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Digital libraries and social media are two sources of online information with different characteristics. The purpose of this paper is to integrate self-efficacy into the analysis of the relationship between information sources and decision making, and to explore the effect of self-efficacy on decision making, as well as the interacting effect of self-efficacy and information sources on decision making.

Survey data were collected and the partial least squares structural equation modeling was employed to verify the research model.

The effect of digital library usage for acquiring information on perceived decision quality (PDQ) is larger than that of social media usage for acquiring information on PDQ. Self-efficacy in acquiring information (SEAI) stands out as the key determinant for PDQ. The effect of social media usage for acquiring information on PDQ is positively moderated by SEAI.

Decision making is a fundamental activity for individuals, but human decision making is often subject to biases. The findings of this study provide useful insights into decision quality improvement, highlighting the importance of SEAI in the face of information overload.

This study integrates self-efficacy into the analysis of the relationship between information sources and decision making, presenting a new perspective for decision-making research and practice alike.

With the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers’ economic models to help drive online sales. Initially, the purpose of this paper is to undertake an investigation of apparel recommendations in the commercial market in order to verify the research value and significance. Then, this paper reviews apparel recommendation techniques and systems through academic research, aiming to acquaint apparel recommendation context, summarize the pros and cons of various research methods, identify research gaps and eventually propose new research solutions to benefit apparel retailing market.

This study utilizes empirical research drawing on 130 academic publications indexed from online databases. The authors introduce a three-layer descriptor for searching articles, and analyse retrieval results via distribution graphics of years, publications and keywords.

This study classified high-tech integrated apparel systems into 3D CAD systems, personalised design systems and recommendation systems. The authors’ research interest is focussed on recommendation system. Four types of models were found, namely clothes searching/retrieval, wardrobe recommendation, fashion coordination and intelligent recommendation systems. The forth type, smart systems, has raised more awareness in apparel research as it is equipped with advanced functions and application scenarios to satisfy customers. Despite various computational algorithms tested in system modelling, existing research is lacking in terms of apparel and users profiles research. Thus, from the review, the authors have identified and proposed a more complete set of key features for describing both apparel and users profiles in a recommendation system.

Based on previous studies, this is the first review paper on this topic in this subject field. The summarised work and the proposed new research will inspire future researchers with various knowledge backgrounds, especially, from a design perspective.

The big challenge in apparel recommendation system research is not the exploration of machine learning technologies in fashion, but to really understand clothes, fashion and people, and know what to learn. The purpose of this paper is to explore an advanced apparel style learning and recommendation system that can recognise deep design-associated features of clothes and learn the connotative meanings conveyed by these features relating to style and the body so that it can make recommendations as a skilled human expert.

This study first proposes a type of new clothes style training data. Second, it designs three intelligent apparel-learning models based on newly proposed training data including ATTRIBUTE, MEANING and the raw image data, and compares the models’ performances in order to identify the best learning model. For deep learning, two models are introduced to train the prediction model, one is a convolutional neural network joint with the baseline classifier support vector machine and the other is with a newly proposed classifier later kernel fusion.

The results show that the most accurate model (with average prediction rate of 88.1 per cent) is the third model that is designed with two steps, one is to predict apparel ATTRIBUTEs through the apparel images, and the other is to further predict apparel MEANINGs based on predicted ATTRIBUTEs. The results indicate that adding the proposed ATTRIBUTE data that captures the deep features of clothes design does improve the model performances (e.g. from 73.5 per cent, Model B to 86 per cent, Model C), and the new concept of apparel recommendation based on style meanings is technically applicable.

The apparel data and the design of three training models are originally introduced in this study. The proposed methodology can evaluate the pros and cons of different clothes feature extraction approaches through either images or design attributes and balance different machine learning technologies between the latest CNN and traditional SVM.

The chapter is designed to discuss the preconditions for the competitiveness of the furniture industry, global networks and regional perspectives, as well as the competitive advantages of different regions such as the USA, Europe and the East. The challenges created by customisation and the needs of consumers for individual products are also discussed. As consumers become more and more focussed on furniture designed exclusively for them, the furniture business has to reorient its production and has to deal with a number of management issues. It is necessary to reconsider not only how to involve consumers but also how to keep prices competitive because even for an individual order, the customer is less and less willing to pay more. The issue of new product development is also discussed. It delves into the management of furniture companies, the characteristic organisational structures, and management models that could ensure the sustainability of the business. Particular attention is paid to the digital issues of furniture manufacturing and enterprise resource planning (ERP) in particular. An examination of how the furniture sector evaluates prices and costs, which are the most popular methods and which can be used for forecasting, looks at the most important global trends. Such cost estimation methods as cost-based, competition-based, analogous-based, and expert-based are discussed, highlighting the limits of their applications. Then discusses current trends and the current IT supply, which unfortunately does not fully meet the needs of customised furniture production, and digitisation within a small company becomes more difficult. So, companies have to recognise the limits of digitisation.

The aim of this paper is to address a local separability character partly identified by non‐farm participation behaviors in the context of multiple market imperfections.

The paper develops a model to analyze agricultural household's non‐farm participation based on heterogeneous asset endowments. The model is applied to recent data from Zhouzhi, a mountainous county in rural western China.

The paper shows that human capital, social capital and other capital assets have significant but different effects on the agricultural household's participation in non‐farm activities, and they help to break down non‐farm labor constraints. Nonseparability holds only for those households unable to participate in non‐farm activities due to poor asset endowments.

