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This study compares those interim disclosures that managers desire to make with those they are required to make. Managers and regulators agree on the optimal degree of disclosure on growth potential and size. It appears that the less managers voluntarily disclose, the greater the firm's growth potential. This may be because managers feel that other evidence signals the good future prospects or the information indicating positive growth is too proprietary to reveal to competitors. Some differences are observed. Managers would pay more attention to the specific needs of their governance groups. Regulations would require more disclosure of variables indicating: business risk; capital structure; and growth. These differences in perceived need for disclosure highlight the importance of continued study of the optimal scope and scale of disclosure.

For several branches of industry, an increasingly competitive environment has raised important questions concerning maintenance in plant systems. For example, the 1996 deregulation in Sweden’s electricity sector has resulted in increased competition among the country’s power producers. To survive the competition, suppliers have to reduce maintenance costs, i.e. handle maintenance more efficiently. Risk analysis is one tool decision makers can use to help them prioritise as they plan maintenance actions. There are a number of different approaches to risk analysis. As the results of an analysis must form a reliable basis for decision making, it is important to consider whether the quality of the results will vary significantly with the risk analysis approach chosen. This paper presents a comparative study based on three independent risk analyses performed on a specific hydro‐power plant. The comparison and evaluation of the analyses reveal major differences in performance and results, along with various factors that affect the quality of the analyses. The study establishes the importance of a well‐planned requirement specification and the need to analyse and interpret risk analysis results, before making maintenance decisions.

Globally, there are more than 16 stock markets that are undergoing drastic enough growth to be considered emerging. These markets possess characteristics very distinct from those which may be characterised as mature. This paper compares interim reporting regimes in two markets with these different characteristics. The regulatory environment within the USA represents a mature market, and the Finnish regulatory environment depicts an emerging market. First, historical developments are detailed for both of these contexts. Similarities and differences in the regulation are systematically pointed out. Second, the potential consequences of some differences in regulatory schemes are discussed. Special emphasis is placed on the influences these variations have on both legislators and the producers of interim reports. This paper should offer valuable insights for the regulation of a large number of emerging markets.

The purpose of this paper is to create an organized picture of the current understanding of social media-based value creation and business models.

Following the process model presented by Fink (2005), a systematic literature review of academic journal articles published between 2005 and 2014 was conducted. The research was grounded on the theoretical foundations of service-dominant logic.

This study offers detailed descriptions and analyses of the major social media mechanisms affecting how value is created in social media-based value networks and the kinds of impact social media can have on present and future business models.

The study is limited to academic research literature on business organizations, excluding all studies related to public and non-profit organizations.

Attention is given to developing an in-depth understanding of the functions and concrete value creation mechanisms of social media-based co-creation within the different organizational processes (e.g. in product and service development and customer services) and to updating the related practices and knowledge.

This study provides new insight into the challenges related to research models and frameworks commonly used for observing value creation, thus highlighting the need for further studies and updates.

This study aims to examine the effects of perceived product–brand fit and brand type on consumer evaluations of wearable smart masks’ technological, aesthetic and social attributes and how these affect consumers’ attitudes and intentions to use.

Through an experimental approach, a total of 240 US consumers’ evaluations of smart masks are compared according to perceived product–brand fit (high vs low) and brand type (electronics vs fashion).

The results showed that high perceived product–brand fit increases consumers’ evaluations, while brand type did not significantly affect consumers’ evaluations. Among various attributes, social acceptability had the greatest influence on consumers’ attitude and intention to use. Perceived ease of use, however, positively influenced attitude but negatively influenced intention to use.

As consumers’ interest in smart health-care wearables increases and air pollution is a serious issue across countries, research on wearable smart masks is being facilitated. Smart masks refer to the digitalized, reusable wearable masks that provide protection and health-care functions. However, their market penetration is still limited. To close this gap between smart mask technology and the market, this study examines how perceived fit and brand type can be used to enhance consumer evaluations.

This paper aims to present an augmented subjective risk management process that can partially solve the problem of inconsistent results in qualitative risk management. Note that “subjective” in this context is to separate this risk management process from the purely statistical risk management processes often associated with finance.

The approach has been developed by logically approaching the problem of inconsistency and, based on experience and literature review, conceptually designing a solution.

