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Purpose This study aims to examine the impact of corporate attributes on integrated reporting quality of top 100 listed firms in South Africa. Design/methodology/approach With a sample of the top 100 listed firms in South Africa, this paper drew insights from the legitimacy and stakeholder theory to examine the impact of corporate attributes on integrated reporting quality. This paper measured integrated reporting quality based on the International Integrated Reporting Council framework of 2013. Corporate attributes were determined taking into consideration three broad perspectives (board committee attributes, firm attributes and audit committee attributes). This paper analyzed the data using content analysis, ordered probit regression and logistic regression method. Findings Results indicate that board committee attributes, firm attributes and audit committee attributes have a positive and significant relationship with integrated reporting quality. Additional analysis reveals that external assurance contributes to the quality of integrated reporting. The findings empirically revealed that most South African firms have intensified efforts toward the quality and full disclosure of integrated reporting framework. Research limitations/implications The study was limited to a sample size of 100 firms, which is country-specific, however, it sets the tone for future empirical research on the subject matter. This study provides an avenue for future research in the area of corporate attributes and integrated reporting quality in other emerging countries, especially other African countries. Practical implications The result of this study provides practical implications in the areas of good corporate governance, corporate reporting and integrated reporting. The empirical approach used in this study emphasizes the need for corporate organizations to introduce integrated reporting practices into their reporting cycle. The finding implies that non-compliance with integrated reporting by corporate organizations may have an adverse effect on corporate growth, corporate sustainability and corporate reputation in the long run. Originality/value The work extends prior research on the subject of integrated reporting in South Africa. Also, this study broadens the application of legitimacy and stakeholder theory in influencing corporate organizations to disclose relevant information that could aids stakeholders’ interest.

Analogy-based software effort estimation (ASEE) plays an important role in software development. It attracts the attention of researchers nowadays due to the simplicity of the ASEE reasoning method. ASEE reasoning is considered simple because it is similar to human reasoning. The estimation approach repeatedly uses the effort values of preceding similar projects. In this approach, the appropriate number of similar previous projects to be reused is still a topic of debate in ASEE research studies. The reliability and accuracy of ASEE methods are considerably affected by the quality of software repositories (datasets). Therefore, if a software dataset does not follow the ASEE principle, then it is not considered useful for the ASEE method. This article presents a novel approach for ASEE to find the appropriate number of analogues from quality datasets. In this approach, the data pre-processing stage is based on Spearman’s rank-order correlation and Kruskal–Wallis test. In the proposed approach, it can deal with categorical (both nominal and ordinal) attributes individually. Spearman’s rank-order correlation is used to find reliable numerical and ordinal attributes. Kruskal–Wallis test identifies reliable nominal attributes. Reliable attributes refer to those attributes which significantly influence the effort. The experimental results show that the proposed approach enhances the quality of the dataset, attribute selection from the metadata, and reduces the abnormal observation and overall project development cost.

Research suggests that unit-level ambidexterity positively impacts subsequent unit performance but theory and testing on this impact remain impoverished. We develop a cross-level model suggesting that structural and resource attributes of the organizational context significantly shape the relationship between unit ambidexterity and performance. Using multisource and lagged data from 285 organizational units located within 88 autonomous branches, results from hierarchical linear modeling show that this relationship is boosted when the organization is decentralized, more resource munificent, or less resource interdependent. We also find that structural differentiation of the organization does not condition the unit ambidexterity-performance relationship. Through this cross-level theory and testing, we develop a richer explanation of the effectiveness of ambidextrous units operating in multiunit contexts.

Facial attribute analysis has received considerable attention when deep learning techniques made remarkable breakthroughs in this field over the past few years. Deep learning based facial attribute analysis consists of two basic sub-issues: facial attribute estimation (FAE), which recognizes whether facial attributes are present in given images, and facial attribute manipulation (FAM), which synthesizes or removes desired facial attributes. In this paper, we provide a comprehensive survey of deep facial attribute analysis from the perspectives of both estimation and manipulation. First, we summarize a general pipeline that deep facial attribute analysis follows, which comprises two stages: data preprocessing and model construction. Additionally, we introduce the underlying theories of this two-stage pipeline for both FAE and FAM. Second, the datasets and performance metrics commonly used in facial attribute analysis are presented. Third, we create a taxonomy of state-of-the-art methods and review deep FAE and FAM algorithms in detail. Furthermore, several additional facial attribute related issues are introduced, as well as relevant real-world applications. Finally, we discuss possible challenges and promising future research directions.

