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The authors originally explore the factors for blockchain technology (BCT) adoption in agricultural supply chains (ASCs) to enhance circularity and understand the dependencies, hierarchical structure and causalities between these factors.

Based on an extant literature review and expert opinion, the present study identified ten enablers for adopting BCT to leverage the circular economy (CE) practices in the ASCs. Then, using an integrated interpretive structural modeling and decision-making trial and evaluation laboratory (ISM-DEMATEL) approach, hierarchical and cause–effect relationships are established.

It was observed that traceability is the most prominent enabler from the CE perspective in ASCs. However, traceability, being a net effect enabler, will be realized through the achievement of other cause enablers, such as seamless connectivity and information flow and decentralized and distributed ledger technology. The authors also propose a 12 Rs framework for enhancing circularity in ASC operations.

The paper identifies enablers to BCT adoption that will enhance circularity in ASC operations. The ISM hierarchical model is based on the driving and dependence powers of the enablers, and DEMATEL aids in identifying causal relationships among the enablers.

The study's findings and proposed 12 Rs framework may help the practitioners and policymakers devise effective BCT implementation strategies in ASCs, thereby empowering sustainability and circularity.

This study enriches the literature by identifying and modeling enablers for BCT adoption in ASCs. The study also proposes a new 12 Rs framework to help enhance ASC circularity.

This paper aims to systematically demonstrate a methodology to determine the relative and absolute encapsulation efficiencies (α_Re and α_Ab) for thermally- and chemically-robust inorganic pigments, typically like ZrSiO₄-based pigments, thereby enhancing their coloring performance.

The authors designed a route, surplus alkali-decomposition and subsequently strong-acid dissolution (SAD2) to completely decompose three classic zircon pigments (Pr–ZrSiO₄, Fe₂O₃@ZrSiO₄ and CdS@ZrSiO₄) into clear solutions and preferably used inductively coupled plasma-optical emission spectrometry (ICP-OES) to determine the concentrations of host elements and chromophores, thereby deriving the numeric data and interrelation of α_Re and α_Ab.

Zircon pigments can be thoroughly decomposed into some dissoluble zirconate–silicate resultants by SAD2 at a ratio of the fluxing agent to pigment over 6. ICP-OES is proved more suitable than some other quantification techniques in deriving the compositional concentrations, thereby the values of α_Re and α_Ab, and their transformation coefficient K_RA, which maintains stably within 0.8–0.9 in Fe₂O₃@ZrSiO₄ and CdS@ZrSiO₄ and is slightly reduced to 0.67–0.85 in Pr–ZrSiO₄.

The SAD2 method and encapsulation efficiencies are well applicable for both zircon pigments and the other pigmental or non-pigmental inhomogeneous systems in characterizing their accurate composition.

The authors herein first proposed strict definitions for the relative and absolute encapsulation efficiencies for inorganic pigments, developed a relatively stringent methodology to determine their accurate values and interrelation.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

This study aims to evaluate the impact of extrinsic, intrinsic and amotivation on an individual’s knowledge-sharing behavior.

The authors reviewed the literature on self-determination theory (SDT) as an applied predictor of knowledge-sharing behavior, and documented the extent to which SDT conceptual framework components have been studied in relation to predict knowledge sharing.

Building from SDT and its sub-theories, this study uncovers a gap in the knowledge-sharing literature as the continuum of the SDT framework has yet to fully be applied to knowledge-sharing behavior.

Contributing to the literature on knowledge management and knowledge sharing, this study is the first, to the best of the authors’ knowledge, of its kind to apply Deci and Ryan’s self-determination continuum, in its entirety, to a knowledge-sharing conceptual framework. The authors thereby address the potential impact of amotivation on an individual’s knowledge-sharing behavior.

This research conducts bibliometric analyses and network mapping on smart libraries worldwide. It examines publication profiles, identifies the most cited publications and preferred sources and considers the cooperation of the authors, organizations and countries worldwide. The research also highlights keyword trends and clusters and finds new developments and emerging trends from the co-cited references network.

A total of 264 records with 1,200 citations were extracted from the Web of Science database from 2003 to 2021. The trends in the smart library were analyzed and visualized using BibExcel, VOSviewer, Biblioshiny and CiteSpace.

The People’s Republic of China had the most publications (119), the most citations (374), the highest H-index (12) and the highest total link strength (TLS = 25). Wuhan University had the highest H-index (6). Chiu, Dickson K. W. (H-index = 4, TLS = 22) and Lo, Patrick (H-index = 4, TLS = 21) from the University of Hong Kong had the highest H-indices and were the most cooperative authors. Library Hi Tech was the most preferred journal. “Mobile library” was the most frequently used keyword. “Mobile context” was the largest cluster on the research front.

This study helps librarians, scientists and funders understand smart library trends.

There are several studies and solid background research on smart libraries. However, to the best of the author’s knowledge, this study is the first to conduct bibliometric analyses and network mapping on smart libraries around the globe.

Every year, millions of consumers around the world become victims of credit card fraud. These individuals have to appeal to their credit card companies to reverse unauthorized charges. This study aims to profile the American consumers’ experience when complaints to their credit card companies about unauthorized charges fail to produce a resolution. Using a large database of consumer complaint filings with the Consumer Financial Protection Bureau (CFPB), the characteristics of these consumer complaints are identified, and the drivers of consumer financial hardship resulting from credit card fraud are determined.

A random sample of consumer complaints about their credit card companies’ perceived mishandling of cases, filed with the CFPB, is used to conduct content analysis. The resulting content analysis categories are used in a predictive model to determine the drivers of consumer hardship.

