In this paper, an operational framework is presented to improve electrical distribution network resilience based on the Mobile Energy Hubs (MEHs) concept. In fact, critical loads should be immediately islanded in a post-flood state and then recovered. Accordingly, this paper focuses on providing an effective management solution to enhance the functioning of electricity distribution systems with the objective of maximizing restoration of critical loads and minimizing their restoration time span based on MEH. To this end, MEHs are installed on trucks to deliver the required power for supplying the islanded critical loads in zones affected by a flood. Besides, in order to demonstrate a practical resilient structure, possible damage inflicted on other critical infrastructures is considered. Moreover, obstacles resulting from the destruction of the transportation infrastructure caused by a flood are overcome by using the shortest path algorithm (SPA). In this case, the optimization algorithm determines the shortest possible path for transporting the MEHs to supply critical loads in the least time aiming to improve the network resilience indicators. Finally, the proposed framework is studied in a standard test electricity distribution network. Simulations are carried out to evaluate the network resilience indicators of the proposed framework in obtaining a resilient distribution network during natural disasters.
Studies in human-computer interaction recommend creating fewer than ten personas, based on stakeholders’ limitations to cognitively process and use personas. However, no existing studies offer empirical support for having fewer rather than more personas. Investigating this matter, thirty-seven participants interacted with five and fifteen personas using an interactive persona system, choosing one persona to design for. Our study results from eye-tracking and survey data suggest that when using interactive persona systems, the number of personas can be increased from the conventionally suggested ‘less than ten’, without significant negative effects on user perceptions or task performance, and with the positive effects of increasing engagement with the personas, having a more diverse representation of the end-user population, as well as users accessing personas from more varied demographic groups for a design task. Using the interactive persona system, users adjusted their information processing style by spending less time on each persona when presented with fifteen personas, while still absorbing a similar amount of information than with five personas, implying that more efficient information processing strategies are applied with more personas. The results highlight the importance of designing interactive persona systems to support users’ browsing of more personas.
Inter-unit collaboration in transnational multinational corporations (MNCs) is central to unlocking MNCs' competitive advantage. We find that managing multilevel interaction of macro-level (social structures within and outside the MNC) and micro-level (individual interpretations and behaviors) factors ensures the implementation of strategic goals regarding inter-unit collaboration. In a case study of Finnish, Russian, and Indian unit collaboration in one European-origin transnational MNC, we observe that individual ascriptions of social identity to Indian colleagues (micro-level factor) affect the MNC's strategy implementation (SI) process and outcomes (macro-level factors). Building on the latter observations, critical realist theory of identity, and the idiosyncratic Indian context, we develop the currently inadequate multilevel theorization on the SI process in the MNC and expand perspectives on social identity in International Business literature. For MNC managers from Western countries, the paper offers insights into factors that should be considered to succeed in strategic and operational inter-unit collaboration with India.
The Smart city is important for sustainability. Governments engaged in developing urban mobility in the smart city need to invest their limited financial resources wisely to realize sustainability goals. A key area for such sustainability investment is how to implement and invest in emerging technologies for urban mobility solutions. However, current frameworks on how to understand the impact of emerging technologies aligned with long-term sustainability strategies are understudied. This article develops a simulation-based comparison between different cities and autonomous vehicle (AV) adoption scenarios to understand which aspects of cities lead to positive AV implementation outcomes. As urban mobility and cities will become smart, the analysis represents a first attempt to explore the impact of AVs on a large scale across different cities around the world. Archetypes are formed and account for most, if not all, world cities. For three of our archetypes (car-centric giants, prosperous innovation centers, and high-density megacities), promoting AV-shuttle use would deliver the greatest advantage as measured by improvements in the model's KPIs. To develop urban powerhouses, however, micromobility would deliver greater benefits. For highly compact middleweights, a shift from private cars to other non-AV modes of transportation would be the smartest choice.
