The Journal of American Business Review, Cambridge
Vol. 4* Number 2 * Summer 2016
The Library of Congress, Washington, DC * ISSN 2167-0803
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Understanding FDI and Production Networks in East Asia
Dr. Willem Thorbecke, Japan’s Research Institute of Economy, Trade and Industry (RIETI), Tokyo, Japan
Nimesh Salike, Lecturer, International Business School Suzhou, Xi'an Jiaotong-Liverpool University, China
We investigate the factors influencing foreign direct investment in East Asia. We provide empirical evidence from the electronics industry supporting Kojima’s (1973) hypothesis that FDI in the region originates from the capital exporting country’s disadvantaged industry into the host country’s advantaged industry. These results imply that FDI and trade are complementary, unlike in Mundell’s (1957) model where they are substitutes. The results also indicate that exchange rate volatility deters trade, implying that reduction in the service link cost between production blocks can promote fragmentation. These findings imply that host countries in East Asia can obtain technology transfer by lowering service link costs. Why do firms engage in foreign production? According to Dunning (1988), the answer depends on a firm’s OLI (Ownership, Location, and Internalisation) configuration and its ability to exploit these OLI advantages in the target market. Ownership advantage is based upon technological and managerial superiority of home country firms relative to host country firms. Such superiority must be sufficient to overcome the extra costs incurred due to differences in business customs, formal and informal norms, languages, etc. Thus ownership is linked with control, and control becomes weaker as ownership becomes more diluted. Of course firms that outsource or subcontract may retain some control if they are involved in long-term relations. There may also be benefits to relinquishing ownership if the business partner has better managerial or technological ability in a particular product. Locational considerations and advantages include wage levels, factor endowments, technology transferability, physical and human infrastructure, and market-supportive institutions and political regimes. Internalisation advantage refers to the net benefits obtained by FDI firms through more captive and more integrated business activities conducted by parent firms. The optimal degree of internalisation revolves around how to balance the costs of asymmetric information, incomplete contracts, and ineffective dispute settlement mechanisms with the efficiency gains of complete outsourcing and de- verticalisation. Mundell (1957) showed that capital movement occurs from a capital-abundant country to a capital-scarce country in search of a higher marginal rate of return when the latter impedes the importation of capital-intensive goods from the former. In the capital-receiving country, the capital inflow causes the equilibrium production point to shift in such a direction that the capital-intensive industry (i.e., that country’s comparatively disadvantaged industry) expands, while the less capital-intensive industry (i.e., that country’s comparatively advantaged industry) contracts. In the capital-transferring country, exactly the opposite phenomenon occurs. As a consequence, the basis for trade (i.e., the existing pattern of comparative advantage between the two countries) is in the end eliminated by the movement of capital. In this model, FDI thus substitutes for exports. In contrast Kojima (1973) presented a general equilibrium model where trade and FDI are complementary. In his model FDI originates from the capital exporting country’s disadvantaged industry into the host country’s advantaged industry and is therefore export-oriented in nature. In Mundell’s model, FDI originates from the home country’s advantaged industry into the host country’s disadvantaged industry and is therefore export-substituting in nature. Kojima focused on foreign value-added activities that create capabilities in which the host country is comparatively well endowed relative to the home country. For instance, as wages in the investing country increase and as new products become more capital and knowledge intensive, it becomes profitable for firms in the investing country to transfer the location of production to lower wage countries. The investing country then exports sophisticated parts and components and technology to the assembly country, so that there is a complementary relationship between exports and FDI. Figure 1 shows the differences in Kojima’s and Mundell’s models in terms of Rybczynski lines for the host economy.(1) The vertical axis measures the comparatively advantaged goods which is labour intensive and the horizontal axis represents comparatively disadvantaged, capital intensive goods. The Rybczynski lines move away from each other in opposite direction in these models. For Kojima’s model, the Rybczynski line implies that the inflow of capital causes the labour intensive industry to expand and the capital-intensive industry to decline—a result opposite to that predicted by the original Rybczynski theorem. In this paper we seek to choose between Mundell’s and Kojima’s hypotheses as an explanation for East Asian FDI and trade. East Asian FDI began in earnest after the yen appreciated 60% following the Plaza Accord in September 1985. Japanese firms lost their price competitiveness and responded by shifting labour- intensive activities to other Asian countries. Japanese multinational enterprises (MNEs) first transferred factories to South Korea and Taiwan (see Figure 2a). However, in the late 1980s both wages and exchange rates in these economies skyrocketed. The locational advantage of producing labour- intensive goods in the newly industrialised economies (NIEs) declined, and Japanese firms transferred production to the Association of Southeast Asian Nations (ASEAN) countries (see Figure 2b). Surplus labour in ASEAN held wages down, and exchange rates in these countries were pegged at competitive levels relative to the US dollar. Japanese MNEs provided ASEAN firms with detailed engineering and managerial instructions and specifications, facilitating the assimilation of the new technologies. A virtuous circle of learning and growth developed that continued until the Asian Economic Crisis of 1997–1998. With the crisis, new Japanese FDI to ASEAN collapsed. However, As Figure 2b shows, the flow of parts and components from Japan to ASEAN continued unabated and Japanese firms did not pull out. Thus, Japanese investment in the region was not footloose.
