Earlier research contrasting human performance with and without automationsupport only have focused on what has been referred to as “out-of the-loop unfamiliarity” effects without varying the levels or stages ofautomation (e.g.,Crossman, 1974; Eprath & Young, 1981; Kessel & Wickens, 1982; Wickens & Kessel, 1979; 1980, 1981). These studies provide evidence for automation-induced performance consequences but do not allow for any conclusion about the relationship to different degrees ofautomation. The latter issue attracted little research until the early 1990s (see for early examples, e.g., Crocoll & Coury, 1990; Layton, Smith & McGoy, 1994). Yet, since then at least a limited number of studies have become available that have collected empirical data on effects of two or more different DOAs on workload and/or SA (e.g., Endsley & Kiris, 1995; Kaber, Onal & Endsley, 2000; Lorenz, Di Nocera, Röttger & Parasuraman, 2002a; Sarter & Schroeder, 2001). The pattern of results of these single studies provides a somewhat mixed picture. Whereas some studies support the existence of the above trade-off as defined by better routine performance but worse performance when automation fails (e.g.,Sarter & Schroeder, 2001) others do not find this effect (Lorenz et al., 2002a) and still others suggest that medium levels ofautomation provide the best choice in terms of maintaining SA and return-to-manual performance (Endsley & Kiris, 1995) or provide an even more complex pattern of effects (Endsley & Kaber, 1999). However, due to differences in DOA levels considered, and a generally limited statistical power, the effects of single studies are inconclusive. A more valid overall picture might be revealed by quantitatively combining data from a variety of studies across varying domains (e.g., process control, aviation), an approach analogous to a classic meta-analysis (Rosenthal 1991; Fadden, Ververs, & Wickens, 1998; Horrey & Wickens, 2006; Wickens, Hutchinson, Carolan & Cumming, 2013). The purpose of the current investigation is to provide such meta-analysis by (a) aggregating data from studies that compared different degrees ofautomation, (b) examining the extent to which they show the postulated trade-off between normal operations and failure conditions as the degree ofautomation (DOA) was manipulated and (c) if possible, by identifying factors that may mitigate or moderate this trade-off.
In line with the constitution and the Afghanistan National Development Goals (ANDS), the government has transformed all the laws from a centrally planned economy to a market led system in the last decade. Since 2001 telecommunications, construction, banking and other services have made significant progress; but the industrial production’s contribution to GDP was only 22.5%. The Textile industry has played a significant role in the country’s history and culture, owed in part to Afghanistan’s central location on the Silk Road, connecting the east and west. In the 1980s during the soviet regime the country was producing 350000 metric tonnes of processed cotton per year, in 2013 it was a meagre 36300 metric tonnes (AISA, 2013). The country has 4 main cotton and textile companies located in four major cities, namely Gulbahar Textile Plant (Kabul), Kandahar Cotton Textile Enterprise, Herat Textile Project and the Balkh Cotton Textile Enterprise. Currently 66% of the total textile production is sold regionally, 27% nationally and 7% is exported to other countries. While cheap labour and raw materials, and a growing domestic market have been promising factors for the growth of this sector; the industry is still plagued by low productivity, the lack of modern equipment and efficiency, and the tendency to export raw materials. These weaknesses place domestic firms at a disadvantage and make them vulnerable towards foreign competitors and cheaper imports. Out of 350 factories in the Herat Industrial Park, only 100 remain due to international competition (AISA, 2013). In pursuance of Afghanistan’s WTO accession, tariffs rates have been kept as low as 2.5% (OTEXA, 2011).
