bluuebunny/crossref_metadata_2025_split huggingface dataset:
Supply chain management in the era of circular economy: the moderating effect of big data
Authors: Del Giudice Manlio , Chierici Roberto , Mazzucchelli Alice , Fiano Fabio 
Published: September 2020

PurposeThis paper analyzes the effect of circular economy practices on firm performance for a circular supply chain and explores the moderating role that big-data-driven supply chain plays within these relationships.Design/methodology/approachThis study uses data collected through an online survey distributed to managers of 378 Italian firms that have adopted circular economy principles. The data are processed using multiple regression analysis.FindingsThe results indicate that the three categories of circular economy practices investigated – namely circular economy supply chain management design, circular economy supply chain relationship management and circular economy HR management – play a crucial role in enhancing firm performance from a circular economy perspective. A big-data-driven supply chain acts as a moderator of the relationship between circular economy HR management and firm performance for a circular economy supply chain.Originality/valueThis study makes a number of original contributions to research on circular economy practices in a big-data-driven supply chain and provides useful insights for practitioners. First, it answers the call to capture digital transformation trends and to extend research on sustainability in supply chain management. Second, it enhances the literature by investigating the relationships between three different kinds of circular economy supply chain practices and firm performance. Finally, it clarifies the moderating role of big data in making decisions and implementing circular supply chain solutions to achieve better environmental, social and economic benefits.
The effect of green HRM on employee green behaviors in higher education: the mediating mechanism of green work engagement
Authors: Aboramadan Mohammed 
Published: September 2020

PurposeBuilding on the social exchange theory (SET), this study aims to propose a model of the effects of green human resource management on employee in-role, extra-role and green innovative work behavior (GIWB). This study proposes, building on both the job demands-resources model and SET, that the aforementioned links can be explained through the mediating role of green work engagement (GWE).Design/methodology/approachData were collected from employees (n. 208) working in Palestinian higher education organizations using a self-administered questionnaire. The partial least squares-structural equation modeling was the primary statistical technique adopted to examine the study’s hypotheses.FindingsThe results suggest that green human resources management (GHRM) was a significant predictor of employee in-role green behavior, extra-role green behavior and GIWB. Furthermore, GWE demonstrated to be a significant intervening mechanism to explain the above-mentioned relationships.Practical implicationsThe results provide useful insights for higher education policymakers on how GHRM may positively contribute to employee green outcomes.Originality/valueThis paper is novel for several reasons. First, it contributes to the general literature of GHRM. Second, it contributes to the limited body of knowledge on GHRM in the context of higher education. Third, the distinct contribution of this study is the introduction of GIWB as an outcome of GHRM, and GWE as a mediating variable in the relationship between GHRM and employee green behaviors.
Development of a soft cable-driven hand exoskeleton for assisted rehabilitation training
Authors: Xu Dawen , Wu Qingcong , Zhu Yanghui 
Published: September 2020

Purpose
Hand motor dysfunction has seriously reduced people’s quality of life. The purpose of this paper is to solve this problem; different soft exoskeleton robots have been developed because of their good application prospects in assistance. In this paper, a new soft hand exoskeleton is designed to help people conduct rehabilitation training.

Design/methodology/approach
The proposed soft exoskeleton is an under-actuated cable-driven mechanism, which optimizes the force transmission path and many local structures. Specifically, the path of force transmission is optimized and cables are wound around cam-shaped spools to prevent cables lose during fingers movement. Besides, a pre-tightening system is presented to adjust the preload force of the cable-tube. Moreover, a passive brake mechanism is proposed to prevent the cables from falling off the spools when the remote side is relaxed.

Findings
Finally, three control strategies are proposed to assist in rehabilitation training. Results show that the average correlation coefficient of trajectory tracking is 90.99% and this exoskeleton could provide steady clamping force up to 35 N, which could meet the demands of activities in daily living. Surface electromyography (sEMG)-based intention recognition method is presented to complete assistance and experiments are conducted to prove the effectiveness of the assisted grasping method by monitoring muscle activation, finger angle and interactive force.

Research limitations/implications
However, the system should be further optimized in terms of hardware and control to reduce delays. In addition, more clinical trials should be conducted to evaluate the effect of the proposed rehabilitation strategies.

Social implications
May improve the ability of hemiplegic patients to live independently.

