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The purpose of this paper is to identify if peer firms’ capital structure decision plays a role in determining focal firms’ capital structure decision, despite the fact that correlated effects can also lead to co-movement of financing behavior among firms from the same industry (i.e. industry-specific capital structure).

Instead of using relative measurement (of individual outcome variable over industry variable) as in previous work, this paper borrows the linear-in-means model and, after controlling for potential endogeneity problems, directly identifies the existence of peer effects with coefficient estimation. To deal with correlated effects, additional empirical investigations such as test of heterogeneity in direction and scale, social multiplier identification test and instrumental regression test based on another instrumental variable (that is less influenced by correlated effects) are performed.

Using data from Chinese listed firms, this paper, for the first time, identifies the presence of peer effects in capital structure and debt maturity decision. Further investigations show that first, focal firms react asymmetrically to peer firms’ debt adjustment of different direction and scale. Second, social multiplier, a unique attribute of peer effects, is identified in the leverage choices. Third, the significant correlation of capital structure decision remains even if we use another “correlated effects-immune” instrument. All these results point to the fact that peer effects, rather than correlated effects, play a significant role in determining capital structure.

The empirical results of this paper provide strong evidence that firms, driven by motivations such as either learning or competition, will actively react to peers’ financial decisions. As the bridge between individual firms and the industry, social multiplier can be fully taken advantage of to induce positive spillover of good management practices and prohibit inefficient decisions from spreading.

This paper theoretically and empirically introduces peer effects – a well-acknowledged social concept – into capital structure decision of Chinese listed firms, thus both complementing the traditional capital structure theory and providing an empirical paradigm for peer effects research.

This article aims to describe a qualitative, exploratory study with the objective of developing scenarios for the road freight transport industry in Brazil and evaluating the effectiveness of the method applied, which used the stakeholders of said industry as a means to identify the variables of the scenarios.

According to the classification scheme developed by Huss and Honton, the authors' method fits into the intuitive logics approach to scenarios, employing concepts of stakeholder analysis as proposed by Freeman. Primary data collection was conducted through key informant interviews, as outlined by Fetterman. The use of the method of intuitive logics combined with the stakeholder analysis evaluates the consistency of experts' opinions on the characteristics of stakeholders. Four environmental scenarios, distinct but equally plausible, were generated for the road freight transport industry as it was felt that more than four scenarios tends to be too complex.

The method applied produced scenarios distinctive enough to classify them as contrasting, accounting for macroenvironmental variables and variables determined by influential stakeholders in the analyzed industry. Organized and connected, these variables produced precise end states that warrant consideration in the policies and strategies of industry players. The characteristics of the scenarios produced reveal that the method was effective. The authors found the most influential stakeholders in the industry to be the government, shipping clients, end consumers, logistics service providers, and trade associations. The industry's main uncertainties are tied to how the actions of government, shippers, and logistics service providers will unfold.

Some limitations could be identified in the method. One refers to the absence of procedures to govern the chronology of events at the time of preparation of scenario plots. Another shortcoming is the third and final stage of the research; the authors observed some weakness in the method when defining a variable that is independent because it can be independent of the variables selected for the last step but dependent on others considered but not selected.

The results of the study can stimulate reflection of stakeholders on factors that will affect their decision making, stimulate understanding of the conditions for sustainability of the industry, and identify business opportunities and necessary strategic resources for the success of organizations in the future.

The transport industry plays a vital role in factors that are paramount for the economical development of a country, such as exploration of resources and mass production, and, in Brazil, road freight transport is of particular importance. The research can guide public policy in regulating and investing in industry, since the plots facilitate the understanding of the consequences of causal relationships as well as the final states resulting from these. The scenarios reveal causal relationships strongly influenced by the stakeholder “government”, especially regarding investment in infrastructure, regulation and supervision of the industry.

Application of the method proposed by Boaventura and Fischmann to the road freight transport industry generated distinct, but equally plausible scenarios. The method considered the key uncertainties as dichotomous variables. The scenarios were different since combinations of final states of the key uncertainties led to a different logic or rationale. The authors may state that this particular application contributed towards improvement of the method, as it tested the method's logic when applied to a complex environment influenced by many stakeholders.

Accounting studies have attempted to forecast future attributes of firms' financial statements, primarily earnings. These studies typically adopt a cross‐sectional approach in estimating forecasting models, combining firms from different industries in the same model. This cross‐sectional approach implicitly assumes the relations between earnings and the explanatory variables are consistent across industries.

