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This paper studies the dynamic effects of selected macroeconomic factors on the performance of the housing market in Kenya using Autoregressive Distributed Lag (ARDL) Models. This study aims to explain the dynamic effects of the macroeconomic factors on the three indicators of the housing market performance: housing prices growth, sales index and rent index.

This study used ARDL Models on time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited.

The results indicate that household income, gross domestic product (GDP), inflation rates and exchange rates have both short-run and long-run effects on housing prices while interest rates, diaspora remittance, construction output and urban population have no significant effects on housing prices both in the short and long run. However, only household income, interest rates, private capital inflows and exchange rates have a significant effect on housing sales both in the short and long run. Furthermore, household income, GDP, interest rates and exchange rates significantly affect housing rental growth in the short and long run. The findings are key for policymaking, especially at the appraisal stages of real estate investments by the developers.

The authors recommend the use of both the traditional hedonic models in conjunction with the dynamic models during real estate project appraisals as this would ensure that developers only invest in the right projects in the right economic situations.

The imbalance between housing demand and supply has prompted an investigation into the role of macroeconomic variables on the housing market in Kenya. Although the effects of the variables have been documented, there is a need to document the short-run and long-term effects of the factors to precisely understand the behavior of the housing market as a way of shielding developers from economic losses.

The figure of the female revenger has haunted the western imagination as far back as some of the earliest extant texts, most starkly in Euripides' tragedies Hecuba and Medea (c. 430–420 bc). She has tended to take on one of three forms: the scorned woman, the vengeful mother or the victim of physical violence, almost always sexual violence.

This chapter presents an interdisciplinary and transhistorical understanding of the troubling figure of the violent female revenger in her shifting incarnations. The investigation traces conceptual strands through a variety of cultural texts, focusing on specific instances that are both situated historically and simultaneously analysed for the ways in which they reflect recurring priorities and cultural anxieties through the centuries.

After considering key ideas such as revenge and justice and gender and revenge, the chapter looks more closely at the so-called rape-revenge genre, moving from the earliest examples such as I Spit on Your Grave (1978) to more recent films which are considered for the ways they intersect with the global feminist protest movement #MeToo, and other key cultural moments such as the Harvey Weinstein case and the very public trial of the USA Gymnastics national team doctor Larry Nassar: Revenge (2017), The Nightingale (2018) and Promising Young Woman (2020). The chapter draws direct lines of connection between imaginative works, cultural types and stereotypes, and lived reality in order to come to a fuller understanding of the female revenger.

The housing market in Kenya continues to experience an excessive imbalance between supply and demand. This imbalance renders the housing market volatile, and stakeholders lose repeatedly. The purpose of the study was to forecast housing prices (HPs) in Kenya using simple and complex regression models to assess the best model for projecting the HPs in Kenya.

The study used time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited. Linear regression, multiple regression, autoregressive integrated moving average (ARIMA) and autoregressive distributed lag (ARDL) models regression techniques were used to model HPs.

The study concludes that the performance of the housing market is very sensitive to changes in the economic indicators, and therefore, the key players in the housing market should consider the performance of the economy during the project feasibility studies and appraisals. From the results, it can be deduced that complex models outperform simple models in forecasting HPs in Kenya. The vector autoregressive (VAR) model performs the best in forecasting HPs considering its lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and bias proportion coefficient. ARIMA models perform dismally in forecasting HPs, and therefore, we conclude that HP is not a self-projecting variable.

A model for projecting HPs could be a game changer if applied during the project appraisal stage by the developers and project managers. The study thoroughly compared the various regression models to ascertain the best model for forecasting the prices and revealed that complex models perform better than simple models in forecasting HPs. The study recommends a VAR model in forecasting HPs considering its lowest RMSE, MAE, MAPE and bias proportion coefficient compared to other models. The model, if used in collaboration with the already existing hedonic models, will ensure that the investments in the housing markets are well-informed, and hence, a reduction in economic losses arising from poor market forecasting techniques. However, these study findings are only applicable to the commercial housing market i.e. houses for sale and rent.

While more research has been done on HP projections, this study was based on a comparison of simple and complex regression models of projecting HPs. A total of five models were compared in the study: the simple regression model, multiple regression model, ARIMA model, ARDL model and VAR model. The findings reveal that complex models outperform simple models in projecting HPs. Nonetheless, the study also used nine macroeconomic indicators in the model-building process. Granger causality test reveals that only household income (HHI), gross domestic product, interest rate, exchange rates (EXCR) and private capital inflows have a significant effect on the changes in HPs. Nonetheless, the study adds two little-known indicators in the projection of HPs, which are the EXCR and HHI.