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In order to calculate prospective and retrospective reserves, interest and transition intensity functions have to be chosen. Note that in the retrospective view the interest and transition intensity functions relate to the past and, thus, their realized values can be observed. This implies that the observed basis can be used as basis for the retrospective calculations. In the prospective view, however, interest and transition intensities relate to the future and therefore the prospective calculations are always performed with an assumed basis. Under this assumed basis, the values of retrospective and prospective reserves are equal provided premiums are determined by the equivalence principle (using the same basis). Let us mention that the International Association of Insurance Super- visors (IAIS) recommends that the liabilities of insurance companies should be evaluated on consistent bases, i.e. by means of an economic valuation that reflects the prospective future cash flows. In Europe, Solvency II determines that the best estimate of the provi- sion for future commitments must be measured based on current information and realistic predictions.
The road from Kamanjab town meanders through the arid land of southern Kunene region in northwest Namibia, pitting aside large commercial livestock farms enclosed in wire fences, mostly owned by farmers of European descent. About a kilometre into the farms, one can see green leafy palm trees surrounding large brick houses in which households of commercial farmers live. On the main road, we pass through the gate directly aligned to a metallic billboard that reminds us of our entry into ǂKhoadi ǁHôas conservancy, which lies in a communal land. Warning signs along the road announce the likelihood of the presence of elephants in the area as well as other wild animals. After all, the name ‘ǂKhoadi ǁHôas’ literally translates to ‘elephants’ corner’, the meaning which will become clear through this work. Beyond the gate is a communal area where people, majority of whom are from Damara community, live in small villages of about 15 households clustered around water points consisting of a concrete water reservoir and plastic tanks. Their housing is mostly mud-walled and tin-roofed huts. Cattle, goats and sheep graze in the unfenced fields marking the dominance of pastoralism as an important livelihood strategy in the area. Households own livestock in varied numbers and share water which mostly is pumped from boreholes, using diesel engines, into communal concrete reservoirs. As a communal conservancy, people live in the area with wild animals. Occasionally, a motorcade of tourists drives through the conservancy with their occupants, flashing their cameras at every fascinating scenery, especially of wild animals, and eventually retiring to the luxurious Grootberg Lodge or Hoada Campsite located within the conservancy. Somewhere on the western cliffs of Grootberg hills and plateau, a trophy hunter aims his riffle, from a hideout, at an elephant or oryx to make a kill for his profit and leisure. After some days of safari or trophy hunting in the conservancy, the tourists and hunters drive off to far lands taking with them the satisfaction of leisure and photographic memories of community conservation. They leave behind an income to the conservancy that is expected to support development for communities in ǂKhoadi ǁHôas to incentivise their desire for conservation.
As we can derive from this taxonomy, transaction-costs are conceptually inherently dynamic (Dixit 1996, pp. 43-44). The last component of our framework adds to these dynamic aspects and the strategic dimension of club formation. I borrow from insights of the venue-shopping theory as outlined by Baumgartner and Jones (1993) that theorizes similar strategic actions, but rests on basic assumptions which are not sufficiently convincing under the present discussion of differentiated integration. 2 A recent perspective on venue shopping that focuses less on the issues at stake and more systematically on the key actors’ political strategies is helpful to convincingly apply this framework to the challenging issue of differentiated integration as club formation (Pralle 2003). Firstly, Pralle argues that venue shopping (read here instead: sub club formation) can be more experimental and less deliberate or calculated than commonly perceived; secondly, actors choose venues not only to advance substantive policy goals, but also to reinforce organizational identities; and finally, venue choice is shaped by policy learning processes (Pralle 2003, 234). In general, these findings fit very well with the empirical case studies of this article and can be transposed to club theory reasoning. Club formation opens the leverage to forward and decide on policy proposals that have been blocked in the Council before. Tactical reasoning of adversaries' exclusion seems to be purely calculated, whereas the “long term” circumvention strategy is far more experimental and challenged by contingent dynamics. I presume that the strategic goal of the sub club members is to eventually communitarize their club good by having all member states take part in the policy by way of altering the cost-benefit balance. I, however, argue that experimentation and contingency play a major role in the process of defining how this strategic goal can be reached. Furthermore, Pralle's emphasis of policy learning touches my case studies in two ways. First, in several aspects the Pruem initiative was rooted in the Schengen experience and partly driven by the same actors. Hence, it accounts per se for an instance of policy learning. Second, empirical evidence from both cases shows a strong reframing of the initiative as a
Counting users of mobility aids as a subset of all users of transport provides a means to make links between transport outcomes, social wellbeing and public health. Central to its uptake and effectiveness is a high-level directive, so that the transport industry begins to measure accessibility as part of its decision making process. Without indicators of accessibility in transport, social and health objectives relating to active and independent participation have no means of being realised. As noted by Rickert (2005), “The recent emphasis on inclusive transport occupies an intersection between the interests of urban infrastructure workers and social development workers. These two groups understandably look at indicators and performance measures from different perspectives.” (p13). Currently in New Zealand, government priorities for transport are dominated by traffic efficiency and general road safety, which is itself biased towards car crashes because of inherent under-reporting rates of incidents involving pedestrians. The importance of accessibility for non-transport outcomes (aside from economic development) is largely ignored.
