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The purpose of this paper is to consider the key findings of a yearlong collaborative research project focusing on the London Symphony Orchestra’s development, implementation and testing of a branded smartphone app. This app was designed to primarily sell discounted tickets, engage and inform a student audience.

A mixed-method approach including an analysis of the technology, focus groups and interviews was used.

Though the aims of app developers and marketers are often to provide customers with more choice and interactivity, this research suggests that though the app proved a useful mechanism for selling discounted tickets, it indicates that existing customers were mostly enroled and mobilised via a limited and focused functionality for the app.

This paper is significant as mobile phone use remains comparatively under-researched, in particular there is still a relatively small literature on the growing phenomena of apps, and even less on their use in brand marketing. Also importantly, though this paper offers a consideration of one case, the app has since been expanded to include the ticketing for ten major orchestras in London, and moreover, many of the lessons learnt from this study will be of relevance to other arts organisations.

Waiting is associated with pain and stress that leads to frustration. However, consumer narratives may help cope with the stress associated with such waiting. This study aims to understand consumer waiting behaviours within online communities.

Data was gathered following a netnographic approach from a Facebook brand community (FBC) by downloading and archiving the threads related to members’ waiting behaviours. This resulted in 91 pages of data, with 438 individual comments and 179 distinct threads.

The data revealed that members of the sampled FBC exercised waiting behaviour. The authors confirm that waiting for a product is associated with both negative outcomes (frustration, boredom, etc.), but positive ones (create stronger ties with the brand and fellow members, etc.). Members of the FBC exhibited reduced consumer anxiety and stress during the waiting period.

This study found 13 waiting behaviours within the FBC and supports the idea that new value-creating behaviours are noticed within the context of FBCs.

This study focuses on waiting within a goods-based context (waiting to be served has been examined predominantly within the service sector). The study explored the behaviours of consumers who use social media to complain about extended waiting periods to receive the product along with other consumer reactions to these waiting crowds to reduce the emotional pain associated with such delays.

La espera se asocia con el dolor y el estrés, lo que lleva a la frustración. Sin embargo, el relato de otros consumidores puede ayudar a hacer frente al estrés asociado a dicha espera. Este trabajo busca entender el comportamiento de espera de los consumidores dentro de las comunidades online.

Los datos fueron recopilados siguiendo un enfoque netnográfico de una comunidad de marca en Facebook (CMF) mediante la descarga de mensajes; sólo los hilos relacionados con los comportamientos de espera de los miembros fueron descargados y archivados. Esto resultó en 91 páginas de datos con 438 comentarios individuales y 179 hilos distintos.

Los datos revelan que en Facebook se dan comportamientos de espera. Confirmamos que la espera de un producto no sólo está asociada a resultados negativos (frustración, aburrimiento, etc.), sino también a resultados positivos (crear lazos más fuertes con la marca y los compañeros). Además, se comprueba que los miembros de la CMF reducen la ansiedad y el estrés del consumidor durante el período de espera.

Este trabajo encuentra 13 comportamientos de espera dentro de una CMF

Este trabajo se centra en la espera dentro del contexto basado en los bienes (la espera para ser atendido ha sido examinada principalmente dentro del sector de servicios). Además, no sólo nos centramos en los consumidores que utilizan los medios sociales para quejarse de una espera más larga en la recepción del producto, sino también en la reacción de otros consumidores a estas esperas para reducir el dolor emocional asociado a dicho retraso.

The purpose of this paper is to explore the interdependencies between temporary and permanent aspects of project organizing and how they affect the management of public infrastructure operation and maintenance (O&M) activities.

The paper applies a case study approach and uses Lundin and Söderholm's (1995) framework of the temporary organization (with the themes of time, task, team and transition) to distinguish between temporary and permanent aspects of organizing two infrastructure O&M projects.

This paper adds to the literature on temporary organizations by recognizing a mixture of temporary and permanent aspects of project organizing in an empirical project-level example. In line with previous research, the themes of time, task, team and transition were shown to be interdependent. Furthermore, the paper broadens the theory of temporary organizations by presenting a project organization with significant permanent aspects.

Project managers of public sector projects need to be aware of the possible mixture of temporary and permanent aspects of project organizing. Management of projects that are found to have a mixture of temporary and permanent aspects should combine the perspectives and management practices of both temporary and permanent organizing. Not acknowledging permanent aspects could lead to management that is not adapted to the prerequisites of project organizing in this context.

The findings further develop the literature on temporary organizations by recognizing that there is not only a mixture of temporary and permanent aspects between the temporary organization and its permanent environment but there is also a mixture of temporary and permanent aspects of organizing within project organizations.

The electronics industry is a key industry in South Korea. To improve efficiency and maintain a competitive edge in fast-paced technological age it is critical to synchronize production supply with market demand. This paper focuses on the strategies which make the “just in time” production system within the electronic supply chain. The presented case study of Samsung Electronics Thin Film Transistor-Liquid Crystal Display (TFT-LCD) Inbound Logistics provides a solid example of process integration in the areas of: packaging, transportation and information sharing. The problems faced by Samsung Electronics in the early 2000’s were a direct result of an imbalance between increased traffic/demand and existing production capacity. The most detrimental of these problems caused delays in supply, shortage of space in the hub center and the bottlenecking of supply transportation. This paper demonstrates how Samsung Electronics reexamined and systematically streamlined their existing processes and how they successfully integrated “lean thinking” strategies to all areas of TFT-LCD production. As a result of the integration of these strategies, Samsung Electronics began using a line-to-line packing system which focused on packing items in a group using a standardized, returnable packing system. Additionally, they opted to replace their paper invoice system for an improved transportation system through which a data could reflect flows of information and materials in real time. The integration of these strategies gave Samsung Electronics a new approach to TFT-LCD Inbound Logistics which allowed them to overcome their dilemmas and resulted in successful outcomes, decreased lead times, higher quality products and an reduction in total costs.

