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Family businesses have contributed significantly to economic growth in various countries, including Indonesia. The coronavirus disease 2019 (COVID-19) pandemic reduced the world economy and caused economic shocks in various business sectors. Women successors face significant challenges in overcoming family business problems during the COVID-19 pandemic, especially in terms of resilience, which is seen as the organization's prominent ability for sustainability in a turbulent environment. Thus, this study aims to explore women's initiatives and propose a framework for family business resilience during the COVID-19 pandemic in Indonesia.

This study uses a qualitative method with a case study approach to explore women's initiatives in family business resilience during the COVID-19 period. Data were collected through in-depth interviews with five women's successors of family businesses in Indonesia. Triangulation was used to test data validity. Meanwhile, data analysis uses the Miles–Huberman technique: data reduction, data display and conclusion drawing/verification.

This study found that women have the initiative to deal with the COVID-19 pandemic to develop and implement resilience in the family business. This study proposes a framework for factors that enhance family business resilience, including strategic decision-making (adaptive capacity, strategy renewal and appropriation capacity), strategic factors (successor motivation, successor competence and family support) and successor incremental program. Women have a long-term orientation toward the sustainability of their family businesses and can overcome various conflicts. The stereotypical view of women's leadership in family businesses has experienced a shift in which capability and competence are the main factors in recognizing women's leadership.

This study contributes to understanding women's roles in the resilience of family businesses under extreme stress during the pandemic. This study proposes a framework for family business resilience. In addition, this study adds new insights into the specific context of managing family business systems during the COVID-19 pandemic based on the resource-based view (RBV) and strategic management approach.

Throughout the 21st century, US households have experienced unprecedented levels of leverage. This dynamic has been exacerbated by income shortfalls during the COVID-19 crisis. Leveraging and deleveraging decisions affect household consumption. This study investigates the effect of the dynamics of household leverage and consumption on the stock market.

The authors explore the relation between household leverage and consumption in the context of the consumption capital asset pricing model (CCAPM). The authors test the model's implication that leverage has a negative risk premium by transforming the asset pricing restriction into an unconditional linear factor model and estimate the model using the general method of moments procedure. The authors run time-series regressions to estimate individual stocks' exposures to leverage, and cross-sectional regressions to investigate the leverage risk premium.

The authors show that shocks to household debt have strong and lasting effects on consumption growth. The authors extend the CCAPM to accommodate this effect and find, using various test assets, a negative risk premium associated with household deleveraging. Looking at individual stocks the authors show that the deleveraging risk premium is not explained by well-known risk factors.

This paper contributes to the literature on the role of leverage in economics and finance by establishing a relation between household leverage and spending decisions. The authors provide novel evidence that households' leveraging and deleveraging decisions can be a fundamental and influential force in determining asset prices. Further, this paper argues that household leverage might explain the small, persistent, and predictable component in consumption growth hypothesised in the long-run risk asset pricing literature.

The study aims to propose a conceptual Bhartiya (Indian) model of workplace spirituality (WPS) in non-profit organisations (NPOs) in the context of burnout and resilience by synthesising the concepts of the east and the west. The researchers have kept an open approach by exploring various dimensions of WPS by reviewing the extant literature of both the east and the west. The researchers delved into Bhartiya (Indian) scriptures to identify the concepts that have similarity with the dimensions of WPS so that it may further assist in facilitating those dimensions in NPOs. Furthermore, to propose a conceptual Bhartiya model for NPOs, the researchers synthesised the literature pool of Bhartiya studies on WPS. They examined how WPS decreases burnout and leads to resilience. The study’s findings reveal that concepts from Bhartiya scriptures such as Karm Yog (Nishkam Karm, self-abnegation, swadharm), parasparam bhavayantaha, loksangrah, daivi sampat and kritagyata are instrumental in facilitating the constructs of WPS. Meaningful work is facilitated through karm yog; sense of community is facilitated through parasparam bhavayantaha and loksangrah; and alignment with organisational values is facilitated through daivi sampat and kritagyata. The findings further suggest that WPS is an antidote to burnout and an enabler of resilience.