This paper presents a novel method for resiliency assessment of the distribution system considering smart homes' arbitrage strategies in the day-ahead and real-time markets. The main contribution of this paper is that the impacts of smart homes' arbitrage strategy on the resilient operation of the distribution system are explored. The optimal commitment of smart homes in external shock conditions is another contribution of this paper. An arbitrage index is proposed to explore the impacts of this process on the system costs and resiliency of the system. A two-level optimization process is proposed for day-ahead and real-time markets. At the first stage of the first level, the optimal bidding strategies of smart homes are estimated for the day-ahead market. Then, the database is updated and the optimal bidding strategies of smart homes for real-time horizon are assessed in the second stage of the first level problem. At the first stage of the second level problem, the optimal day-ahead scheduling of the distribution system is performed considering the arbitrage and resiliency indices. At the second stage of the second level, the distribution system optimal scheduling is carried out for the real-time horizon. Finally, at the third stage of the second level, if an external shock is detected, the optimization process determines the optimal dispatch of system resources. The proposed method is assessed for the 33-bus and 123-bus IEEE test systems. The proposed framework reduced the expected values of aggregated costs of 33-bus and 123-bus systems by about 62.14 % and 32.06 % for the real-time horizon concerning the cases in which the smart homes performed arbitrage strategies. Furthermore, the average values of the locational marginal price of 33-bus and 123-bus systems were reduced by about 59.38 % and 63.98 % concerning the case that the proposed method was not implemented.
With the continued growth of wind power penetration into conventional power grid systems, wind power forecasting plays an increasingly competitive role in organizing and deploying electrical and energy systems. The wind power time series, though, often present non-linear and non-stationary characteristics, allowing them quite challenging to estimate precisely. The aim of this paper is in proposing a novel hybrid model named Evol-CNN in order to predict the short-term wind power at 10-min interval up to 3-hr based on deep convolutional neural network (CNN) and evolutionary search optimizer. Specifically, we develop an improved version of Grey Wolf Optimization (GWO) algorithm by incorporating two effective modifications in its original structure. The proposed GWO algorithm is more effective than the original version due to performing in a faster way and the ability to escape from local optima. The proposed GWO algorithm is utilized to find the optimal values of hyperparameters for deep CNN model. Moreover, the optimal CNN model is employed to predict wind power time series. The main advantage of the proposed Evol-CNN model is to enhance the capability of time series forecasting models in obtaining more accurate predictions. Several forecasting benchmarks are compared with the Evol-CNN model to address its effectiveness. The simulation results indicate that the Evol-CNN has a significant advantage over the competitive benchmarks and also, has the minimum error regarding of 10-min, 1-hr and 3-hr ahead forecasting.
This paper presents a multi-level optimization framework for power system operators' joint electricity markets capacity-withholding assessment. The main contribution of this research is that three capacity-withholding indices are introduced for day-ahead, intra-day, and real-time scheduling of the system that detect the capacity withholding and arbitrage opportunities of Virtual Power Plants (VPPs) and non-utility fossil-fueled GENeration COmpanies (GENCOs) in an ex-ante procedure. A three-level optimization process is used so that the system operator can estimate the coordinated bidding of VPPs/GENCOs in different energy and ancillary services markets to prevent the formation of withholding groups. The first level problem consists of two stages. The first stage estimates the optimal capacity withholding and arbitrage bidding strategy of VPPs/GENCOs, and the second stage determines the optimal system scheduling for the day-ahead horizon. A full competition algorithm is also carried out to evaluate the competition states of VPPs/GENCOs. The second and third level problems consist of two optimization stages for the intra-day and real-time optimization horizons. At the first stage of each level, the process estimates the coordinated bidding of VPPs/GENCOs, and at the second stage of each level, the system resources are optimally dispatched. The proposed method is applied to 30-bus and 118-bus IEEE test systems. The proposed algorithm reduced the maximum locational marginal prices of 30-bus and 118-bus test systems by about 57.04% and 44.73% compared to the normal and the worst-case contingency operating conditions, respectively. Furthermore, the proposed method reduced the average values of day-ahead, intra-day and real-time dynamic capacity withholding indices of the 118-bus test system by about 32.92%, 40.1%, and 46.85%, respectively.
