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The purpose of this research is to ascertain the extent to which the income capitalization approach reflects the pattern of emerging rental income in Enugu property market.

The survey research design was used in this study. Data from the field was gathered through a data collection pro forma administered to 40 valuers in Enugu metropolis in the manner of conducting interviews. This study used key valuation details of 54 sampled income generating properties valued by the respondent valuers between 2015 and 2022 using the income capitalization approach. The same sampled properties were then revalued by the researchers using annuity due assumption/formulas of the income capitalization approach. Descriptive and inferential statistics were used to analyze the data.

The study revealed that the income capitalization approach used by most valuers in Enugu does not reflect the property rental income pattern prevailing in Enugu property market where rents are paid in advance. The study further shows that the application of the income capitalization approach for valuation of annually in-advance property rental income cash flow results in a higher capital value of 3.49% in Enugu property market.

The limitations to this study are that past valuation done by valuers were used in the analysis instead of actual property sales and a relatively small number of sampled valuers and properties are used in the study The implication of the study is that ordinary annuity assumptions or formulas is inaccurate and not suitable for valuation of income generating property in an emerging market like Nigerian where timing of cash flow is annually in advance. Based on the result of this study it seems that ordinary annuity approach negate the principle of estimating value using income capitalization method by converting future cash flow from income generating property into an estimate of property value.

The study advocates the adoption of the use of annuity due formulas in the valuation of income generating properties in Nigeria as its practice standard to avoid undervaluation as this assumption is logical and provides more accurate value due to prevailing lease structure and rent payments patterns in the country. The implication of the study is that the use of ordinary annuity assumptions or formulas is inaccurate and not suitable for the valuation of income generating property in an emerging market like Nigerian where timing of cash flow is annually in advance.

This is one of the very few empirical studies carried out in Nigeria to ascertain the extent to which the income capitalization approach used by valuers reflects the rental income pattern that prevails in the Nigeria property market.

With access to the large-scale search data from Rightmove plc, the paper firstly indicated the possibility of using user-generated data from online property portals to predict housing market activities and secondly embraced a GIS approach to explore what people search for housing and what they chose and investigated the issue of mismatch between search patterns and revealed patterns. Based on the analysis, the paper contributes a visual GIS-based approach which may help planners and designers to make more informed decisions related to new housing supply, particularly where to build, what to build and how many to build.

The paper used the 2013 housing search data from Rightmove and the 2013 price data from Land Registry with transactions made after the search period and embraced a GIS approach to explore the potential housing demand patterns and the mismatch between searches and sales. In the analysis, the paper employed the K-means approach to group prices into five levels and used GIS software to draw maps based on these price levels. The paper also employed a simple analysis of linear regression based on the coefficient of determination to investigate the relationship between online property views and values of house sales.

The result indicated the strong relationship between online property views and the values of house sales, implying the possibility of using search data from online property portals to predict housing market activities. It then explore the spatial housing demand patterns based on searches and showed a mismatch between the spatial patterns of housing search and actual moves across submarkets. The findings may not be very surprising but the main objective of the paper is to open up a potentially useful methodological approach which could be extended in future research.

It is important to identify search patterns from people who search with the intention to buy houses and from people who search with no intention to purchase properties. Rightmove data do not adequately represent housing search activity, and therefore more attention should be paid to this issue. The analysis of housing search helps us have a better understanding of households' preferences to better estimate housing demand and develop search-based prediction models. It also helps us identify spatial and structural submarkets and examine the mismatches between current housing stock and housing demand in submarkets.

The GIS approach in this paper may help planners and designers better allocate land resources for new housing supply based on households' spatial and structural preferences by identifying high and low demand areas with high searches relative to low housing stocks. Furthermore, the analysis of housing search patterns helps identify areas with latent demand, and when combined with the analysis of transaction patterns, it is possible to realise the areas with a lack of housing supply relative to excess demand or a lack of latent demand relative to the housing stock.

The paper proves the usefulness of a GIS approach to investigate households' preferences and aspirations through search data from online property portals. The contribution of the paper is the visual GIS-based approach, and based on this approach the paper fills the international knowledge gap in exploring effective approaches to analysing user-generated search data and market outcome data in combination.

We take Cumulative Prospect Theory (CPT) seriously by rigorously estimating structural models using the full set of CPT parameters. Much of the literature only estimates a subset of CPT parameters, or more simply assumes CPT parameter values from prior studies. Our data are from laboratory experiments with undergraduate students and MBA students facing substantial real incentives and losses. We also estimate structural models from Expected Utility Theory (EUT), Dual Theory (DT), Rank-Dependent Utility (RDU), and Disappointment Aversion (DA) for comparison. Our major finding is that a majority of individuals in our sample locally asset integrate. That is, they see a loss frame for what it is, a frame, and behave as if they evaluate the net payment rather than the gross loss when one is presented to them. This finding is devastating to the direct application of CPT to these data for those subjects. Support for CPT is greater when losses are covered out of an earned endowment rather than house money, but RDU is still the best single characterization of individual and pooled choices. Defenders of the CPT model claim, correctly, that the CPT model exists “because the data says it should.” In other words, the CPT model was borne from a wide range of stylized facts culled from parts of the cognitive psychology literature. If one is to take the CPT model seriously and rigorously then it needs to do a much better job of explaining the data than we see here.