Consumer product firms renowned for marketing appear to be complementing brand creation, extension and acquisition with minority equity investments in entrepreneurial brand ventures (EBVs) for strategic purposes. Similarly, EBVs are looking for growth and resources that can be accessed via inter-organizational partnerships. This flourishing industry practice and the paucity of empirical research indicates the potential for new studies. The research objective was to examine why and how large incumbents were implementing strategic brand venturing (SBV), and with this understanding to develop a framework useful for descriptive and normative purposes.

This qualitative research study comprised in-depth interviews and multiple data sources across seven case studies drawn from US subsidiaries of global firms within the consumer products industry. Grounded in resource theory, the dimensions of strategic brand equity investments are abductively derived.

The findings delineate 16 process capabilities within four aggregate clusters entailing, the designing of the SBV program, opportunity identification, brand entrepreneur partnerships and venture portfolio management. Prefaced by endogenous and exogenous antecedents, these process capabilities help to contribute strategic and financial value when implemented.

This qualitative research study yielded analytical rather than statistical generalizations. A range of market and economic factors exist in the United States contributing towards a favorable entrepreneurial and brand incubation climate. This may render the SBV concept as contingent and contextual. Furthermore, the view of brand entrepreneurs' regarding the design of the process model were not explicitly sought but inferred from the discourses of the venturing units interviewed.

The article outlines several important implementation imperatives for corporations endeavoring to competitively advantage their brand portfolios via adoption of a minority equity investing strategy in EBVs. Practitioners are cautioned against myopically adopting this process alone as a success heuristic given other factors may impact success such as changes in corporate strategy or upper echelon sponsorship.

Mission preservation for social brand ventures being tethered to a large incumbent may need to be taken into account prior to and during SBV relationships.

The research contributes to the call for greater insights into the investment processes used in venturing relationships as well as coverage of new industry sectors beyond technology industries that often characterize corporate venture capital studies. Several novel findings emerged related to the importance of—the industry ecosystem; symbiosis between the founding brand entrepreneur and brand culture; synchronization of investment strategies with an emerging brand life-cycle model and serendipitous corporate entrepreneurial opportunities.

One increasingly popular financing option for entrepreneurial ventures is to attract corporate venture capital (CVC) investments. Prior research tends to take a CVC-centric perspective assessing the benefits and contingencies for incumbent firms or corporate investors to engage with entrepreneurial ventures. Few studies have taken the opposite perspective of investigating factors that entrepreneurial ventures need to take into account when engaging with CVC investments. As such, this study aims to investigate pre- and post-IPO entrepreneurial venture performance that partners with CVC providers or corporate investors, as well as to assess organizational and environmental contingencies.

This study draws on a sample of 631 entrepreneurial ventures from the CSMAR database ranging from 2009 to 2019, along with CVC financing data from the CVSource database and financial data in entrepreneurial ventures’ annual reports from the Juchao Network. This study applies multiple linear regression modelling and fixed effect panel data analyses to test the proposed hypotheses.

The results show that CVC investment contributes to entrepreneurial ventures’ financial performance, both pre- and post-IPO. However, while research and development (R&D) intensity and geographic proximity strengthen the positive relationship between CVC investment and entrepreneurial ventures’ performance pre-IPO, R&D intensity has a negative moderating effect on the relationship between CVC investment and entrepreneurial ventures’ performance post-IPO.

First, in emerging economies, adopting a CVC financing strategy is an important strategic choice for entrepreneurial ventures that have a great demand for external capital, resources and technology support. Second, leveraging the relationship between external financing and internal R&D investment is essential for them to maintain their core competitiveness and sustainable growth. Moreover, entrepreneurial ventures should deal with the coopetitive relationship with incumbent companies and manage their dependency on other market participants in the external environment.

This study focuses on the performance implications for entrepreneurial ventures engaging with CVC investments pre- and post-IPO. First, this study broadens and expands prior research on the mechanism of the relationship between CVC and entrepreneurial ventures’ financial performance. Second, the research conducts a comparative study of the moderating effects of different timings. Third, this study applies learning theory to the field of CVC in emerging economies.