Originality/value
A novel under-actuated soft hand exoskeleton structure is proposed, and an sEMG-based auxiliary grasping control strategy is presented to help hemiplegic patients conduct rehabilitation training.
Mixing data analytics with intuition: Liverpool Football Club scores with integrated intelligence
Authors: Lichtenthaler Ulrich 
Published: September 2020

Purpose
The paper aims to present key insights for achieving a sustainable competitive advantage from implementing data analytics solutions and artificial intelligence (AI).

Design/methodology/approach
This conceptual paper builds on prior empirical and conceptual research, and it offers anecdotal evidence from a best-practice case study of Liverpool Football Club.

Findings
Beyond the well-known companies in profiting from AI, such as Amazon, this paper presents a surprising best-practice example for achieving a competitive advantage from combining data analytics and AI with human expertise. Liverpool Football Club is a leading soccer club from England, and it has experienced a major revival in terms of sporting success and financial performance in recent years. The club’s emphasis on data analytics and AI only contributed to this impressive evolution when it was complemented with the emotional intelligence and people skills of its present manager Juergen Klopp. Along with new conceptual arguments, this example of integrated intelligence offers important insights for managers and executives in companies from many industries – far beyond sports management.

Originality/value
The conceptual arguments and case example illustrate that the competitive benefits of implementing standardized AI solutions in an isolated way will often be relatively limited. Many AI solutions will be standardized in the near future, and they may easily be applied by many firms. Thus, even those companies that are considered as AI pioneers may not be able to sustain their competitive advantage unless they develop an integrated intelligence architecture, which combines human and AI. This integration of data analytics and AI with the human intelligence and expertise of a firm’s employees offers the basis for a sustainable advantage because it is difficult to match for competitors. Thus, the paper offers new theoretical insights and direct managerial implications with regard to profiting from AI and data analytics.
Automatic energy demand assessment in low-carbon investments: a neural network approach for building portfolios
Authors: Gabrielli Laura , Ruggeri Aurora Greta , Scarpa Massimiliano 
Published: September 2020

Purpose
This paper aims to develop a forecasting tool for the automatic assessment of both environmental and economic benefits resulting from low-carbon investments in the real estate sector, especially when applied in large building stocks. A set of four artificial neural networks (NNs) is created to provide a fast and reliable estimate of the energy consumption in buildings due to heating, hot water, cooling and electricity, depending on some specific buildings’ characteristics, such as geometry, orientation, climate or technologies.

Design/methodology/approach
The assessment of the building’s energy demand is performed comparing the as-is status (pre-retrofit) against the design option (post-retrofit). The authors associate with the retrofit investment the energy saved per year, and the net monetary saving obtained over the whole cost after a predetermined timeframe. The authors used a NN approach, which is able to forecast the buildings’ energy demand due to heating, hot water, cooling and electricity, both in the as-is and in the design stages. The design stage is the result of a multiple attribute optimization process.

Findings
The approach here developed offers the opportunity to manage energy retrofit interventions on wide property portfolios, where it is necessary to handle simultaneously a large number of buildings without it being technically feasible to achieve a very detailed level of analysis for every property of a large portfolio.

Originality/value
Among the major accomplishments of this research, there is the creation of a methodology that is not excessively data demanding: the collection of data for building energy simulations is, in fact, extremely time-consuming and expensive, and this NN model may help in overcoming this problem. Another important result achieved in this study is the flexibility of the model developed. The case study the authors analysed was referred to one specific stock, but the results obtained have a more widespread importance because it ends up being only a matter of input-data entering, while the model is perfectly exportable in other contexts.
A multi-objective optimization model for sustainable supply chain network with using genetic algorithm
Authors: Ehtesham Rasi Reza , Sohanian Mehdi 
Published: September 2020

Purpose
The purpose of this paper is to design and optimize economic and environmental dimensions in a sustainable supply chain (SSC) network. This paper developed a mixed-integer linear programing (MILP) model to incorporate economical and environmental data for multi-objective optimization of the SSC network.

Design/methodology/approach
The overall objective of the present study is to use high-quality raw materials, at the same time the lowest amount of pollution emission and the highest profitability is achieved. The model in the problem is solved using two algorithms, namely, multi-objective genetic and multi-objective particle swarm. In this research, to integrate sustainable supplier selection and optimization of sustainability performance indicators in supply chain network design considering minimization of cost and time and maximization of sustainability indexes of the system.

Findings
The differences found between the genetic algorithms (GAs) and the MILP approaches can be explained by handling the constraints and their various logics. The solutions are contrasted with the original crisp model based on either MILP or GA, offering more robustness to the proposed approach.

