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The purpose of this study is to introduce a matching function approach to analyze matching in financial reporting.

The matching function is first analyzed analytically. It is specified as a multiplicative Cobb-Douglas-type function of three categories of expenses (labor expense, material expense and depreciation). The specified matching function is solved by the generalized reduced gradient method (GRG) for 10-year time series from 8,226 Finnish firms. The coefficient of determination of the logarithmic model (CODL) is compared with the linear revenue-expense correlation coefficient (REC) that is generally used in previous studies.

Empirical evidence showed that REC is outperformed by CODL. CODL was found independent of or weakly negatively dependent on the matching elasticity of labor expense, positively dependent on the material expense elasticity and negatively dependent on depreciation elasticity. Therefore, the differences in matching accuracy between industries emphasizing different expense categories are significant.

The matching function is a general approach to assess the matching accuracy but it is in this study specified multiplicatively for three categories of expenses. Moreover, only one algorithm is tested in the empirical estimation of the function. The analysis is concentrated on ten-year time-series of a limited sample of Finnish firms.

The matching function approach provides a large set of important information for considering the matching process in practice. It can prove a useful method also to accounting standard-setters and other specialists such as managers, consultants and auditors.

This study is the first study to apply the new matching function approach.

The purpose of this paper is to explore the matching process before and after the Great Recession in the Netherlands. The Dutch case is interesting because it is characterised by increasing matching efficiency.

This paper uses data from 2001 to 2014 to study the Dutch labour market matching process accounting for the three labour market states and their heterogeneities.

The elasticity of hires with respect to the short-term employed was significant, positive and countercyclical, while elasticities relating to new entrants were procyclical. The matching function (MF) displays constant returns to scale (CRTS) when using an alternative labour supply (LS) measure that includes the short-term employed as jobseekers. The findings are at odds with the idea of mismatch and a shortage of skills. Search frictions for employers were lower and vacancies were filled faster. This can be related to the fact that in a loose labour market context with increasing short-term employment, employers increase their hiring of employed workers which generates negative externalities on unemployed.

The implications concern the specification of the MF and the CRTS assumption when using unemployment as a LS measure.

The purpose of this paper was to design ontology for describing semantic context‐aware quality services, and to present a new web management tool that provides a great flexibility and enables automatic semantic adaptation and customization of mobile client services.

The tool is developed using ontology‐based approach. This ontology captures a shared conceptual schema common in the tourism domain and maintains semantic quality information in heterogeneous service providers for service model. The results of the tool will be compared to prior works from other quality and distributed based service selection methods for mobile‐based application.

The tool support is based in the ontology Context‐aware Quality Semantic Web Service called (CxQWS). At the first step, services are defined as a set of semantic metadata, reflecting service requirements and QoS parameters. At the second step, services with a semantic contextual metadata are elaborated. Such a procedure ensures that the selection decisions should be based on the semantic quality representation of the created services. The SELETOR tool results suggest that the level of intelligent method use continue to be high flexibility in World Tourism organisations.

The tourism services in a mobile environment have a critical role in creating tourist satisfaction. They are neither a uniform group, nor able to give consistently high service quality. Indeed they have significantly different platforms and a variety of heterogeneous service providers which make the management of service qualities complex.

A significant proposition is to integrate new tourism quality attributes of mobile‐based application, to provide a dynamic adaptation of selection services based on context metadata parameters (user, environment, device, and service provider context) and the management of the heterogeneity of service needs, of mobile devices capacities and their various communication protocols (GPRS, WIFI, Bluetooth, etc.) as well as the media variety (sound, video, text and image), possibly reflecting the decreased time responses and the increased visibility of standard services management methods.

The paper proposes SELECTOR, a dynamic service selection tool based on CxQWS ontology, defined as set of semantic metadata, which context and QoS parameters. The tool is based on semantic services and offer architecture, with three layers (semantic query, management and web services). The most innovative characteristic of the tool is that it profits from the potential of semantic representation techniques to express high level explicit constraints, while they may be useful to guide the selection and adaptation process. This tool provides low adaptation effort, e.g. takes into account all the heterogeneous services as its various communication protocols (GSM, 3G, Bluetooth, etc.) as consequences of self‐selection for dynamic context evolution guided by the adaptation policies.