We lack an in-depth understanding of how the different roles played by public innovation intermediaries during their engagement in collaborative projects enable them to generate ambidexterity. By adopting a sequential mixed methods research design to gather data from 122 Research and Technology Organizations (RTOs) operating in Europe, the findings suggest that public innovation intermediaries performed two different roles in collaborative projects namely, knowledge integration and network building, and these have a differential impact on the generation of distinct types of in-house innovation. The knowledge integration role is conducive to exploratory innovation, whereas the network building role contributes to exploitative innovation. Importantly, relational, and internal capabilities mediate between these roles and innovation. Yet, this mediation effect varies depending on the nature of the public innovation intermediary’s role and innovation profile. How public innovation intermediaries should utilise their key roles to generate in-house ambidexterity is crucial in leveraging the impact of public funding in this area.
The unique properties of natural gas (NG), including high availability and lower cost compared with other fossil fuels, make it attractive in internal combustion engine (ICE) application. NG is composed mainly of methane and has greater knock resistance than gasoline, enabling higher compression ratios (CR). In contrast with the distinctive advantages, the NG fueled engines suffer from lower power and torque outputs. To address the subject, this study proposes an approach employing NG direct injection (NGDI) strategy (with higher volumetric efficiency unlike port injection), enabling a higher CR irrespective of knock limit. This work applies reactive computational fluid dynamics (CFD) to investigate spark ignited co-combustion of direct-injected NG with port-admitted gasoline. The results are validated against experimental data. In all simulated cases, the equivalence ratio (i.e., ∅ = 1) and the total input energy are kept constant. Engine performance is evaluated for three CRs (10.5, 11.5, and 12.5:1), five proportion of CNG (RCNG) and at part- and full-load conditions at an engine speed of 1500 rpm. Results indicated that while running RCNG = 100 % with a CR of 10.5:1, carbon monoxide (CO) and carbon dioxide (CO2) emissions were decreased by 29.3 % and 23.5 % respectively, compared to RCNG = 0 %. The corresponding emission reduction at CR = 11.5:1 was 27.1 % and 24 %; at CR = 12.5:1 they were 29.6 % and 23.5 % respectively. At each CR, the knock intensity at full load fell significantly as the percentage of NG increased. At a CR of 12.5:1, ringing intensity (RI) at full load decreased by 88.6 % when using RCNG = 100 %, instead of RCNG = 0 %. Under the same conditions, RCNG = 25 % cut RI by 56 %.
We examine how the institutional distance between home and host countries is associated with the characteristics of foreign subsidiary debt, including leverage, debt maturity choices, and cash holdings. We utilize the multidimensionality of institutional distances to examine ten different distance dimensions. We use a sample of 3139 foreign subsidiaries operating in France and being headquartered in 44 different countries. We find that while subsidiaries' financing choices are partially explained by standard determinants, they are also significantly associated with different forms of institutional distance. Regarding the heterogeneity of institutional distances, results show the dominance of financial and cultural distances for leverage levels; knowledge and political distances for debt maturities; and a dominance of demographic, geographic, and political distances for cash holdings levels.
Enterprise architecture (EA) adoption initiates broad changes in organizations and organizational functions. However, the existing literature on how and what factors influence the changes in EA adoption remains limited. Our study aims to fill in this gap. We study how the EA-initiated changes occur and what are the factors influencing it. Our process-oriented perspective, our data from a qualitative case study, and the lens of organizational change illustrate how the changes occur in organizations, what the factors are, and how especially managers and their activities influence the change. We show that the change is both sociotechnical and punctuated, oscillating between different organizational levels.