Practical implications
The model is applied to Mega Motor company to optimize the sustainability performance of the supply chain i.e. economic (cost), social (time) and environmental (pollution of raw material). The research method has two approaches, namely, applied and mathematical modeling.

Originality/value
There is limited research designing and optimizing the SSC network. This study is among the first to integrate sustainable supplier selection and optimization of sustainability performance indicators in supply chain network design considering minimization of cost and time and maximization of sustainability indexes of the system.
Corporate social responsibility image and emotions for the competitiveness of tourism destinations
Authors: Rodrigues Paula , Borges Ana Pinto , Vieira Elvira Pacheco 
Published: September 2020

Purpose
The purpose of this paper is to evaluate the perception of tourists in relation to corporate social responsibility (CSR) practices in the city of Porto as a tourism destination in three main domains as follows: community, environment and customer’s perception on the effect of the creation of corporate social responsibility image (CSRI) and if this produces a direct effect in brand love (BL). At the same time, it evaluates the effect of tourist engagement in the BL destination.

Design/methodology/approach
A sample of 958 complete answers that were collected through a questionnaire, which was administered to tourists in the city of Porto is used. The exploratory factor analysis, the confirmatory factor analysis and the structural equation modelling are appliedto analyze the data.

Findings
Empirical results indicate that the city of Porto should invest to increase the CSR activities to develop a positive CSRI in the tourist’s perceptions, thus reinforcing their emotions and engagement. The feelings of BL and brand engagement (BE) are sensations characterized by a strong character that can increase the competitiveness of a tourism destination with favourable and unique associations.

Practical implications
This is the first time that this evaluation has been carried out for the city of Porto, providing useful information to design the destination’s brand identity so as to differentiate it from other competing destinations.

Originality/value
This research aims to fulfil the aforesaid gaps related to the effect of the three dimensions of CSR in the consumer perceptions of CSRI applied to tourism destinations and use the BE and the consumer perception of CSRI to establish a positive relationship, as a BL, with a city destination.
Architectural identity and place attachment in historic neighbourhoods: an empirical study in Sanandaj, Iran
Authors: Farhad Sharareh , Maghsoodi Tilaki Mohammad Javad , Hedayati Marzbali Massoomeh 
Published: September 2020

Purpose
Environmental and functional situations of neighbourhoods can play a significant role in maintaining neighbourhoods’ socio-economic activities and residents’ place attachment. This study aims to examine the relationship between architectural identity components (i.e. symbol, ornament and old pattern) and place attachment in Aghazaman neighbourhood as a well-known historic neighbourhood in Sanandaj, Iran.

Design/methodology/approach
The study is quantitative in nature and a questionnaire survey was conducted on a sample of 300 respondents to assess the relationships between the architectural identity components and place attachment.

Findings
The findings indicate a significant and positive relationship between architectural identity and place attachment, indicating that higher architectural identity is associated with high perceptions of place attachment amongst residents. The results further indicate that the most influencing factor in shaping architectural identity is old pattern, followed by ornaments and symbols.

Social implications
As place attachment is a strong incentive to maintain social interactions in neighbourhoods and to create a sense of attachment towards the residential environment, attention must be paid to the relationships amongst people, place, meaning and identity.

Originality/value
The architectural elements such as motifs and columns give identity to the image of historic neighbourhoods. The use of architectural identity components can help in decision making of planners and practitioners of urban neighbourhoods. The study was designed to develop a guideline so that future developments in Iran could be monitored to sustain strong neighbourhood attachment.
Markers of professional identity: records management jobs advertisements in Australia
Authors: Gibbons Leisa , Douglas Janine 
Published: September 2020

Purpose
This paper aims to explore how and why the Australian records and archives professions are in decline by examining job advertisements. The hypothesis was that competencies, developed as standards to communicate a professional identity to recruiters, would show in job advertisements.

Design/methodology/approach
Searches were set up to capture job advertisements for records and archives positions advertised on Australia’s largest job seeking website: Seek.com.au. Criteria developed to identify relevant advertisements were based on existing competencies and standards outlining records professionals’ skills, knowledge and attributes. Statistical analysis was used to assess the data.

Findings
Employers and recruiters are looking for generalist skills rather than specialist knowledge. Additionally, the requirement of having experience outweighs qualifications. Most job advertisements did not demonstrate awareness of records professionals’ specialist skills, knowledge and attributes.

