fills some of the gaps.

This paper presents a review of the state of the art on the application of blockchain and smart contracts to the peer-review process of scientific papers. The paper seeks to analyse how the main characteristics of the existing blockchain solutions in this field to detect opportunities for the improvement of future applications.

A systematic review of the literature on the subject was carried out in three databases recognized by the research community (IEEE Xplore, Scopus and Web of Science) and the Frontiers in Blockchain journal. A total of 1,967 articles were initially found, and after the exclusion process, the 26 remaining articles were classified according to the following dimensions: System Type, Open Access, Review Type, Reviewer Incentive, Token Economy, Blockchain Access, Blockchain Identification, Blockchain Used, Paper Storage, Anonymity and Maturity of the solution.

Results show that the solutions are normally concerned on offering incentives to the reviewers' work (often monetary). Other common general preferences among the solutions are the adoption of open reviews, the use of Ethereum, the implementation of publishing ecosystems and the use of InterPlanetary File System to the storage of the papers.

There are currently no studies covering the main aspects of blockchain solutions in the field of scientific peer review. The present study provides an overall review of the topic, summarizing important information on the current research and helping new adopters to develop solutions grounded on the existing literature.

This paper aims to review the literature on “E-fulfillment” with respect to marketing and operations issues in the current dynamic and complex e-tailing environment and thereby generate significant insights.

This paper is based on a systematic literature review on e-fulfillment focusing on marketing and operations issues therein. This systematic literature review consists of a critical review on e-fulfillment under planning (review question initialisation), searching (literature search), screening (literature evaluation), extraction and synthesis and reporting phases to conceptualise e-fulfillment. A total of 122 research articles have been reviewed to explore e-fulfillment and to develop key constructs and propositions.

This review provides the following three outcomes. First, the varied-fulfillment definitions have been critically reviewed, leading to synthesis, and thereby, an e-fulfillment definition is provided. Further, the variations for e-fulfillment across product types, which have been identified as a key variable for e-fulfillment, have been explored. Second, authors find five e-fulfillment components at the marketing and operations interface: website quality, customisation strategy, distribution strategy, last mile delivery and return management. Continuing with the e-fulfillment interface with marketing, the linkages between e-fulfillment and select post-purchase consumer behaviours measures across different product types have been reviewed. The paper thus with a focus on synthesising e-fulfillment literature from a process perspective emphasises the consumer behaviour metric for measuring e-fulfillment performance.

This study would help academicians, researchers, e-tailers and practitioners to understand e-fulfillment from a process perspective. For the researcher, it presents areas for future research by giving possible research directions in this emerging area. This study also brings out the impact of e-fulfillment according to product type on the post-purchase consumer behaviour measures, which will help e-tailers to link e-fulfillment to consumer behaviour metrics.

The paper classifies the fragmented literature to develop constructs and propositions for e-fulfillment. This is the first kind of study on e-fulfillment process and its impact on select post-purchase consumer behaviour measures across product types.

This research aims to investigate the role of brand types (value brand vs premium brand) in determining review content. Specifically, this research focuses on attribute-based reviews for an innovative product and suggests that consumers of value brands tend to discuss more attributes in their product reviews than those of premium brands. Also, this research suggests that review valence and time have moderating effects on the relationship between brand types and the number of attributes discussed in a review.

This research employed a data set of online consumer reviews (N = 106,980) for wireless earbuds from Amazon.com. It extracted products' attributes from review text using Bigram analysis and measured the number of attributes discussed in a review. It then analyzed the effect of brand types (value brand vs premium brand) on the number of attributes and the moderating effect of review valence and time.

The estimation results of Poisson models reveal that reviews for value brands tend to contain more product attributes than reviews for premium brands. Interestingly, the tendency is stronger among positive (vs negative) reviews, and it decreases over time as more reviews are accumulated.

While existing studies focused on the outcomes of the review content, there was not enough investigation into the determinants of the review content. This study focuses on the number of attributes discussed in a review, a key content characteristic, and provides the first empirical evidence that review content differs by brand types of an innovative product.

Within the context of providing an indexing service to the review literature in science and technology, reviews are defined and six types of review literature described. The sources of review literature are examined and an attempt made to estimate the current annual output of reviews in specialist review serials, primary journals, conference proceedings, books, reports and secondary services. The total output of ‘good’ quality reviews from these sources in 1972 is estimated to be 22,000. The characteristics of review literature are examined for the purposes of automatic identification; a method of identifying reviews by using the number of references was found to be between 75 and 85 per cent effective at the 40 references level.

Many jurisdictions fine illegal cartels using penalty guidelines that presume an arbitrary 10% overcharge. This article surveys more than 700 published economic studies and judicial decisions that contain 2,041 quantitative estimates of overcharges of hard-core cartels. The primary findings are: (1) the median average long-run overcharge for all types of cartels over all time periods is 23.0%; (2) the mean average is at least 49%; (3) overcharges reached their zenith in 1891–1945 and have trended downward ever since; (4) 6% of the cartel episodes are zero; (5) median overcharges of international-membership cartels are 38% higher than those of domestic cartels; (6) convicted cartels are on average 19% more effective at raising prices as unpunished cartels; (7) bid-rigging conduct displays 25% lower markups than price-fixing cartels; (8) contemporary cartels targeted by class actions have higher overcharges; and (9) when cartels operate at peak effectiveness, price changes are 60–80% higher than the whole episode. Historical penalty guidelines aimed at optimally deterring cartels are likely to be too low.