The successful provision of context‐awareness in pervasive environments requires the support of autonomic management facilities that provide ways to efficiently acquire and use contextual information. This paper claims that in order to offer viable context‐aware services, the issue of context imperfection and aging as well as the alignment of the context information that is used by a service with the customized service objectives should be taken into account. It presents an approach for managing the selection of context sources considering the freshness and actuality of the available information, and dynamically adapting to any source change and failure. Accordingly, there is no need to know beforehand the context sources to obtain the required information, but a quality‐aware discovery of the sources is envisioned. Finally, the proposed approach allows services to be ported easily to an environment with a different set of context sources.

The purpose of this paper is to analyze cloud reviews according to the end-user context and requirements.

propose a comprehensive knowledge base composed of interconnected Web Ontology Language, namely, modular ontology for cloud service opinion analysis (SOPA). The SOPA knowledge base will be the basis of context-aware cloud service analysis using consumers' reviews. Moreover, the authors provide a framework to evaluate cloud services based on consumers' reviews opinions.

The findings show that there is a positive impact of personalizing the cloud service analysis by considering the reviewers' contexts in the performance of the framework. The authors also proved that the SOPA-based framework outperforms the available cloud review sites in term of precision, recall and F-measure.

Limited information has been provided in the semantic web literature about the relationships between the different domains and the details on how that can be used to evaluate cloud service through consumer reviews and latent opinions. Furthermore, existing approaches are lacking lightweight and modular mechanisms which can be utilized to effectively exploit information existing in social media.

The SOPA-based framework facilitates the opinion based service evaluation through a large number of consumer's reviews and assists the end-users in analyzing services as per their requirements and their own context.

The SOPA ontology is capable of representing the content of a product/service as well as its related opinions, which are extracted from the customer's reviews written in a specific context. Furthermore, the SOPA-based framework facilitates the opinion based service evaluation through a large number of consumer's reviews and assists the end-users in analyzing services as per their requirements and their own context.

The purpose of this paper is to examine the effect of celebrity-advertising context congruence on transferring a celebrity’s image to a brand image.

This study investigates the effect of the advertising context, which is the background of the ad provided by the vehicle carrying it, in transferring a celebrity athlete’s image.

The results indicate that the presentation of a celebrity athlete in an advertising context that is congruent with the professional expertise of the athlete enables the more effective transfer of the athlete’s image to the brand’s image compared with the incongruent advertising context. In addition, the findings suggest that image attributes perceived as belonging to the professional expertise of an athlete are transferred more effectively by a context-congruent advertisement, while image attributes based on the sociocultural influence of an athlete show no difference in image transfer based on the advertising context.

Although the advertising context has been thought to influence advertising effectiveness, no specific research has thus far analyzed the relationship between image transfer and the advertising context in the sports marketing literature.

The purpose of this paper is to enhance understanding on supply chain partnership strategy-environment context co-alignment and its relationship with performance. Using the environment-strategy-performance view framework and the supporting relational perspective, the study develops a model and hypotheses to understand how supply chain partnership strategy as a response to co-align with operating context elements may impact operational and overall firm performance. Additionally, the study investigates the interrelationships among partnership strategy elements.

Data for testing the hypothesized relationships in the conceptual model was collected through a survey of managers in the Hoover’s database of US manufacturing firms. The survey sample included 115 responses from a wide variety of manufacturing forms.

Findings support the conventional wisdom relating collaboration to operational and financial performance. While product complexity associates with the “building block” resources, resource complementarity and resource specificity, technological turbulence relates significantly with only resource specificity. Interestingly, competitive intensity associates differentially with the resources – positive with resource specificity and negatively with resource complementarity. The results also reveal mediating influences of resource specificity and collaboration.

The research findings have to be considered in context. The moderate size, wide industry/firm diversity and robust research design notwithstanding, and the cross-firm nature can potentially obscure causal linkages. Besides, more comprehensive insights could be obtained by modeling the co-alignment of strategy with other factors in the operating context such as industry munificence, and market unpredictability.

Firms derive operational and financial performance benefits from close collaboration with partners since the operational enhancements from such relationships have customer service implications. Besides, the synergistic interrelationships among strategic partnership resources and their eventual impact on operational and financial performance is highlighted suggesting that firms develop a proper mix of unique and complementing set of resources and leverage them through collaborative behaviors. Importantly, the results provide a framework for managers to understand the criticality of aligning their resources with contextual elements to realize enhanced operational efficiencies, customer service, and financial benefits.