(R&D) activities in low-carbon technologies and to high prices for CO 2 certificates, renewable energy expansion does not rely on additional policy incentives.
Return of the nation state. Prevailing international competition and national protec- tionism leads to a renunciation of transnational trade agreements. As a consequence, there is no significant exploration of new trade routes with growing fossil fuel prices in Europe. The collaboration in planning, operating and optimizing the European power system is low and there are no collective efforts in designing a unified European power market. Hence, transnational infrastructure projects like large interconnec- tors have low priority. Furthermore, national policy measures towards climate and environmental protection are preferred to international institutions. As a result, the EU ETS becomes less important, leading to diminishing CO 2 prices. Additional policy incentives are introduced to stimulate investment in renewable energy as a means for a higher level of energy autarky. Sharing economy concepts become pop- ular and regional value-added chains are broadly utilized. However, the extended regionalization of economic activities puts the local environment under additional stress, which results in a society that attaches high importance to sustainability and environmental protection on a local level.
real activity, which emerged following the recession in the U.S. in the 1990s (see, e.g., Bernanke and Lown 1991, Hancock and Wilcox 1993, Berger and Udell 1994, and Furfine 2000). The studies that evolved since then, including those that were motivated by the recent global financial crisis, can be grouped according to how changes in capital are measured. Using observed capital ratios is one option (see e.g. Bernanke and Lown 1991, Noss and Toffano 2014) while exploiting variation in bank-level capital requirements, i.e. supervisory data which is in general unobservable for the public, is a second (see e.g Ediz et al. 1998, Mishkin 2000, Francis and Osborne 2009, Aiyar et al. 2014b, Bridges et al. 2014, Jim´ enez et al. 2014, Meeks 2015, and Behn et al. 2016). Our paper circumvents an identification based on capital and instead translates the impulses first to credit supply shocks (of two polar kinds) which can then be identified based on sign restrictions. That is, we assume that capital ratios are adjusted by the amount required by the prudential supervisor and distinguish between ’asset-side deleveraging’ and ’raising fresh equity’ scenarios. By considering these two polar cases, we are agnostic about the effects of higher capital requirements on banks’ funding costsand the pass-through to lending rates.
The description of possible futures through the creation of scenarios is a for- malized way to make statements about possible future development paths using knowledge from the present and insights from the past. A fundamental distinction can be made between qualitative and quantitative scenarios. Qualitative scenarios, often also called narratives or storylines, are largely based on verbal descriptions of potential futures [e.g., 5]. Methods for developing such narratives are usually flexible in terms of the parameters they require, allowing to consider a range of different social, economical, technical, and environmental parameters. This way, softer and more diffuse concepts such as political stability, or environmental awareness can be included in the analysis. Computer-based quantitative scenarios, on the other hand, allow for numerical insight into the system under consideration. Alcamo  argues that quantitative approaches are more transparent than their qualitative counter- parts because their model assumptions are expressed as mathematical equations. Craig et al.  contrast this with the fact that, for energy forecasts, there are nec- essarily implicit assumptions about human behavior, including social, institutional and personal interactions, as well as human innovation.