Reengineering Culture as a Way to Address the Economic Disparity Among Native Hawaiians
Dr. Larson Ng, University of Hawai‘i at Mānoa, Honolulu, Hawai‘i
Native Hawaiians are the most economically disenfranchised ethnic group living in Hawai‘i. Many have often attributed their situation to a lack of education and/or economic opportunity. However, there is another possible factor that could be contributing to this situation. This factor is culturally rooted in the Native Hawaiian notion of economic production and profit. The following paper will initially survey the current economic disparity facing Native Hawaiians and then go on to explain the Native Hawaiian cultural understanding of economic production and profit, illustrating its differences with the capitalist-based notions of economic production and business profit. The paper will then conclude by offering what can be done to reengineer traditional Native Hawaiians economic beliefs that may work to culturally assist Native Hawaiians address the economic problems most of them face. Native Hawaiians are one of the most disadvantaged people living in Hawai‘i. The following will briefly survey the economic situation that a majority of Native Hawaiians face. Native Hawaiians have the highest unemployment rate of any major ethnic group living in Hawai‘i. Nearly one in ten Native Hawaiians in the civilian labor force was unemployed, in contrast with approximately one in seventeen statewide from 2006 to 2010 (Kamehameha Schools, 2014). Native Hawaiians are also underrepresented in white collar or management and professional occupations, which tend to offer the most economic security. For example, the ratio of Native Hawaiians in such occupations is 8.1 percentage points lower than the statewide average (Kamehameha Schools, 2014).Conversely, Native Hawaiians are overrepresented in blue collar or non-management, nonprofessional occupations. For example, the ratio of Native Hawaiians in transportation and production occupations is 3.0 percentage points higher than the statewide average (Kamehameha Schools, 2014). Similarly, the ratio of Native Hawaiians in construction, extraction, and repair occupations is 3.5 percentage points higher than the statewide average (Kamehameha Schools, 2014). Moreover, Native Hawaiians continue to be employed in agricultural, labor, and production occupations at higher rates than the state population. This gap between the Native Hawaiian rate and the statewide average, however, has ranged from 7.5 percentage points in 1990, decreased to 5.7 percentage points in 2000, but increased 6.5 percentage points in 2010 (Kamehameha Schools, 2014). Finally, the unemployment rate among Native Hawaiians has exceeded the statewide average since 1990. For example, the gap between the unemployment rate of Native Hawaiians and the state population has increased over the last two decades from 2.2 percentage points in 1990, to 3.5 percentage points in 2000, and 3.2 percentage points in 2010 (Kamehameha Schools, 2014). Native Hawaiian households were the least likely to have a livable income and the most likely to live in poverty of all the major ethnic groups in Hawai‘i. For example, in 2009 about 56.8% of Native Hawaiian households had a livable income, compared 71.9% of Japanese and 69.8% of White households (Kamehameha Schools, 2014). Moreover, among the major ethnic groups in Hawai‘i, Native Hawaiians experienced a 10.5 percentage point decrease of households with a livable income from 2003 to 2009 (Kamehameha Schools, 2014). The mean income of Native Hawaiian family households with young children was approximately $76,925 between 2006 and 2010 (Kamehameha Schools, 2014). This was $4,429 less than the statewide average of all family households with young children during the same period (Kamehameha Schools, 2014). In addition, the mean income among Native Hawaiian family households with young children was $7,651 lower than that of non-Hispanic White family households, which was the second lowest income group and $22,931 lower than that of Japanese family households, which was the highest income group (Kamehameha Schools, 2014). Homelessness is one of the major realities that Native Hawaiian has to cope. For example, the number of Native Hawaiians who received services at government-funded homeless and transitional shelters increased by 55.8 percent from 1,569 to 2,444 compared to just 54.4 % for non-Hawaiians from 2006 to 2013 (Kamehameha Schools, 2014). Between 2003 and 2009, Native Hawaiians experienced the highest poverty rate of the state’s major ethnic groups. For example, in 2009 the prevalence of poverty among Native Hawaiians was 3.0 percentage points higher than the statewide average (Kamehameha Schools, 2014). In addition, Native Hawaiian family households were more likely to be living in poverty than any of the state’s other major ethnic groups. For example, between 2003 and 2009, Native Hawaiian family households were more than twice as likely to live in poverty, as were Japanese family households, which were the most prosperous ethnic group in Hawai‘i (Kamehameha Schools, 2014). At the foundation of any capitalist system is the notion in economic freedom (Ghosh, 2014; Rogers, 2014; Scott, 2011). Economic freedom is made up of two components: the freedom of choice and freedom of enterprise. The freedom of choice