Capturing behavioural outcomes through branded applications: the perspective of the investment model

PurposeNumerous companies have launched branded applications to foster consumer–brand relationships. Due to fierce competition among branded apps, the retention rate is quite low. The facilitation of behavioural outcomes through branded apps is a highly relevant research area. This paper investigates the drivers of behavioural outcomes in the context of branded apps from an investment model perspective.Design/methodology/approachThis work examines various branded apps primarily used by consumers in disparate product categories, namely, Target, Walmart, Under Armour, Nike, Pandora, Spotify, Starbucks, Burger King, Disney and Netflix. Four hundred and one valid online questionnaires were obtained and partial least squares structural equation modelling was used for data analysis.FindingsThe results obtained extend the investment model to the context of branded apps and show that app investment size and app satisfaction facilitate brand relationship commitment, successively enhancing app continuance intention, brand purchase intention and app word-of-mouth (WOM) intention. Furthermore, app confidence benefits and self-enhancement benefits facilitate app satisfaction, while app social benefits and special treatment benefits facilitate app investment size.Originality/valueThe present work applies an investment model to various branded apps to show how relationship components facilitate behavioural outcomes. We contribute to the literature by identifying four types of app relational benefits as drivers of relationship components in the context of branded apps.

What differentiates angel investors in pre-seed versus seed-stage investments? Evidence from India

Purpose Angel investments are increasingly getting specialized. In recent years, start-ups are raising pre-seed funding before seed-stage funding. Investors in pre-seed and seed-stage companies commonly are angel investors. The purpose of this paper is to understand the differences between these two groups of angel investors. Design/methodology/approach Data for this study obtained from angel funding deals from the sources such as Venture Intelligence, VCCEdge, Keiretsu Forum, Dealcurry and The Chennai Angels. A total of 732 angel investments made by 405 investors during 2014–18 were used in the analysis. Non-parametric tests and regression estimations were used to identify the differences between angel investors investing in pre-seed and seed-stage ventures. An index was developed to measure the extent of syndication in angel investments and used as an independent variable in the regression. Findings There are significant differences between angel investors investing in pre-seed and seed-stage ventures. The results show that angels with more industry-specific experience make a higher proportion of investment in seed-stage ventures. Seed-stage ventures attract investors from Tier-1 cities, whereas the pre-seed stage has higher investors from smaller cities. Though the investment size is smaller, the extent of syndication is greater in pre-seed stage investments. Originality/value To differentiate the angel investments between pre-seed and seed-stage funding, this study uses data from Indian start-ups. Further, this study develops a composite syndication index to measure the extent of syndication in angel investments and assesses its impact on an angel investor’s choice of pre-seed stage investments.

Family control and influence on JV investment – the moderating effect of JV type and IC components

PurposeThis paper aims to investigate the effect of the nature of ownership and board characteristics on the investment choices in joint ventures (JVs) from the dimensional point of view, controlling for the effect of JV type and other components of intellectual capital.Design/methodology/approachThe authors study a sample of Italian, Spanish, German and French nonfinancial listed firms over the 2010–2018 period, controlling for the fixed effects of the company's sector of operation and the year. The authors also analyze the effect of family control and influence on JV investment size, taking into consideration certain board characteristics, the type of JV, human capital efficiency, structural capital efficiency and capital employed efficiency while also controlling for a firm's profitability and size. To test the hypotheses, GLS panel data was used.FindingsThe results indicate that the size of the investment in JVs is smaller for family firms than for nonfamily businesses. The presence of CEO duality has an opposing effect on the size of the investment in joint ventures as it has a lowering effect in family businesses while it exerts an amplifier influence in nonfamily businesses. Moreover, the type of joint venture has a significant effect for family firms: the choice of a link joint venture reduces the size of the investment. The authors find that human capital efficiency increases JV investment size for all firms.Originality/valueThis study is the first to analyze the effect of the main dimension of socioemotional wealth – family control and influence – on a firm's JV investment size. It controls for the effect of JV type – link or scale – and the interplay of the other IC components.