The authors of this study aim to investigate possible explanations of the prevalence of price clustering in the final offer prices of mergers and acquisitions (M&A).

The authors use final offer price in M&A deals to investigate the price clustering phenomena. The authors used regressions and logistic regressions to examine potential factors that might affect pricing strategy by looking into one-time acquirers and experienced serial acquirers.

Price clustering increases with negotiation uncertainties characterized as competitive bidding, number of bidders, challenged deals and duration. Moreover, the authors find persistent price clustering in experienced serial acquirers that are more experienced and better equipped with handling uncertainties, suggesting a preference of using round numbers regardless of levels of uncertainties. The authors' evidence shows that price clustering results from a combination of Harris' (1991) costly negotiation hypothesis where round prices may be used to lower search costs and psychological bias and preference.

The authors appear to be the first to investigate alternative theories that support M&A offer price clustering behavior, finding that both the costly negotiation and psychological bias and preference theories apply to M&A final price formation. Thus, the authors' major contribution, specific to the M&A process, is a clarification of physical and psychological factors associated with bidding and negotiation behavior. The authors are confident that the authors' study impacts conventional knowledge regarding M&A deal negotiation strategies, including bidding behavior, contract negotiation, financial analysis, management practices and risk management.

VINE Journal of Information and Knowledge Management Systems (VJIKMS) is one of the oldest and leading journals in the knowledge management (KM) field. The purpose of this study is to conduct bibliometrics analysis of publications published in VJIKMS for the past two decades. For doing this, this paper covered the past two decades of publications and carried out a science mapping analysis of publications.

The methodology included bibliometrics and the science mapping analysis process. This paper imported the bibliographic information of VJIKMS from the abstract and citation database Scopus. Through bibliometrics method, this paper examined the citation results, author productivity, authorship pattern, research collaboration and other parameters of the selected publications. Afterward, this paper used VOSviewer software to carry out the science mapping of bibliometric networks.

The findings showed that VJIKMS published 718 publications during 2000–2020, which got cited 4,311 times (6 times per article) till date. Joint authorship and international collaboration have been increasing where 1,417 authors from 66 countries have published. The USA, the UK and Iran lead the KM publications in this journal. Nonaka’s publications and Journal of Knowledge Management (JKM) are highly cited references and journals in the VJIKMS.

The findings of this study would help the KM students, researchers and practitioners to see the diffusion of KM globally, what are the promising areas to work and helps to know the various patterns of publications if they aim to publish in VJIKMS.

This is the first time a bibliometric analysis has been conducted to analysis of research publications published in VJIKMS. This presents a comprehensive analysis of publications between 2000 and 2020.

The curved and tribologically highly stressed surfaces of bearing components pose a major challenge for steel alloys or tribological resistant coatings like tetrahedral amorphous carbon (ta-C) coatings which in particular have an increased risk of delamination due to the significantly increased residual stresses. A possibility to prevent coating failure is the use of dopants while maintaining or even increasing tribological properties. This study aims to compare the tribological behavior of several doped diamond-like-carbon coatings with an undoped ta-C coating under varying slip conditions and Hertzian pressure up to 1800 MPa.

For this purpose, the tribological behavior was studied using of a ball-on-disc tribometer and a two-disc test rig under mixed/boundary conditions. The tests were conducted with coated specimens against uncoated 100Cr6 steel. Additionally, the influence of lubrication additives was studied due to the use of two fully formulated PAO-based oils, one without and one with molybdenum containing additives. The friction was measured in situ, and the wear was analyzed trough laser scanning microscopy and tactile measurement.

It was shown that the use of doped ta-C coatings exhibited a tendency for a more favorable tribological behavior compared to undoped ta-C coatings, with no general dependence on the lubricants used. The use of the most suitable coatings reduced the wear of the steel counter-body considerably.