means that any individuals are free to make their own economic decisions (Ghosh, 2014; Rogers, 2014; Scott, 2011). Every member in a given society is free to make those choices that will impact their lives. The freedom of enterprise is the freedom to own and operate a business. It also entails the freedom to make all business decisions, constrained only by competition and the forces of supply and demand in the open market (Ghosh, 2014; Rogers, 2014; Scott, 2011). The concept of private property is central to the belief in economic freedom (Ghosh, 2014; Rogers, 2014; Scott, 2011). The factors of production as well as the goods and services produced are all privately held by individuals (Ghosh, 2014; Rogers, 2014; Scott, 2011). In this arrangement, individuals are free to utilize or dispose of their property as they see fit. Consequently, individual ownership includes the right to reap the rewards or suffer the risks of their decision-making. As owners of the factors of production, individuals may reap the rent gained for the use of their land, wages for the use of their own physical labor, interest as a return on the use of their own capital, and/or profits gained from their entrepreneurship. Individual decisions in capitalism are directly influenced by self-interest responding to a system of economic incentives (Ghosh, 2014; Rogers, 2014; Scott, 2011). Positive economic incentives encourage the rise of economic activity and negative economic incentives discourage it (Ghosh, 2014; Rogers, 2014; Scott, 2011). Based on this situation, economic incentives guide resource allocation toward the production of goods and services that society wants and away from the production of goods and services not desired by society. For example, the desire for profit would direct the capitalist to put more productive resources into the production of goods and services commanding a high demand in the marketplace, while the desire to avoid losses would discourage the capitalist from putting productive resources into the production of goods and services that people did not want. Subsequently, consumers seek lower prices and avoid higher prices for the goods they want; savers seek higher interest rates and avoid lower ones; and workers are encouraged to supply their labor in jobs that command higher wages and move their labor away from those occupations that pay lower wages.
Defining The Market: Performance Measurement For Management Forecasting
Dr. Tony Carter, Professor, University of New Haven, West Haven, CT
This article investigates and examines the positive role that management tools, such as quantitative based sum of the least squares regression analysis can have coupled with qualitatively based Customer Advisory Boards. The ability to identify the future conditions in the marketplace, how active a company will be, what staff levels it will need or the actions of competitors can be difficult to predict with any degree of precision. Documenting this plan creates a degree of accountability rather than just being able to say “I’ve got our plan all in my head.” No business understands the importance of effective sales planning better than the competitive soft drink beverage industry. PepsiCo’s net sales totaled $30.4 billion, with 71 percent of its revenue generated from the domestic U.S. market. Coke’s net sales totaled $18 billion, with 71 percent of its revenue coming from global markets. PepsiCo will alter its global strategy in the future, targeting emerging markets, like China, India and Eastern Europe. Managers that direct growing business activity annually have the ability to know their customers well enough in order to design a sales process that meets their needs. Customer sophistication and price and value sensitivity are strong factors in both retail and business to business customer buying behavior. Wal-Mart changed its business design by offering inexpensive access to a wide range of nationally known products. Wal-Mart made shopping easier by offering lower prices than other department stores and using logistics to cut an average of two hours off shopping time. Customer Advisory Boards are a dynamic practical management tool that can greatly enhance the customer development and retention process and give firms a distinct competitive advantage. Organizations should develop programs for customer satisfaction, measure customer loyalty and further understand customer intimacy and its role in customer retention and more study is warranted in this area. The purpose of this article is to look at how management tools, such as Customer Advisory Boards along with traditional quantitative forecasting, such as sum of the least squares and regression analysis, can help organizations. The turmoil in the workplace results in part from management ineffectiveness that made these organizations less competitive. Workers who gave loyalty under the old system have suffered under the new. It is no surprise that employee cynicism has grown. For effective performance to occur, even in large organizations, which depend on thousands of employees understanding the company, strategies must be translated into appropriate actions and leaders must win over their followers one by one. Customer Advisory Boards are a dynamic, practical management tool that can greatly enhance the customer development and retention