The variable Size is significant in all models at a 1% confidence level. The algebraic sign of the coefficient of the size variable is positive and less than 1. 32 This indicates that costs increase underproportionally compared with the size of the company, which suggests that there are economies of scale regarding costsof regulation for insurance companies. 33 Big insurance companies have in total higher regulation costs than small insurers, but in relation to their size regulatory costs are lower. These findings are in line with economic intuition and with Grace and Klein (1999), Deloitte (2006) as well as Europe Economics (2010). As mentioned in Table 2, Grace and Klein (1999) evaluate the explanatory impact of the stringency of the regulatory environment on different expense ratios (total expenses/premiums written, claims costs/premiums written, licenses & fees/premiums written and salary expenses/premiums written). In addition they control for size and report a significantly negative impact of size on each expense ratio. Deloitte (2006) also reports economies of scale regarding costsof regulation for investment banking & corporate finance companies and institutional fund management firms. Investment & pension advice companies are an exception; their size seems not to affect the costsof regulations. Finally, Europe Economics (2010) report that relative to their size, large insurers have to bear lower compliance and implementation costs than small insurers regarding MiFID regulation.
The Special Act on City–Rural District Consolidation, the neighbouring city and rural
districts can be merged into one bigger local authority following a referendum of the voters within the two areas; and as a result, as was mentioned earlier, the 40 consolidated local authorities were created, with financial incentives from central government. Up until now, however, there has been no case, in Korea, of the creation of a unitary local authority, partly because the two-tier, province–district system has been embedded in Korean administrative culture for more than 500 years, and partly because politicians and officials, even in central government, had not attempted local government reorganisation accompanying tier-change, since they know that tier- change reform provokes powerful and indeed uncountable resistance from local politicians, and local government employees in particular. The present government, which came to office in 2003, also knows that local government reorganisation incurring tier-change would be politically burdensome and administratively difficult to implement. However, the Government, which has been focusing on decentralisation and government innovation since it came to power, has decided to support Jeju Province in its move to become a unitary local authority with more autonomous power and special status, in the hope that if such an authority is created, it will become more competitive, both economically and socially. Table 1 shows the main characteristics of Jeju Province.
Scenario 4 (CTA+low fat meat + 200g vegetables) 10.27 1.98 2.73 0.57 Standard errors are estimated using the Krinsky-Robb method (Krinsky & Robb 1986) with 2000 Halton draws. The initial state corresponds to a home-cooked meal with 75g vegetables and meat.
As can be seen from table 3, the estimated user benefit measures in scenario 1 and 4 are all statistically significantly different from zero. The results also reveal that, in general, the ‘multiple alternatives’ approach produces estimates that are about a quarter of the ‘state of the world’ estimates. This result is in line with previous findings (e.g. Lancsar & Savage 2004). Thus, the choice of benefit measure strongly influences the benefit value. If the ‘state of the world’ approach is mistakenly applied in a multiple alternative setting, it can result in biased benefit estimates. Furthermore, the results suggest that all of the scenarios provide a positive welfare gain, with scenario 1 providing the smallest gain, followed by scenario 3 and scenario 2, and finally with scenario 4: the case of going from a home cooked meal with meat and 75g of vegetables to a CTA meal with low fat meat and 200g of vegetables, as the scenario with the largest gain.
household indebtedness. As we show in Section 2.3 in our simulations, the social benefitsof leaning fall short of their social costs for all alternative measures of household indebtedness.