To the best of the authors’ knowledge, this is the first approach of testing the tribological behavior of these doped ta-C coatings, developed for friction efficiency, in dependency on lubrication additives under the given load collective. The approach is relevant to determine whether the friction reduction and the wear inhibition of these coatings are suitable for higher contact pressures and load cycles.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2022-0336/

Social media texts as a data source in depression research have emerged as a significant convergence between Information Management and Public Health in recent years. This paper aims to sort out the depression-related study conducted on the text on social media, with particular attention to the research theme and methods.

The authors finally selected research articles published in Web of Science, Wiley, ACM Digital Library, EBSCO, IEEE Xplore and JMIR databases, covering 57 articles.

(1) According to the coding results, Depression Prediction and Linguistic Characteristics and Information Behavior are the two most popular themes. The theme of Patient Needs has progressed over the past few years. Still, there is a lesser focus on Stigma and Antidepressants. (2) Researchers prefer quantitative methods such as machine learning and statistical analysis to qualitative ones. (4) According to the analysis of the data collection platforms, more researchers used comprehensive social media sites like Reddit and Facebook than depression-specific communities like Sunforum and Alonelylife.

The authors recommend employing machine learning and statistical analysis to explore factors related to Stigmatization and Antidepressants thoroughly. Additionally, conducting mixed-methods studies incorporating data from diverse sources would be valuable. Such approaches would provide insights beneficial to policymakers and pharmaceutical companies seeking a comprehensive understanding of depression.

This article signifies a pioneering effort in systematically gathering and examining the themes and methodologies within the intersection of health-related texts on social media and depression.

Drawing on the social identity theory, this paper explores the impact of rider–driver ethnicity match on the driver’s expected ride satisfaction and willingness to perform, and rider’s trust on the driver.

The study relies on scenario-based online experiments with 291 ride-hailing drivers and 282 riders in the USA.

The findings indicate that ethnicity match between ride-hailing drivers and riders positively impact driver’s ride satisfaction and willingness to perform, and rider’s trust in the driver. The study also revealed a significant positive moderation effect of ethnic identity on the relationship of ethnicity match and those constructs.

While it may be challenging to influence an individual’s level of ethnic identity, managers can take steps to educate and train their employees regarding the impact of ethnic identity and discrimination, with a particular focus on those individuals who possess a strong sense of ethnic identity.

The findings of this research provide theoretical contributions to the existing literature on ride-hailing services and adds to the limited stream of logistics research that examines the impact of ethnicity on ride-hailing operations.

World imports of Italian sparkling wines fell by 9% in value and 5% in quantities. In view of this, the quality characterisation of these products is desirable to increase their market value and restore their global visibility.

For this purpose, in this paper, heavy metals (Cd, Co, Cr, Cu, Fe, Ga, Hf, Hg, Mn, Mo, Nb, Ni, Pb, Re, Sb, Sn, Ta, Th, Tl, U, W, V, Zn, Zr), rare Earth elements (REEs) (Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sm, Tb, Tm, Yb) and isotopes ratio (208Pb/206Pb, 207Pb/206Pb, 206Pb/204Pb, 208Pb/207Pb, 87Sr/86Sr) were analysed in Italian sparkling wines with Protected Designation of Origin (PDO) certification by High Resolution Inductively Coupled Plasma Mass Spectrometry (HR-ICP-MS) and MultiCollector Inductively Coupled Plasma Mass Spectrometer (MC-ICP-MS). The samples were produced in the Veneto region, and they were compared to white and red wines from the same area.

Sparkling wines present a characteristic elemental pattern compared to white and red ones, with lower content of heavy metals and higher content in REEs. The ratio 87Sr/86Sr resulted in a powerful micro-scale geographical origins marker while Pb ratios as winemaking process one, both useful to prevent possible frauds. Multivariate data analyses, such as PCA and PLS-DA, were used to develop a model of recognition of Venetian sparkling wines.

The good classification of sparkling wines was achieved (95%), proving the suitable use of these analytes as markers for recognising sparkling wines and their geographical origin verification. To the best of the authors’ knowledge, this is the first study investigating heavy metals, REEs and isotopes in Venetian sparkling wine for their recognition.

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.