This research aims to identify the optimal configuration of investment which leads firms to their best competitive positions, considering the degree of concentration in the market.

The methodology was quantitative and based on secondary data with samples of 124, 106 and 90 firms from competitive environment classified as perfect competition, monopolistic competition and oligopoly, respectively. Proposed models' parameters were estimated by means of genetic algorithms.

Adjustments on firm's investment are contingent on the degree of competition they face. Results are in line with existing academic research affirmation that the purpose of investments is to create and exploit opportunities for positive economic rents and that investments allow firms to protect from rivals' competitive actions and reinforce the need for investment decision makers to consider the environment in which the firm is competing, when defining the amount of investment that must be done to achieve and maintain a favorable competitive advantage position.

This research brings two main original contributions. The first one is the identification of the optimal amount of capital and R&D investments which leads firms to their best competitive positions, contingent to the degree of concentration of the competitive environment in which they operate, and the size of the firm. The second one is related to the use of genetic algorithms to estimate optimization models that considers the three competitive environments studied (perfect competition, monopolistic competition and oligopoly) and the investment variables in the linear and quadratic forms.

This paper measures different market risk impacts on options portfolios under the new Fundamental Review of the Trading Book (FRTB) regulation, issued in Basel and coming into effect in 2023.

This paper first suggests an algorithm for implementing the FRTB standardised approach via the sensitivities-based method to estimate a portfolio's risk capital and presents an illustration applied to an option position. Second, it proposes a methodology to estimate the expected shortfall in options portfolios from the FRTB internal models approach. In this regard, an application is developed to measure expected shortfall (ES) and value at risk (VaR) impacts under FRTB versus conventional VaR in a currency option position by considering stress scenarios from the 2007–9 and 2020–1 crises and back-testing procedures.

The suggested algorithm satisfactorily captures impacts via the sensitivities-based method, and higher risk capital demands are expected for emerging economies. Also, the planned FRTB methodology to measure ES and VaR is appropriate; in particular, historical metrics perform well. Astonishingly, their revealed impacts are more significant under the 2020–1 pandemic crisis than the 2007–9 financial crisis.

The proposals developed weave a communication bridge between the standardised and internal approaches of FRTB regulation, which can be scaled up technologically and institutionally.

The main aim of this paper is to examine the claim that economic value added (EVA) advocates its superiority over the traditional accounting-based financial performance measures, i.e. profit after tax (PAT), earnings per share (EPS), return on assets (ROA), return on equity (ROE) and return on investment (ROI) in the Indian manufacturing sector and at the same time, give empirical facts. It also tests and examines the information content of various performance measures and their relationship with stock returns.

The paper uses the sample of 534 Indian manufacturing companies from the Bombay Stock Exchange (BSE) during the period 2000–2018. Multiple regression models are applied to examine the information content of EVA and traditional performance measures in explaining shareholders’ returns.

Relative information content tests revealed that traditional accounting-based measures such as EPS, ROE and ROA performed better than EVA in explaining the returns of Indian manufacturing companies. Incremental information content of EVA adds little contribution to information content above traditional performance measures. The claim of superiority of EVA over accounting-based measures in association with shareholder returns is proved invalid in Indian manufacturing companies.

This study concludes that EVA has no superiority over traditional accounting-based financial performance measures in explaining stock returns of Indian manufacturing companies. To achieve heftiness in outcomes, panel data are tested by using Breusch–Pagan–Godfrey (BPG) test for heteroskedasticity, Hausman’s test for fixed and random effect, variance inflation factor (VIF) test for multicollinearity and Durbin–Watson test for autocorrelation.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

The paper aims to propose a practical model for market segment selection and evaluation. The paper carries out a technique of order preference similarity to the ideal solution (TOPSIS) approach to make an operation systematic dealing with multi-criteria decision- making problem.

Introducing a multi-criteria decision-making problem based on TOPSIS approach. A new entropy and new similarity measure under neutrosopic environment are proposed to evaluate the weights of criteria and the relative closeness coefficient in TOPSIS model.

The outcomes show that the TOPSIS model based on new entropy and similarity measure is effective for evaluation and selection market segment. Profitability, growth of the market, the likelihood of sustainable differential advantages are the most important insights of criteria.

This paper put forward an effective multi-criteria decision-making dealing with uncertain information.

The impact of the intellectual capital disclosure (ICD) on the cost of equity capital (COEC) is not well established in the aspect of the Indian scenario. So the objective of this paper is to examine not only the overall effect of ICD but also the individual effect of human capital disclosure (HCD), relational capital disclosure (RCD) and structural capital disclosure (SCD) on COEC.

This research work is conducted by regressing COEC, firm size, leverage, industry type and disclosure index. The disclosure index is prepared based on content analysis of disclosure made in the annual reports of a sample of 50 companies listed in the Nifty 50 index for the year 2018–2019. But in this paper 20 companies are eliminated due to their negative COEC and rest 30 companies are used as the sample companies for this study.

The outcome of this study indicates a negative association between the disclosure of intellectual capital (IC) as a whole and the COEC. But a negative association only for two components (human capital and structural capital) with the COEC is found only when the association of COEC with the categories of ICD is considered.

This is the first study that examines the nexus between the level of ICD and its impact on the COEC in India context.