Search results

Most entrepreneurs are continually concerned about their finances. Their companies perhaps not yet profitable, they may have a fear of “running out of dry powder.” These entrepreneurs often have fallen in love with their company's technologies, products, and potential markets, but they require more resources. Invariably these emerging ventures shroud their fear of the grueling capital raising marathon by presenting voluminous business plans to potential investors. They often flaunt their “optimized business models.”” Investors, however, typically want to know why the potential investment is such a good deal. The entrepreneur often wants guidance regarding what to say to whom in a changing financing environment.

In this article, our “Practitioner's Corner” associate editor Joe Levangie collaborates with a long-time colleague Paul Broude to address how businesses should “make their capital-raising initiatives happen.” Levangie, a venture advisor and entrepreneur, first worked with Broude, a business and securities attorney, in 1985 when they went to London to pursue financing for an American startup. They successfully survived all-night drafting sessions, late-night clubbing by the company founder, and even skeet shooting and barbequing at the investment banker's country house to achieve the first “Greenfield” flotation by an American company on the Unlisted Securities Market of the London Stock Exchange. To ascertain how the entrepreneur can determine what financing options exist in today's investing climate, read on.

The purpose of this study is to develop a novel general mathematical model to find the optimal product mix of commercial graphite products, which has a complex production process with alternative sub-processes in the graphite mining production process.

The network optimization was adopted to model the complex graphite mining production process through the optimal allocation of raw graphite, byproducts, and saleable products with comparable sub-processes, which has different processing capacities and costs. The model was tested on a selected graphite manufacturing company, and the optimal graphite product mix was determined through the selection of the optimal production process. In addition, sensitivity and scenario analyses were carried out to accommodate uncertainties and to facilitate further managerial decisions.

The selected graphite mining company mines approximately 400 metric tons of raw graphite per month to produce ten types of graphite products. According to the optimum solution obtained, the company should produce only six graphite products to maximize its total profit. In addition, the study demonstrated how to reveal optimum managerial decisions based on optimum solutions.

This study has made a significant contribution to the graphite manufacturing industry by modeling the complex graphite mining production process with a network optimization technique that has yet to be addressed at this level of detail. The sensitivity and scenario analyses support for further managerial decisions.

The theory presented suggests that underwriters are both advisors and independent agents in the issuerʼs attempt to send “signals” of quality to investors by making pre-IPO organizational changes. These pre-IPO gambits are intended to increase IPO proceeds, and preemptively address potential investor concerns that would deter them from subscribing. These organizational changes initially can financially benefit founders, early investors and underwriters. But they can also have a longterm impact that some issuers, especially founders, would prefer to avoid. Utilizing signaling and resource-based power, we find that underwriter power is significantly associated with making pre-IPO gambits and lower levels of underpricing.

The study seeks to find the effect of financial leverage on the firm performance of non-financial companies listed in the Tokyo stock market.

The study collected data from 263 companies in the automobile and industrial producer sectors listed on the Tokyo stock exchange between 2001 and 2021. The generalized method of moments was used to estimate the effect of leverage on financial performance due to its ability to overcome the problems of endogeneity and autocorrelation.

The study found that the equity multiplier has a positive and statistically significant effect on return on assets (ROA), return on equity (ROE) and earning per share (EPS). The study discovered that the interest coverage ratio has a positive and statistically significant effect on ROA, ROE, EPS and Tobin’s Q. The results revealed that the degree of financial leverage and debt to earnings before interest, taxes, depreciation and amortization (EBITDA) have a negative and statistically significant effect on ROE, EPS and Tobin’s Q. The study also found that the capitalization ratios of the firms have a negative and statistically significant effect on ROA, ROE, EPS and Tobin’s Q.

The use of debt financing, which presents financial leverage, indicates that the companies can make enough earnings to pay off the interest and principal (debt service obligations), which were shown by the interest coverage ratio, as well as to pay all the long-term fixed expenses, which were shown by the fixed charge coverage ratio. Interest and fixed charge coverage have a positive statistically significant effect on the financial performance of automobile and industrial producer companies.

The study focused on the effect of financial leverage on financial performance by relying on pecking and trade-off theories to contribute to the existing body of literature in finance.

This paper aims to analyze how knowledge networks can be configured within a value chain and provide evidence of the coexistence of multiple knowledge networks in the same value chain.

The empirical setting is the Conegliano Valdobbiadene Prosecco Superiore DOCG wine cluster in the Veneto region of Northeast Italy. Data was collected through the administration by telephone of a semi-structured questionnaire to 37 oenologists, sales managers, production managers and owners of bottling companies in the district. The authors used social network analysis tools to map knowledge networks in the Prosecco cluster.

The results shed light on the importance of singling out knowledge networks in clusters at the value chain level to aid practitioners and researchers in this field. In fact, this research proves the existence of knowledge networks specificities related to the various phases of the production process.

This study has certain limitations. The most relevant is connected to the choice to limit the analysis to a specific cluster. Future research might extend this type of analysis to multiple clusters in different locations.

The authors explain that in the cluster they studied, internationalization, as a common objective, might be made easier if firms could establish a more developed sales knowledge network.

The relational approach to value chain enables disentangling specific roles of each actors. The social dimension of the value chain is taken in consideration.

The authors show that a firm operating in the wine industry can have different knowledge networks in the same value chain. This work adds to previous literature on knowledge networks in clusters by shedding light on an important, but still understudied aspect in the cluster functioning. Knowledge diffusion in clusters is not only uneven but is also value chain stage specific. By intersecting literature on knowledge networks, value chain and cluster research, the authors proposed a new perspective of analysis of the wine industry.