This paper looks at the importance of forecasted information as a key input to investors decision models. The research design uses accounting variables suggested by financial accounting theory, industry variables and economic variables. Analysis of the data indicated that only return on investment yield and capital intensity were associated with earnings. In an environment of rapidly changing economic conditions and attendant uncertainty, the scrutiny of forecast accuracy has is crucial. For a firm, the allocation of resources is based upon forecasts of financial information that may affect its survival. Earnings and dividends forecast, and the growth rates in these forecast are key informational inputs in investor decision models (Chang and Most, 1980). In addition, Securities and Exchange Commission, recognizing the importance of this subjective and non‐verifiable information, permits and encourages firms to include financial forecasts in their annual reports by granting them a “safe harbor”. The accounting profession responded to this demand for forecasted information by producing audit guidelines for these forecasts. Accounting‐based financial forecasts are used in a variety of ways. Banks and non bank financial institutions use forecasts to evaluate credit. Earnings forecasts are useful to financial analysts who attempt to isolate a firm's intrinsic characteristics such as residual income after removing the effect of economy and industry conditions through the use of index models. Auditors use accounting forecasts as a basis for expectations concerning reported items to determine the extent of detailed tests (Stringer, 1975; Kinney, 1978). Lev (1980, p.525) stated that the “… crucial stage of the analytical review process is the generation of expected, or reasonable, values of financial statement items.” Managers use internally generated earnings forecasts for resource allocation decisions concerning present operations as well as future operations and expansions/contractions.

The purpose of this paper is to identify the shareholder value creation measure best suited to express shareholder value creation for a particular industry.

The analysis was performed on 192 companies listed on the Johannesburg Stock Exchange, classified into nine different samples or industries. Five shareholder value creation measures were examined, namely market value added (MVA), a market-adjusted stock return, the market-to-book ratio, Tobin’s Q ratio, and the return on capital employed divided by the cost of equity.

An analysis of the nine categories of firms led to the identification of different measures that are suited to express value creation. Stock returns did not provide an appropriate value measure. Instead, depending on the specific industry, Tobin’s Q ratio, MVA, and the market-to-book ratio should be used to measure and express value creation.

For management, the value drivers identified for each industry present a clear indication of industry-specific variables upon which they can focus in operating activities to most efficiently increase shareholder value.

Unlike previous studies that use only one or two different shareholder value creation measures as dependent variables, this study uses five different value creation measures. Another contribution of this study is the compilation of a unique set of value drivers that explain shareholder value creation separately for each of the nine different categories of firms.

Prior studies on determinants of shareholder value creation have reported conflicting and sometimes confusing results. In this study, to obtain more refined and industry-specific results regarding variables determining shareholder value creation, an analysis was performed focusing on different categories of firms or industries.

Two dependent and 11 independent variables were applied to five different industries to obtain the best set of significant value drivers of shareholder value creation for a particular industry.

Market value added (MVA) is a better indicator of shareholder value created compared to a market adjusted return. Accounting-based variables (EPS, ROA and NOPAT) are superior to economic-based variables (EVA and ROCE) in explaining shareholder value creation, but results differ, depending on the dependent variable chosen as shareholder value creation measure. For each industry, there is a unique set of variables that determine shareholder value creation; the industrial goods industry has seven significant value drivers, namely, EPS, NOPAT, ROCE, the Spread, EVA, EBEI and REVA, whilst for the food and beverages industry, there were only two significant value drivers (EPS and ROA).

These findings imply that management, analysts and shareholders should, depending on the specific industry in which their firm operates, take into account a more specific set of variables when making their financial decisions, including compensation or reward structuring.

The purpose of this paper is to identify organizational and industrial characteristics of publicly traded Brazilian firms with sport and cultural sponsorships.

Secondary data, as organization variables and industry level variables, were sourced from Economatica®. The data were analyzed using logistic regression.

Both size and asset profitability were associated with a sponsorship strategy. Both industry concentration and company size are positively related to both cultural and sport sponsorship strategies.

The findings in this paper provide support to resource-based view and SPC theories. The notable limitation of the study is the reliance on non-standardized social reporting.

This paper fulfills an identified need to study the importance for sponsorship to companies’ performance. The adoption of sponsorship strategies has been growing in Brazil and becoming more and more important for sponsor companies’ performance and in developing these industries, sport and creative. Through culture and sports, companies try to add value to their brands, delivering a socially responsible image to the audience.

In the process industries, it is essential to have a well-articulated manufacturing strategy within companies. However, to facilitate manufacturing strategy development, it is important to start with a good characterisation of the material transformation system and company production capabilities. The paper aims to discuss these issues.

A grounded theory approach, with inspiration from configuration modelling, attempted to characterize the material transformation system as a set of variables. The variable development was based on a literature review and the knowledge base of five industry experts. Two exploratory mini-case studies were carried out, primarily to illustrate the use of the model, but additionally to test its industrial usability.

A set of 31 variables was developed, and related measures and scales were tentatively defined. Two mini-cases supported the usability of the model. The model, focussing on company generic process capabilities, is a conceptual taxonomy and the study’s theoretical contribution.

The lucidity of the definitions and scales for the variables are open to further refinement, and the limited discussions of variable relationships in this study are addressed in an agenda for further research.

The model can be deployed as a facilitative instrument in the analysis of company material transformation systems and may serve as a platform in further discussions on companies’ strategy development.

The model is a new instrument for analysing company generic process capabilities and an effort to build new theory rather than to test an existing one.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.