2.3. How do the benefitsof leaning compare with its costs?
We assess the net benefitsof leaning by computing the responses of key macroeconomic variables to a monetary policy leaning adjustment in three of Bank’s policy models: MP2, ToTEM and LENS. 5 The benefitsof a leaning action are associated with a decrease in the probability and impact of a financial crisis. This response is obtained outside each of the models by constructing a relationship between the growth rate of household debt and the probability of a crisis. 6 That probability falls slowly in our baseline simulation, at most by 0.005 annualized percentage points, from 0.872 to
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The business process and the process KPIs are specified using a business process modeling application that automatically generates the corresponding workflow definitions. The process is supported by different applications like e.g. a Customer Relationship Management (CRM) application for customer interaction, a service management (SM) application to coordinate service orders, a billing system (BS) for invoicing and a Human Resources Management (HRM) system for administering the employees. In order to properly support the business process, the application functionalities have to be integrated according to the process logic. This is achieved in two steps: First, all applications are connected to the same integration platform by supplying uniformly defined services. Each application can publish data messages of the format specified in the interface and subscribe to messages of other applications. The integration mechanism redirects all published messages to all applications that have subscribed to the corresponding message type. Second, the workflow engine of the integration platform uses the workflow definition generated from the process model to coordinate the application messages with respect to the process logic. In this example the CRM application provides a service to manage the customer records, the SM application provides different services for managing incidents, service orders, and incident resolution reports, the BS application offers an invoicing service and the HRM application provides a service for managing records of call center agents. The workflow definition specifies which functionality is needed to execute a specific process step. For instance, during incident resolution functionality is needed to access the incident record, the customer record, and the service order. The integration platform will coordinate the services according to the workflow definition and attaches different time stamps to each coordinated message to allow for monitoring of the process activities. In addition, a specific listening application collects all messages containing performance-relevant data and populates them into the Data Warehouse (DWH). In the DWH performance data is consolidated and prepared for performance analysis purposes.
Various estimates of future fares for autonomous taxis have been proposed for the United States. For example, Burns et al. (2013) used travel survey data in combination with an agent-based optimization model to find that a system of pooled autonomous taxis could offer trips at 0.41US-$ per mile (compared to 1.60US-$ for a privately owned conventional car) and that fares could decrease further to 0.15US-$ per mile for purpose-built vehicles. Using a similar approach, Fagnant and Kockelman (2018) found that a pooled autonomous taxi scheme could offer trips at 1.00US-$ per mile, which already includes a 19% profit margin. Using an agent-based simulation as well, Loeb and Kockelman (2019) put a special emphasis on the detailed calculation of the costs for the charging infrastructure for electric automated taxis. For the Austin area, their results suggest that the costs for the latter amount to 59 Cents per mile versus 45 Cents for the gasoline counterpart. Stephens et al. (2016) analyzed how the single cost components of today’s taxi schemes may be affected by autonomous vehicle technology for average utilization patterns. Depending on the scenario, they project a lower bound of operating costsof 0.20–0.30US-$ per passenger-mile. Taking up a similar methodology for individual autonomous taxis, Johnson and Walker (2016) expect fares of 0.35US-$ by 2035. Unlike Stephens et al. (2016) and Johnson and Walker (2016), Lim and Tawfik (2019) also look at the effect of advertising during the ride. Their results suggest that the costs for electric and automated taxis will amount to between 8 and 29 Cents per mile, three cents lower than without any advertising. In a different approach using NHTS trip distance and time of day distributions to generate realistic demand patterns for Austin, TX, Chen et al. (2016) estimated that a fleet of shared electric automated vehicles could potentially serve the demand at a cost of 0.42US-$ to 0.49US-$ per occupied mile traveled. While those estimates are mostly within the same ballpark, most of the above approaches rely on strong assumptions on travel demand and utilization patterns, neglecting potentially important factors such as maintaining and cleaning the fleet or sometimes do not make all their assumptions transparent. Moreover, mostly only single modes (pooled au- tonomous taxis) were considered, ignoring that they will be only one among several evolving options.