Much of the evidence on the rent generation capabilities in supply chain partnerships is still anecdotal and extant empirical research lacks adequate explanation. Thus this study offers an initial strategic response framework for an appropriate co-alignment of partnership resources with environmental context factors to realize operational benefits and overall financial performance. The framework answers the critical question: does a supply chain partnership strategy that matches “fit” or co-aligns with its critical operating environment context realize better performance? Additionally, it unravels the interrelationships among strategic partnership resources.

Deployment of knowledge management systems (KMSs) suffers from low adoption in organizational reality that is attributed to a lack of perceivable added value for people in actual work situations. Poor task/technology fit in the process of knowledge retrieval appears to be a major factor influencing this issue. Existing research indicates a lack of re-contextualizing stored information provided by KMSs in a particular situation. Existing research in the area of organizational memory information systems (OMISs) has thoroughly examined and widely discussed the topic of re-contextualization. The purpose of this paper, thus, is to examine how KMS design can benefit from OMIS research on approaches for re-contextualization in knowledge retrieval.

This paper examines OMIS literature and inductively derives a categorization scheme for KMS according to their strategy of re-contextualizing knowledge. The authors have validated the scheme validated in a multiple case study that examines the differentiatory value of the scheme for approaches with various re-contextualization strategies.

The classification scheme allows a step-by-step selection of approaches for re-contextualization of information in KMS design and development derived from OMIS research. The case study has demonstrated the applicability of the developed scheme and shows that the differentiation criteria can be applied unambiguously.

Because of the chosen case study approach for validation, the validation results may lack generalizability.

The scheme enables an informed selection of KMSs appropriate for a particular OMIS use case, as the scheme’s attributes serve as design rationale for a certain architecture or constellation of components. Developers can not only select from various approaches when designing re-contextualizaton but also come up with rationales for each candidate because of structured representation. Hence, stakeholders can be supported in a more informed way and design KMSs more effectively along organizational change processes.

The paper addresses an identified need for systematic characterization of KMS approaches and systems intending to meet the objectives of OMISs. As such, it allows streamlining further research in this field, as approaches can be judged according to their originality and positioned relative to each other.

The paper aims to resolve three major issues in location-based applications (LBA) known as heterogeneity, user privacy, and context-awareness by proposing an elastic and open design platform named OpenLS privacy-aware middleware (OPM) for LBA.

The paper analyzes relevant approaches ranging from both academia and mobile industry community and insists the importance of heterogeneity, user privacy, and context-awareness towards the development of LBA.

The paper proposes the OPM by design. As a result, the OPM consists of two main component named application middleware and location middleware, which are cooperatively functioned to achieve the above goals. In addition, the paper has given the implementation of the OPM as well as its experiments. It is noted that two privacy-preserving techniques at two different levels are integrated into the OPM, including Memorizing algorithm at the application level and Bob-tree at the database level. Last but not least, the paper shows further discussion about other problems and improvements that might be needed for the OPM.

Each issue has its sub problems that cause more influences to the OPM. Besides, each of the issues requires more investigations in depth in order to have better solutions in detail. Therefore, more overall experiments should be conducted to assure the OPM's scalability and effectiveness.

The paper hopefully promotes and speeds up the development of LBA when providing the OPM with suitable application programming interfaces and conforming the OpenLS standard.

This paper shows its originality towards location-based service (LBS) providers to develop their applications and proposes the OPM as a unified solution dealing with heterogeneity, user privacy, and context-awareness in the world of LBS.

A challenging task for context‐aware pervasive systems is reasoning about context in uncertain environments where sensors can be inaccurate or unreliable and inferred situations ambiguous and uncertain. This paper aims to address this grand challenge, with research in context awareness to provide feasible solutions by means of theoretical models, algorithms and reasoning approaches.

This paper proposes a theoretical model about context and a set of context verification procedures, built over the model and implemented in a context reasoning engine prototype. The verification procedures utilize beneficial characteristics of spatial representation of context and also provide guidelines based on heuristics that lead to resolution of conflicts arising due to context uncertainty. The engine's reasoning process is presented and it is shown how the proposed modeling and verification approach contributes in tackling the uncertainty associated with the reasoning task. The paper experimentally evaluates this approach with a distributed simulation of a sensor‐based office environment with unreliable and inaccurate sensors.

Important features of the model are dynamic aspects of context, such as context trajectory and stability of a pervasive system in given context. These can also be used for context verification as well as for context prediction. The model strength is also in its generality and its ability to model a variety of context‐aware scenarios comprising different types of information.

The paper describes a theoretical model for context and shows it is useful not only for context representation but also for developing reasoning and verification techniques for uncertain context.