In recent time, Turkey could be said to have experienced different levels of Economic Risk, Financial Risk, and Political Risk from low- to high-level. This study investigates the linkage between country risks, namely Financial Risk, Economic Risk, and Political Risk (FEP risk) in Turkey for the period 1984Q1 to 2019Q1 by using threshold cointegration, Markow-switching regression (given the nonlinearity and structural breaks observed in the time series variables), and frequency domain causality approaches. The empirical findings of this study reveal that (i) nonlinear cointegration between Economic Risk, Financial Risk, and Political Risk in Turkey is statistically significant given the evidence of threshold cointegration test, which determines the structural breaks endogenously; (ii) there is positive linkage among the component of country risk at different volatility periods; (iii) there is a significant Granger causal linkage between Economic Risk, Financial Risk and Political Risk at the different frequency levels. The study is likely to open debate about the literature since the study concludes with a discussion on short-run and long-run implications for economic, political, and financial stabilises, thus offering policy suggestions for the policymakers in Turkey.
To understand how consumers perceive greenwashing, this study examines the impact of green advertising receptivity (GAR), non-deception (ND), green brand image (GBI) and transparency (TR) on green brand trust (GBT) and subsequently the impact of GBT on consumers' purchasing intentions (PI). We also consider the mediation role of GBT between the relationship of the independent variables (GAR, ND, GBI and TR) and the dependent variable (PI). At the same time, we examine the moderating impact of ND on the GAR and GBT and of TR on the GBI and GBT relationship. Using the Stimulus-Organism-Response (SOR) framework, we test a number of hypotheses. A survey-based questionnaire was utilized to gather the data from Vietnamese respondents (n = 262). We analysed the data using the partial least squares (PLS) method, which is a structural equation modelling (SEM) technique, with the assistance of the SmartPLS computer program 3.0. The data results show that GAR and GBI positively influence GBT, but the influence of ND and TR on GBT is insignificant. Again, the data show that GBT has a positive influence on PI, and as a mediator variable, it facilitates GAR, GBI and PI, but not the link between ND, TR and PI. A significant interaction effect of ND demonstrates that it strengthens the GAR-GBT relationship. However, the TR has no impact on the GBI-GBT relationship. The findings of this study provide insights into the theory and the essential managerial implications for successfully managing the implementation of green marketing strategies.
With the exemption of Canada, the G-7 countries have largely flourished at the detriment of their ecological sustainability bearing in mind that these countries' have remained ecologically deficit for several decades. Given the potential effect of environmental degradation associated with the trend of ecological deficit of these countries , this study attempts to understand the contribution of renewable energy dimensions through the measure of renewable energy efficiency and renewable energy use alongside evaluating the role of the four main aspects of economic freedom. By using empirical tools, the findings revealed that renewable energy aspects contribute to environmental sustainability among the countries through a significant mitigation of their ecological footprint. Importantly, the aspects of economic freedom , that is, government size, legal system and property rights, freedom to trade internationally, and regulation hampers environmental sustainability by increasing the countries ecological footprint. The elasticity of impact of this dimension of economic freedom is in the range of 0.19-0.21 at 1% statistically significant level. However, population of these countries does not show a detrimental effect, rather the finding revealed that population improves environmental quality by a statistically significant degree. Given these revelations, there are deducible policy take home from this study.
User-centric design within organizations is crucial for developing information technology that offers optimal usability and user experience. Personas are a central user-centered design technique that puts people before technology and helps decision makers understand the needs and wants of the end-user segments of their products, systems, and services. However, it is not clear how ready organizations are to adopt persona thinking. To address these concerns, we develop and validate the Persona Readiness Scale (PRS), a survey instrument to measure organizational readiness for personas. After a 12-person qualitative pilot study, the PRS was administered to 372 professionals across different industries to examine its reliability and validity, including 125 for exploratory factor analysis and 247 for confirmatory factor analysis. The confirmatory factor analysis indicated a good fit with five dimensions: Culture readiness, Knowledge readiness, Data and systems readiness, Capability readiness, and Goal readiness. Higher persona readiness is positively associated with the respondents' evaluations of successful persona projects. Organizations can apply the resulting 18-item scale to identify areas of improvement before initiating costly persona projects towards the overarching goal of user-centric product development. Located at the cross-section of information systems and human-computer interaction, our research provides a valuable instrument for organizations wanting to leverage personas towards more user-centric and empathetic decision making about users. Supplementary information: The online version contains supplementary material available at 10.1007/s10799-022-00373-9.