This paper aims to investigate the law and logic mechanism of management control matching pattern and company strategy aggressiveness under different strategies by textual analysis, based on the empirical data of Chinese A-share listed companies during the period from 2010 to 2018. Additional analyses further investigate the moderating effect of environmental uncertainty and R&D intensity on the relationship between management control matching type and strategy aggressiveness. The conclusion can help relevant departments to develop management control theory and method system with Chinese characteristics and provide theoretical reference for the matching mode of dual control.

This paper uses the text analysis method. The main explanatory variables are analyzed using the computer SQL Server database software through the relevant text of the board of directors report in the company annual report. Other financial data came from the CSMAR database, excluding ST and PT and companies with missing data, and 16,902 samples were finally obtained. This paper conducted statistical analysis through Stata12.

This paper shows that the matching pattern between formal and informal control is divided into three types. They have different impacts on strategy aggressiveness. Specifically, consistent matching type II significantly positively influences the aggressiveness of offensive strategy. Consistent matching type I significantly positively influences the aggressiveness of defensive strategy. Complementary matching type I significantly positively influences the aggressiveness of analytical strategy. Additional analyses find that compared with non-high-tech companies, high-tech companies have more significant influence on the relationship between management control matching pattern and company strategic aggressiveness. And compared with other two “strategy-control” matching patterns, both environmental uncertainty and product innovation have more significant influence on the relationship between consistent matching II and offensive strategy aggressiveness.

To the best of the authors’ knowledge, this paper divides the formal and informal control matching patterns of management control into three categories for the first time. It examines the relationship between the formal and informal control matching of management accounting and the degree of strategy aggressiveness. The conclusion provides new empirical evidence to promote the effective implementation of development strategies for companies. It can help relevant departments to develop management control theory and method systems with Chinese characteristics and provide theoretical references for the matching mode of dual control.

The objective of this chapter is to identify the key characteristics of Global Services businesses that will thrive and achieve success in the future. These factors are integrated into three main pillars, which we refer to as the Triple-Win. The first and most obvious pillar is technology as a tool. The second pillar is the design and sustainability of the business model, without which the previous factor would be merely a cost and not an investment. And last but not the least, there is the purpose which gives meaning to the proposal, focusing on the human being and their environment. The DIDPAGA business model sits at the intersection of these three elements.

Integrating the resource-based view (RBV) with pay dispersion research, the authors examine how the allocation of resources between hiring new employees and compensating current employees, as well as the allocation of resources among new employees, affects organizational performance.

The authors use panel data on Major League Baseball teams. The authors also use system generalized method of moments (GMM) estimations to control for the impact of past performance on current performance, unobserved individual heterogeneity and omitted variable bias.

The authors find that the larger the portion of the human resources (HR) budget allocated to hiring new employees, the poorer organizational performance becomes unless the focal organization has already significantly underperformed. The authors also find that pay concentration among new employees has a positive impact on organizational performance unless the focal organization has already significantly overperformed.

This study extends RBV research by examining how resource allocation patterns affect organizational performance, which has rarely been studied. Moreover, by showing the organizational context's significant effect on the outcome of financial allocation for resource acquisition, this study extends both the RBV research and the pay dispersion research.

This study aims to explore how individual-level and organizational-level factors interact to influence pay information (PI) seeking and PI sharing preferences in PI conversations (i.e. the face-to-face communications context). The authors examine how an individual’s judgment of their pay relative to others – or pay equity perception – affects their PI seeking and PI sharing preferences and how those relationships are affected by organizationally created pay transparency policies and pay transparency practices.

Using a 2 × 2 × 2 experimental design on the MTurk platform, the authors used a scenario-based prompt method to manipulate employee perceptions of pay equity and organizational pay transparency and tested those effects on employee pay disclosure preferences. The authors consider both pay policy and pay practice dimensions of pay transparency and both PI seeking and PI sharing dimensions of pay disclosure preferences. The final sample had 597 participants.

The authors find employees’ pay equity perceptions are negatively related to PI seeking behaviors and are even more so when organizations have restrictive pay transparency policies. Also, both pay transparency policy and pay transparency practice increase PI sharing preferences.

The authors provide insight into how individual perceptions drive pay disclosure motivations and the role of organizational policy and practice in influencing pay disclosure preferences within PI conversations. The authors provide insight into the antecedents that shape pay disclosure preferences, which lead to a both PI conversations among coworkers and an increase in one’s pay understanding. This study shows the contextual nature of PI seeking and PI sharing preferences, which are a motivational antecedent to pay-related sensemaking behaviors.

The purpose of this study is to explore the exogenous and endogenous drivers of the high-growth of Unicorn start-ups along their life cycle, with a particular focus on Unicorns in the fintech industry.

The study employs an explorative longitudinal analysis with a matched-pair of two cases of Unicorns start-ups with similar antecedent features to understand holistically drivers over the longer term.

High-growth patterns over the longer term are the result of a combined industry- and company-life cycle perspective. Drivers and growth patterns vary significantly according to the time of entry in the industry and its development status. The findings are systematised within a set of propositions to be tested in future research.

The limitations lie in empirical evidence, as the analysis is limited to one-matched-pair. The revealed Unicorns' drivers for long-term growth might encourage future research to further investigate these drivers on a larger scale.

The study offers practical recommendations for start-ups with high-growth ambitions and advice to policy makers regarding the development of tailor-made support programs.

The study significantly extends extant work on growth and high-growth by examining endogenous and exogenous triggers over time and by linking the Unicorn-life cycle to the industry life cycle, an approach which has, to the best of the authors’ knowledge, not yet been applied.