Bats form the widely distributed, species-rich order Chiroptera which occurs on almost all continents (except Antarctica) and most of the islands (Kunz 1982). They are mostly nocturnal and cover various foraging niches and ecological functions (e.g. seed distribution, flower pollination) which highlights their importance for different ecosystems and the necessity to study their responses to different environments. Especially their unique ability to actively fly sets them apart from other mammals. Despite their high metabolic rate, similar to the one of birds (Munshi-South and Wilkinson 2010), bats are exceptionally long-lived compared to other mammals of a similar body size (Wilkinson and Munshi-South 2002, Munshi-South and Wilkinson 2010). The oldest captured bat in the wild was a 41-year old Brandt’s bat (Myotis brandtii) (Podlutsky et al. 2005). Additionally, bats show a low annual reproductive output (Fleischer et al. 2017). As a result of the longevity in combination with the low reproductive output, genetic adaptation to changing conditions is expected to be slow (Reed et al. 2011). Therefore, as already mentioned above, phenotypic plasticity, in particular behavioural plasticity, should be a key mechanism for bats to cope with changing weather conditions during climate change.
The widespread availability and adoption of various smart city solutions have benefited their users by providing new services and information generated in real- time. These solutions use different types of sensors and GPS to collect, process and display data within the web and/or mobile applications. Focusing on the determinants of the intentions to use an application or its success, a large number of researchers developed and validated models such as TAM, UTAUT, IS Success Model and similar ones. This paper presents an exploratory approach that is based on the cost-benefit analysis with end-users who were invited to express their perceptions of different smart city solutions. Qualitative data were collected to devise a research instrument in subsequent phases based on the feedback from second-year business students. For each of the selected four smart city applications (smart parking, water quality monitoring, air quality monitoring, and real-time traffic monitoring), respondents were asked to work in groups and create a list ofbenefitsandcosts from their perspective. The analysis resulted with the list of 98 different cost and benefit statements (16 costs common for four smart city applications, 12 benefits common for four smart city applications, 10 distinctive costsand 60 specific benefits).
There are several models that focus on the evaluation of acceptance and success of different technology solutions in particular. One of the more pertinent ones is Technology Acceptance Model (TAM) developed back in 1989 to measure the technology use (Davis, 1989), that quickly became the dominant model for investigating factors for user acceptance (Marangunić & Granić, 2015). TAM defined two variables addressing the use: perceived usefulness and perceived ease of use. Further to that, and as an extension of TAM, Unified Theory of Acceptance and Use of Technology (UTAUT model, had four core determinants of intention and usage (performance expectancy, effort expectancy, social influence and facilitating conditions), and up to four moderators of key relationships (Venkatesh et al., 2003). The review of standard models such as TAM and UTAUT is often complemented by DeLone–Mclean's model, representing an established and well-known information system (IS) model for assessing IS success. Based on the model, system quality, information quality, service quality, use/intention to use, user satisfaction, and net benefit are distinct, but related dimensions of IS success (DeLone & McLean, 2003). Literature review confirms that many papers focusing on smart city-related topics, in particular, address the concepts of use and intention to use, i.e. explore the factors that can predict that kind of behaviour. In that regard, and in that context, it has been confirmed that that (perceived) ease of use and perceived usefulness affect the intention to use (Althunibat et al., 2014; Van Compernolle et al., 2018; Liao et al., 2007; Mensah, 2018; Susanto et al., 2017). Additionally, while exploring the factors affecting the behavioural intention to use smart city services, many authors confirmed that performance expectancy and effort expectancy positively affect behavioural intention (Gunawan, 2018; Habib et al., 2019; Zuiderwijk et al., 2016). Research that focused on the intention to use digital coupons among university students, showed, however, that perceived economic benefit has the greatest impact on intention to use (Guo et al., 2019). Further, results show that potential users may be willing to use a digital service when they expect it will give them an obvious advantage or benefit over the alternative approach to those services (Sepasgozar et al., 2019; Tomitsch, 2018).