The presented approach seems workable and indeed an improvement over standard qualitative approaches given reasonable skillful implementation.

The approach has not yet been tested in real life, which must be undertaken in the future.

The presented approach will expand the subjective risk management process to include information management and to some extent knowledge management and thus add some more activities to the practice of risk management.

The paper presents an approach that so far seems useful in improving the consistency of subjective risk management. Also, it may be a valuable point of departure for further research.

The article sets out to discuss and present a solution to the fact that various qualitative risk analyses of the same problem can reach significantly different conclusions.

By reviewing a common risk analysis approach and identifying where the possible problems arise, the authors propose ways to overcome the problems based on what they have found in the literature in general.

There are ways to greatly reduce the problems, but this requires a risk analysis approach in which information quality and consistency are the subject of greater focus.

The definitions used, Monte Carlo methods and the analytical hierarchy process are well tested in countless applications. Hence, the authors believe that this work possesses no major limitations.

The approach has only been applied to theoretical situations; real‐life situations are needed to address possible practical limitations.

The paper illustrates the importance of distinguishing between “uncertainty”, “risk” and “capabilities” and the associated implications. It also shows how this can be done in a logically consistent way using the analytical hierarchy process so that the problem of inconsistency is reduced, and how the analysis can be used to systematically improve itself. The proposed risk analysis is a novel approach that has, to the authors' knowledge, never been thought of before.

The paper aims to show that active labour market policies in Scotland over a nine‐year period have failed to meet key policy objectives.

The paper uses the UK national online manpower information system (NOMIS) to conduct a detailed statistical analysis of the spatial differentials in exits from and entry to unemployment across 72 Parliamentary constituencies. The analysis is conducted by reference to the concepts of convergence, NAIRU and hysterisis.

The findings suggest a presence of hysterisis and absence of spatial convergence such that some areas in Scotland have become worse off in terms of the risk of unemployment and despite active labour market intervention.

Future research needs to be undertaken at the micro‐spatial level to confirm these findings and to focus on the weaknesses in the design of active labour market policies.

Active labour market policies in Scotland do not work in terms of reducing the risk of unemployment. Policy needs to focus on creating demand for labour rather than an almost exclusive reliance on “promoting” the supply‐side.

The key contribution of this paper is that it is the first to provide a detailed analysis of the Government's own data on unemployment distribution – it should be of value to both academicians and policy makers in terms of both analytical approach and policy design.

In the present time, there are large databases with parameters related to the maintenance of different equipment and installations. Given that manual analysis of sensors connected to machines is practically impossible, maintenance decisions from these databases can be difficult if the information automatically updated from these sensors is huge. Those great amounts of information are essentially useless if the knowledge contained inside cannot be extracted. Rough set theory facilitates this work by detecting those parameters that are truly significant for establishing the decision rules of the maintenance. In order to show the power of rough sets this paper contains a real case of a plastic injection installation for the analysis. Practical implications. An effective use of resource allocation in manufacturing processes could be achieved by using certain decision rules to indicate where and when maintenance decisions and tasks should be undertaken. This paper illustrates how the powerful theory of rough sets handles these issues. Therefore, the use of this technique is highly recommended for those industrial processes with a great amount of data and time (or in general, any resource) limitations.

The purpose of this paper is to identify the productivity improvement potentials from maintenance planning practices in manufacturing companies. In particular, the paper aims at understanding the connection between machine criticality assessment and maintenance prioritization in industrial practice, as well as providing the improvement potentials.

An explanatory mixed method research design was used in this study. Data from literature analysis, a web-based questionnaire survey, and semi-structured interviews were gathered and triangulated. Additionally, simulation experimentation was used to evaluate the productivity potential.

The connection between machine criticality and maintenance prioritization is assessed in an industrial set-up. The empirical findings show that maintenance prioritization is not based on machine criticality, as criticality assessment is non-factual, static, and lacks system view. It is with respect to these finding that the ways to increase system productivity and future directions are charted.

In addition to the empirical results showing productivity improvement potentials, the paper emphasizes on the need for a systems view for solving maintenance problems, i.e. solving maintenance problems for the whole factory. This contribution is equally important for both industry and academics, as the maintenance organization needs to solve this problem with the help of the right decision support.