Originality/value
There is a dearth of research into the Australian records workforce. There has been only one other research project into job advertisements in Australia, which focussed only on Western Australia. This research collected data over a three-month period for jobs advertised all over Australia. This paper raises questions about the role of competencies in establishing and communicating professional identity, as well as the future of records profession in Australia.
Phytoplankton composition in a eutrophic estuary: Comparison of multiple taxonomic approaches and influence of environmental factors
Authors: Gong Weida , Hall Nathan , Paerl Hans , Marchetti Adrian 
Published: September 2020

SummaryTo assess the comparability between taxonomic identification methods for phytoplankton, multiple approaches were used to characterize phytoplankton community composition within the Neuse River Estuary (NRE), North Carolina. Small subunit 18S rRNA gene sequencing and accessory pigment analysis displayed similar trends, indicating chlorophytes were the dominant microalgal group during most of the year, whereas results from microscopic cell counts, biovolume analysis and metatranscriptomics suggested diatom and dinoflagellate‐dominated communities. Spatial environmental gradients drove variation in taxonomic composition due to preferences for specific environmental conditions among different microalgal groups. Cryptophytes were a greater proportion of the phytoplankton community within high nutrient, fresher environments whereas diatoms and dinoflagellates dominated higher salinity sections of the estuary. This study provides a detailed examination of phytoplankton communities associated with environmental gradients present in the NRE. The high level of taxonomic resolution offered by DNA sequencing (i.e., species to sub‐species level) provides a better understanding of population dynamics at the base of estuarine food webs.
The importance of data source in prescription drug monitoring program research
Authors: Horwitz Jill R. , Davis Corey , McClelland Lynn , Fordon Rebecca , Meara Ellen 
Published: September 2020

AbstractObjectiveTo develop a legal research protocol for identifying various measures of prescription drug monitoring program (PDMP) start dates, apply the protocol to create a useable PDMP database, and test whether the different legal databases that are meant to contain the same information produce divergent results when used in an illustrative empirical exercise.Data sourcesOriginal research from state statutes, regulations, policy statements, and interviews; alternative PDMP data from the National Alliance for Model State Drug Laws and Prescription Drug Abuse Policy System; claims from a 40 percent random sample of Medicare beneficiaries, 2006‐2014.Study designCollaborative research effort among a group of lawyers to develop protocol. Legal research to produce an original database of dates state PDMP laws: (a) were enacted, (b) became operational, and (c) required query before prescribing controlled substances. Descriptive analyses characterize differences in dates of enactment, operation, and must query requirements. Regression analyses estimating, for each beneficiary annually any opioid prescription received in a calendar year, among other measures. Estimates conducted on under age 65 and full Medicare population.Data collection/extraction methodsPDMP legal databases were linked to annual Medicare claims.Principal findingsAn original database differs from commonly used, publicly available data. Outcomes tested depend on the measure of PDMP date used and differ by data source. Must‐query laws show the largest effects among all the laws tested.ConclusionsData choices likely have had large consequences for study results and may explain contradictory outcomes in prior research. Researchers must understand and report protocol for dates used in PDMP research to ensure that results are internally consistent and verifiable.
Advances and novel developments in molecular allergology
Authors: Üzülmez Öykü , Kalic Tanja , Breiteneder Heimo 
Published: September 2020

AbstractThe continuous search for new allergens and the design of allergen derivatives improves the understanding of their allergenicity and aids the design of novel diagnostic and immunotherapy approaches. This article discusses the recent developments in allergen and epitope discovery, allergy diagnostics and immunotherapy. Structural information is crucial for the elucidation of cross‐reactivity of marker allergens such as the walnut Jug r 6 or that of nonhomologous allergens, as shown for the peanut allergens Ara h 1 and 2. High‐throughput sequencing, liposomal nanoallergen display, bead‐based assays, and protein chimeras have been used in epitope discovery. The binding of natural ligands by the birch pollen allergen Bet v 1 or the mold allergen Alt a 1 increased the stability of these allergens, which is directly linked to their allergenicity. We also report recent findings on the use of component‐resolved approaches, basophil activation test, and novel technologies for improvement of diagnostics. New strategies in allergen‐specific immunotherapy have also emerged, such as the use of virus‐like particles, biologics or novel adjuvants. The identification of dectin‐1 as a key player in allergy to tropomyosins and the formyl peptide receptor 3 in allergy to lipocalins are outstanding examples of research into the mechanism of allergic sensitization.