In nearly one-quarter of all complaint filings, the credit card company had blamed the complainant as the party responsible for the fraudulent charges or refused to open a fraud investigation altogether. Nearly 60% of complaint reports contain expressions of emotional distress and many mention financial hardship. Nearly half of all complainants consider the fraud department operations of their credit card company as lacking in service quality, many reporting inability to reach the department or to receive a returned call. Even after CFPB intermediation, only 15% of complainants receive some form of financial relief from their credit card company. The majority of the complainants report a lack of willingness by the credit card company to reverse unauathorized charges, leaving the complainant financially responsible for them.

This study focused on data collected from consumers. Future research can expand the scope of inquiry by surveying the staff and executives in the fraud investigation departments of credit card companies to determine the norms of fraud investigation used within the industry.

This study sheds light on the financial hardship and emotional pains that consumers victimized by credit card fraud experience in dealing with their credit card companies.

To the best of the authors’ knowledge, this is the first study to empirically examine American consumers’ complaints about the fraud investigation operations of their credit card companies. Using data captured through the complaint filing system of a federal bureau (CFPB), the findings have implications for policymakers, regulators and credit card companies.

In the ever-evolving digital landscape, this study aims to explore which specific personality traits contribute to the innovativeness of startups, with a particular emphasis on understanding how technology adoption mediates this relationship. By doing so, the authors strive to unveil the nuanced dynamics of personality, technology adoption and startup innovativeness in the digital era.

The authors conduct a quantitative empirical analysis using a sample of 1,314 German startups. This study utilizes a mediation analysis to examine the effects of personality traits on the innovativeness of startups, taking technology adoption as a mediator into account.

The empirical results show certain personality traits have direct effects on innovativeness. Also, the results show that technology adoption is a driver of startup innovativeness. In addition, these traits are (partially) mediated by technology adoption.

The results shed new light on the interplay of entrepreneurs' personality and technology adoption in relation to startup innovativeness and therefore underline the importance of technology in this triangular relationship. The authors employ secondary data from startups in Germany, which complicates generalization of the results to other geographical and cultural contexts.

This study contributes to the scientific debate on the role of personality traits in entrepreneurship by providing empirical evidence on the mediating effect of technology adoption in the relationship between personality traits and startup innovativeness. The findings offer valuable insights for researchers, entrepreneurs and policymakers interested in understanding and promoting innovativeness in the context of startups.

This study aims to examine the complex effects of foreign direct investment (FDI) on China’s agricultural total factor productivity (TFP) from 2005 to 2020. It also explores the role of absorptive capacity as a moderating factor during this period.

Employing provincial panel data from China, this research measures agricultural TFP using the Stochastic Frontier Approach (SFA)-Malmquist method. The impact of FDI on agricultural productivity is further analyzed using a nondynamic panel threshold model.

The results highlight technological progress as the main driver of agricultural TFP growth in China. Agricultural FDI (AFDI) seems to impede TFP development, whereas nonagricultural FDI (NAFDI) shows a distinct positive spillover effect. The study reveals a threshold in absorptive capacity that affects both the direct and spillover impacts of FDI. Provinces with higher absorptive capacity are less negatively impacted by AFDI and more likely to benefit from FDI spillovers (FDISs).

This study provides new insights into the intricate relationship between FDI, absorptive capacity and agricultural productivity. It underscores the importance of optimizing technological progress and research and development (R&D) to enhance agricultural productivity in China.

This research uses a multifaceted approach to develop an MPA/MPP curriculum to support a data science track within the existing MPA/MPP programs by identifying the core and elective areas needed.

The approach includes (1) identifying a suitable structure for MPA/MPP programs which can allow the program to develop its capacity to train students with the data science and general public administration skills to solve public policy problems and leave explicit space for local experimentation and modification; (2) defining bridging modules and required modules for the MPA/MPP programs; and (3) developing of data science track thought to make suggestions for the inclusion of suitable data science modules into the data science track and benchmarking the data science modules suggested with the best practices developed by other professional bodies. The authors review 46 NASPAA-accredited MPA/MPP programs from 40 (or 22.7%) schools to identify the suitable required modules and some potential data science and analytics courses that MPA/MPP programs currently provide as electives.

The proposal includes a three-course (six–nine credits, not counted in the program but as prerequisites) bridging module, a nine-course (27 credits) required module and a five-course (15 credits) data science track/concentration.

This work can provide a starting point for the public administration education community to develop graduate programs focusing on data science to cater to the needs of both public managers and society at large.

To a large extent, knowledge in government workplaces resides within the hierarchy as it provides unique services to the public. Governments should preserve and pass on valuable and irreplaceable knowledge in providing their services through learning by sharing. Yet, sharing requires time and effort. This study examines the motivations of civil servants to share knowledge and their knowledge-sharing behaviors.

The authors conducted an online survey to collect quantitative data from civil servants in the Hong Kong Government and obtained 104 useable responses to test the eight hypotheses comprising six constructs by structural equation modeling to explore the participants' knowledge-sharing behaviors.

Results indicated that (1) expected contribution was the primary source of motivation behind knowledge sharing; (2) participants engaged more frequently in informal than formal knowledge-sharing activities; and (3) there was a dire need for knowledge sharing focusing on explicit knowledge of work procedures.

The research focuses on motivational factors for knowledge sharing. Despite abundant literature about knowledge sharing in public or governmental organizations, scant studies focus on the motivations behind why expected rewards and expected associations have less significant impacts on the attitude toward knowledge sharing than expected contribution.