Blockchain technology enables a single database source to maintain permanent information about products and manufacturers and share transparent data in the supply chain from suppliers, factories and distributors. In the automotive industry, blockchain technology has improved the transparency of the value chain and improved the operational efficiency of the supply chain. This research designed a blockchain-based supply chain system framework to meet the business needs of the automotive industry and improve operational performance. This research is based on the ongoing European Union Horizon 2020 project AVANGARD. The features of blockchain are investigated and aligned concerning SCM in the automotive industry.KeywordsBlockchainSupply chain managementAutomotive industrySmall- and medium-sized enterprise
As inverter-based distributed energy resources (DERs) continue to proliferate in the distribution systems and provide a significant part of the generation, enhancing the visibility of the system for coupling transmission and distribution networks is becoming essential. The paper offers a monitoring and managing approach based on integrating information from synchrophasors and phasor data concentrators (PDCs) to enhance the deployment of the smart inverter, post their dynamic functions and overcome the decoupling between distribution/transmission operations. The proposed approach includes DER monitoring and managing entity (DER-MME) which communicates with PDC units that can manage the smart inverters functions in real-time during normal/abnormal operation based on a proposed fault detection and localization algorithm. Although the approach can be expanded to include several functions, in the paper, the focus was on the momentary cessation function (MC) and how it can be dynamically controlled by the proposed approach to improve the response of smart inverters. The merit of the proposed approach has been illustrated on several transmission and distribution faults that triggered fault-induced delayed voltage recovery (FIDVR) events which are common in distribution networks. © 2017 Elsevier Inc. All rights reserved.
Research offers some indication that the online customers' shopping experience (OCSE) can be a strong predictor of online impulsive buying behavior, but there is not much empirical support available to form a holistic understanding; whether, and indeed how, the effects of the OCSE on online impulsive buying behavior are affected by customers' attitudinal loyalty and self-control are not well understood areas of research. In this study, we examine how functional and psychological dimensions of the OCSE influence online impulsive buying within e-commerce platforms. We will investigate customers' attitudinal loyalty as a mediator between the OCSE and online impulsive buying behavior, and the customers' self-control as a moderator between customers' attitudinal loyalty and online impulsive buying. To analyze these relationships we will conduct an online survey (n = 1489) with customers of two leading Chinese e-commerce platforms: Jindong and Taobao. The findings from structural equation modeling indicate a positive relationship between the tested dimensions of the OCSE and customers' online impulsive buying. We also find a mediating role of customers' attitudinal loyalty and negative moderation of customers’ self-control. Theoretically, the findings contribute to the literature regarding online impulsive buying and the online customer experience. For managers, the findings stress the importance of ethical management with regard to the online shopping experiences.
The increasing environmental challenges associated with the Global South is potentially associated with the socioeconomic changes amid potential institutional deficiencies such as the weak or inefficient environmental regulation. Thus, this twenty-first century challenge has increasingly necessitated more climate action from the Global South as championed by the developed economies. On this note, examines the environmental aspects of law and order (LO) vis-à-vis legal system and socioeconomic (SE) indexes of the Political Risk Services for a panel of 80 selected Global South countries over the period 1984–2014. Additionally, by employing the economic growth vis-à-vis the Gross Domestic Product per capita (GDPC) as additional explanatory variable, the study employs the more recent experimental techniques of Mean Group Estimator (MG), the Augmented Mean Group Estimator (AMG) and the Common Correlated Effects Mean Group (CCEMG). Importantly, with the more efficient CCEMG, the study found that the strength of the legal system in the Global South (although not statistically significant) is a crucial factor to mitigated carbon emission in the panel countries. However, the study found that an improved socioeconomic condition and economic expansion is detrimental to the Global South’s environmental quality. Furthermore, the Granger causality result implied that each of LO, SE and GDPC exhibits a feedback relationship with carbon emissions. Hence, the study suggests the need for a stronger implementation of environmental regulations through a revitalized legal system and some concerted socioeconomic policies that address poverty and unemployment among other factors.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.