The agricultural household model developed in this paper and its application in China provide insights into theory and empirical analysis of agricultural households' behavior and rural development.

Collaborative filtering based recommender systems (CF–RS) are widely used to recommend products based on consumers' preference similarity. Recommendations by CF–RS merely provide suggestions as “people who bought this also bought this” while, consumers are unaware about the source of these recommendations. By amalgamating CF–RS with consumers' social network information, e-commerce sites can offer recommendation from social networks of consumers. These social network embedded systems are known as social recommender systems (SRS). The extant literature has researched on the algorithms and implementation of these systems; however, SRS have not been understood from consumers' psychological perspective. This study aims to qualitatively explore consumers' motives to accept SRS in e-commerce websites.

This qualitative study is based on in-depth interviews of frequent online shoppers. SRS are currently not very widespread in the Indian e-commerce space; hence, a vignette was shown to respondents before they responded to the questions. Inductive qualitative content analysis method was used to analyse these interviews.

Three main themes (social-gratification, self-gratification and information-gratification) emerged from the analysis. Out of these, social-gratification acts as an enabler, while self-gratification along with some elements of information-gratification act as inhibitors towards acceptance of social recommendations. Based on these gratifications, we present a conceptual model on consumer's acceptance of social recommendations.

This study is an initial attempt to qualitatively understand consumers' attitudes and acceptance of social recommendations on e-commerce websites, which in itself is a fairly new phenomenon.

Pharmaceutical industry involves highly specialized business processes where strong research and development focus along with market differentiation and localization are the deciders of success. This has led to evolution of segments and complexities in supply chain. This paper aims to focus on segmental differences in supply chain performance of Indian Pharmaceutical firms.

This paper measures supply chain performance of select segmental players of the pharmaceutical industry using financial metrics and supply chain operations reference (SCOR) key performance indicators through a five-year timeline. The best performance results are compared across the segments to identify unique performance features, if any. The sample results are validated through hypothesis testing methodology.

This paper has evidenced that the innovators segment is performing better in cash-to-cash cycle time and supply chain working capital productivity, whereas generics segment is doing better in distribution cost efficiency and total cost to serve aspects.

The paper is based on historical financial data of firms and measures the firm focused supply chain performance. The results may not be generalized in a global context but serve as a motivator for other researchers to take similar studies. The paper may further be analyzed with primary data of the firms to understand the segmental difference in customer focus supply chain performance measures.

This paper has brought out important segmental supply chain performance features of the Indian pharmaceutical firms and identified segment-specific problems by integrating SCOR KPIs and financial metrics.

This paper has integrated both SCOR KPIs and financial metrics to provide unique insights on segmental differences in the performance behavior of pharmaceutical supply chain.

This study aims to examine the scientific publications related to leadership research in hospitality from 2000 to 2021 by conducting a systematic review (qualitative) and to discuss implications for future research.

For the qualitative approach, the authors conduct an in-depth critique of major leadership theories using 167 articles indexed in the Web of Science Core Collection.

The findings show that transformational leadership, leader–member exchange and servant leadership are the most prominent leadership topics studied from 2000 to 2021, followed by abusive supervision, empowering leadership, ethical leadership and authentic leadership. A framework is presented highlighting the mediators, moderators, outcomes, sample and research designs used in each of these lines of leadership research. Moreover, 16 areas for further research are identified and discussed.

This review uncovers scholars’ general lack of regard for how the study of leadership might benefit from examining hospitality as a special and challenging context for leadership and business performance.

This study reviews and critically analyzes leadership research in hospitality using qualitative methods. Therefore, the authors believe this review is of great value to academics and practitioners because it synthesizes and analyzes the field and identifies important research opportunities.

Digital product and service innovations (DPSI) has attracted widespread attention from both management scholars and practitioners. Previous studies have documented that information technology (IT) capability and digital orientation positively influence DPSI performance. However, the question of whether and how digitalization capability can facilitate DPSI performance remains unresolved. This paper fills these gaps by investigating the mediating role of improvisation capability and the moderating role of technological turbulence.

This study used two-wave data from 240 matched digital transformation department leaders and senior managers from Chinese firms and examined the hypotheses deploying hierarchical regression and bootstrapping.

Our analyses reveal positive, significant links between digitalization capability and improvisation capability and between improvisation capability and DPSI performance. The findings further show that the effect of digitalization capability on DPSI performance is partially mediated by improvisation capability and that technological turbulence strengthens the indirect relationship between digitalization capability and DPSI performance through improvisation capability.

Integrating resource-based view, this research provides evidence that the extent to which improvisation capability mediates the relationship between digitalization capability and DPSI performance depends on technological turbulence. It provides a new direction for digitalization capability and DPSI performance.

The overall goal of this chapter is twofold. First, the authors aim to identify indigenous phenomena that influence employee turnover and retention in the Chinese context. Second, the authors link these phenomena to the contextualization of job embeddedness theory. To achieve the goal, the authors begin by introducing three macro-level forces (i.e., political, economic, and cultural forces) in China that help scholars analyze contextual issues in turnover studies. The authors then provide findings in the literature research on employee retention studies published in Chinese academic journals. Next, the authors discuss six indigenous phenomena (i.e., hukou, community in China, migrant workers, state-owned companies, family benefit prioritization, and guanxi) under the three macro-level forces and offer exploratory propositions illustrating how these phenomena contribute to understanding employee retention in China. Finally, the authors offer suggestions on how contextualized turnover studies shall be conducted in China.