Antecedents of Stage-wise Investment Preferences of Venture Capital and Private Equity Firms in India: An Empirical Exploration

This article is a maiden attempt at exploring determinants of stage-specific investment choices of Indian venture capital and private equity (VCPE) firms. Analysis of 5,782 VCPE investment deals during 1998–2016 shows that firms’ preferences to invest in various stages (early vs. late) are significantly affected by the characteristics of the VCPE firms, features of the deal, and characteristics of the investee firms. More specifically, experience and ownership (foreign vs. domestic) of VCPE firm, type of deal (syndicated or otherwise), investment size of the deal, and location and industry of the investee firm influence the stage of investment. Detailed empirical analysis shows that younger VCPE firms and those with domestic investors prefer to invest in early stages, presumably because they wish to build a reputation and also leverage their proximity with investee firms to manage high market and technological risks associated with early-stage investments. Syndication is another mechanism used to manage the risks associated with early-stage deals. Investee firms in industries that have lower investment requirements or shorter gestation periods and those located in regions with a mature entrepreneurial ecosystems are more likely to attract early-stage investments. JEL Classification: G24, L26, D81

Competitive Investment with Bayesian Learning: Choice of Business Size and Timing

Curse of a Favorable Opportunity: Strategic Choice of Investment Size and Timing with Bayesian Learning

Zero-Debt Policy under Asymmetric Information, Flexibility and Free Cash Flow Considerations

We build a model of debt for firms with investment projects, for which flexibility and free cash flow problems are important issues. We focus on the factors that lead the firm to select the zero-debt policy. Our model provides an explanation of the so-called “zero-leverage puzzle”. It also helps to explain why zero-debt firms often pay higher dividends when compared to other firms. In addition, the model generates new empirical predictions that have not yet been tested. For example, it predicts that firms with zero-debt policy should be influenced by free cash flow considerations more than by bankruptcy cost considerations. Additionally, the choice of zero-debt policy can be used by high-quality firms to signal their quality. This is in contrast to most traditional signalling literature where debt serves as a signal of quality. The model can explain why the probability of selecting the zero-debt policy is positively correlated with profitability and investment size and negatively correlated with the tax rate. It also predicts that firms that are farther away from their target capital structures are less likely to select the zero-debt policy when compared to firms that are close to their target levels.

What qualities do government-owned venture capital investors seek in a new venture? A comparison of investment criteria across pre-seed, seed, and expansion stage startups

Private venture capital (VC) investors usually do not invest in early life-cycle stage startups such as seed and pre-seed companies, since investment size typically doesn’t reach investment thresholds. The entry of governments with fund managers to venture capital markets presents seed and pre-seed companies with the opportunity to receive funding. This paper examines the main investment preferences of Hungarian government-owned venture capital investors regarding pre-seed, seed, and expansion stage startups. Verbal protocol analysis enabled examination of the screening process in real-time in all three life-cycle stages. It is found that governmental VC funds mostly value financial indicators followed by market-related qualities while private VCs value these characteristics in alternate formation. However, in the pre-seed stage, the financial acumen and capabilities of management teams form the main criteria in similarity to angel investors. Governmental VCs also greatly seek innovational value in target firms.

The Definition of Money Grand Received as a Key Determinant of Venture Investment Size in IT Russian Based Startups

Since 2006, Russian policymakers have been actively taking various measures to stimulate the venture capital market. Government venture capital funds are being created, for example, the Russian Venture Company with a capital of 15 billion rubles. Since 2011, thanks to the new legal mechanism, state-owned companies begin to invest in private venture funds. These measures have led to increased fundraising for startups. The main mechanism of such financing is grant support for young companies. According to 2018, the amount of grants to the total amount of funds raised in Russia is one of the highest among developed and developing countries, for comparison in the USA, it is more than 2.5 times lower. The venture market specifics is such that when deciding whether to invest in a company, investors inevitably turn their attention to the previous rounds of financing the company. The purpose of the work is to analyze the effect of the money grant received on the volumes of attracting subsequent financing. To analyze the impact of the receiving money grant, the determinant approach was used, as the most common in research on this topic. Based on a sample of 184 Russian IT startups, two OLS models have built to show the effect of money grant size to follow-on investment rounds. As a result, various sets of determinants were considered that explain the volume of investments attracted by startups for both international markets and the Russian market. In addition, an excursus was conducted to study the effectiveness of government venture funds, which are the main grantors in the Russian venture market. Based on the results of previous studies on this topic, it was shown that the size of the money grant has a positive effect on the amount of funding attracted in both follow-on rounds. For comparison, a number of previous studies of the Russian venture capital market show that the investment size of the current round has influenced only by the previous fundraising. Such a result testifies to the attractiveness for investors Russian IT startups that received grant support. The scientific novelty of the article is that the amount of funding attracted by startups is explained by such a specific indicator as grant support. In addition, the results obtained have certain practical value for those who invest in Russian startups. Investing in startups that received grants increases the likelihood of an exit for the investor in the next investment round.