We investigate the moderating effect of product attributes and review ratings on views, conversion|views (conversion conditional on views), and final conversion of a purchase-based collaborative filtering recommender system on an e-commerce site. We run a randomized field experiment on a top retailer with 184,375 users split into a recommender-treated group and a control group. We tag theory-driven attributes of 37,125 unique products via Amazon Mechanical Turk to augment the usual product data (e.g., review ratings, descriptions). By examining the recommender’s impact through different stages—awareness (views), salience ( conversion|views ), and final conversion—and across product types, we provide nuanced insights. The study confirms that the recommender increases views, conversion|views , and final conversion rates by 15.3%, 21.6%, and 7.5%, respectively, but this lift is moderated by product attributes and review ratings. We find that the lift on product views is greater for utilitarian products compared with hedonic products as well as for experience products compared with search products. In contrast, the lift on conversion|views rate is greater for hedonic products compared with utilitarian products. Furthermore, the lift on views rate is greater for products with higher average review ratings, which suggests that a recommender acts as a complement to review ratings, whereas the opposite is true for conversion|views , where recommender and review ratings are substitutes. Additionally, a recommender’s awareness lift is greater than its saliency impact. We discuss the potential mechanisms behind our results as well as their managerial implications. This paper was accepted by David Simchi-Levi, information systems .

Rating norms for semantic attributes (e.g., concreteness, familiarity, valence) are widely used to study the content that people process as they encode meaningful material. Intensity ratings of individual attributes have been manipulated in numerous experiments with a range of memory paradigms, but those manipulations are contaminated by substantial correlations with the intensity ratings of other attributes. A method of controlling such contamination is needed, which requires a determination of how many distinct attributes there are among the large collection of attributes for which published norms are available. Identification of overlapping words in multiple rating projects yielded a data base containing normed values for each word’s perceived intensity ( M rating) and ambiguity (rating SD ) on 20 different attributes. Principal component analyses then revealed that the intensity space was spanned by just three latent semantic attributes, and the ambiguity space was spanned by five. Psychologically, the big three intensity factors (emotional valence, size, age) were highly interpretable, as were the big five ambiguity factors (discrete emotion, emotional valence, age, meaningfulness, and verbatim memory). We provide a data base of intensity and ambiguity factor scores that can be used to conduct uncontaminated studies of the memory effects of the intensity and ambiguity of latent semantic attributes.

PurposeThe emergence of social media has brought the influencer marketing landscape to an unprecedented level, where many ordinary people are turning into social media influencers. The study aims to construct and validate a model to yield strategic insights on the relevance of content curation, influencer–fans interaction and parasocial relationships development in fostering favorable endorsement outcomes (i.e. purchase intention).Design/methodology/approachThe present study analyzes data from a survey of 411 consumers using partial least squares-structural equation modelling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA) to understand the net and combined effects of content attributes, interaction strategies and parasocial relationships on purchase intention.FindingsPLS-SEM results reveal that content attributes (i.e. prestige and expertise) and interaction strategies (i.e. interactivity and self-disclosure) positively influence parasocial relationships, and in turn, lead to high purchase intention. Findings from fsQCA indicate six solutions with different combinations of content attributes, interaction strategies and parasocial relationships that sufficiently explain high purchase intention.Originality/valueThe present study demonstrates the roles of content attributes and interaction strategies in engendering parasocial relationship and the endorsement outcome (i.e. purchase intention) from both linear and non-linear (complexity) perspectives.

Building on social commerce (s-commerce) perspectives and the trust transfer theory, this study develops a theoretical model that explains the indirect effects of two types of s-commerce attributes (community and platform) on behavioral outcomes (s-commerce intentions and e-Word-of-Mouth (e-WOM) intentions) through trust in community and platform. We analyze data collected from s-commerce users on travel booking websites using structural equation modeling technique. Results confirm that s-commerce intentions and e-WOM intentions are contingent upon s-commerce community and platform attributes. Moreover, the results provide evidence for the mediating effects of trust in community and platform on the relationship between s-commerce attributes and behavioral outcomes. The study provides further insights about the impact of s-commerce experience on s-commerce intention and e-WOM intention. Moreover, this study contributes to s-commerce research and practice by developing and validating the role of s-commerce community and platform attributes in forming consumers’ s-commerce behavioral outcomes.

Engineering stress or strain into materials can improve their performance. Adding mechanical stress to silicon chips, for instance, produces transistors with enhanced electron mobility. Ghadimi et al. explore the possibility of enhancing the vibrational properties of a micromechanical oscillator by engineering stress within the structure (see the Perspective by Eichler). By careful design of the micromechanical oscillator, and by building in associated stresses, exceptional vibrational properties can be produced. Such enhanced oscillators could be used as exquisite force sensors. Science , this issue p. 764 ; see also p. 706 Engineered stress is used to fabricate micromechanical oscillators with enhanced vibrational properties. Extreme stresses can be produced in nanoscale structures; this feature has been used to realize enhanced materials properties, such as the high mobility of silicon in modern transistors. We show how nanoscale stress can be used to realize exceptionally low mechanical dissipation when combined with “soft-clamping”—a form of phononic engineering. Specifically, using a nonuniform phononic crystal pattern, we colocalize the strain and flexural motion of a free-standing silicon nitride nanobeam. Ringdown measurements at room temperature reveal string-like vibrational modes with quality ( Q ) factors as high as 800 million and Q × frequency exceeding 10 15 hertz. These results illustrate a promising route for engineering ultracoherent nanomechanical devices.