process and give firms a distinct competitive advantage (Ross 1997). These boards should be comprised of ten to twenty members. Ideally, members should be senior level managers with decision making authority in their own organizations. It is also advised to target as board members people with knowledge in areas where the company is weak. For example, a company might try to draft a technology expert for help with finding opportunities on the internet. Once a board has been formed, it should meet two to four times per year. Agendas could include company strategy, product quality, sales problems, customer satisfaction and marketplace changes (Carter 1999). A CAB is also a competitive advantage in itself. One telecommunications company included in the study found that its customer base was eroding. When its executives turned to the CAB for help, the board gave them suggestions on how to track defections and how to develop and implement a system that provided continuous customer feedback. As a result, the company was able not only to stem the flow of customer defections but also to reverse the trend, rebuilding its customer base (Ross 1997). By developing a personalized dialogue with buyers on an ongoing basis and listening to their needs, concerns, and feedback organizations can become more responsive, insightful, and competitive. It is important that an organization acts on the members’ input at some level, even if it does not adopt every suggestion the CAB may have made. This demonstrates a sincere and secure organization willing to listen to critical feedback from clients because it genuinely understands them (Carter, 2003). A CAB can assist in this process since the dynamics of a board actively improve the contact and dialogue that a company has with board members, who represent actual or prospective customers. Having customers involved as partners allows them to play a participatory role, enabling the company to see itself from the customers’ standpoint, an invaluable insight (Carter 1999). Fortune 500 companies for example, from many industries use Customer Advisory Boards like, Microsoft, Hewlett-Packard, Delta Airlines, Lucent Technologies and Northwest Airlines. Customer Advisory Boards allow a company to listen and keep in touch with their customers, a very effective way to build loyalty and promote retention. Boards will obviously never replace talking to customers during sales calls, but they do provide a formal interface where the buyer-seller relationship is based on honesty, not negotiation (Carter, 1999). To be manageable a board should have five to ten members, or as many as twelve to fifteen members who are prominent in the business community. Companies can even use a revolving board that changes membership every one to two years to broaden the pool of participants (Carter 1999). The length of tenure for board members can be short or long term depending on the needs and circumstances of the company and the board members. For example, a membership period of one to two years allows for more frequent influx of new members and the perspectives that they bring through their participation. Longer-term memberships provide a more stable, familiar board presence. The board can meet quarterly or more frequently, as circumstances dictate. In the event of a crisis or emergency situation, the board may actually provide a company with a fuller range of perspectives to help overcome the dilemma. At a minimum, boards should meet at least twice a year to be of some strategic utility, which means they need not be a totally time-consuming endeavor to provide some clear benefit (Ross, 1997). The CAB can be comprised of CEOs and presidents or the functional executives who decide where they will direct their business. Members could be selected for their particular expertise. For example, a strategic planner with a good long-range perspective or a technical expert who understands a products’ or service’s features might be a good choice. Other favored board candidates are women and minorities, who can reflect a diversity of views and insights that can be of great benefit to a firm. Members could also be selected for their demographic segment, geographical location, revenue potential, reputation, and prestige in the marketplace (Ross, 1997). Board membership can serve as a perk for current customers and reward them for their loyalty or reach prospective customers who do not currently use a company. Customers want to believe that companies care, and the formation of a CAB alone can show this and help to develop a rapport. Board membership also makes it possible for the various professionals on the board to network with one another. Membership can also be a stamp of success for candidates who accept companies’ invitations since the criteria for selection are their prominence and knowledge. Thus, a unique feature of CABs is that the members expect their advice to be taken seriously (Ross, 1997). The characteristics of a good board member are intelligence combined with business savvy and relevant industry experience and asking lots of focused questions to provide informed feedback on what a company is doing right and what it is doing wrong. An important factor in retaining board members is their having demonstrated the ability to learn and interact with fellow board members and company employees in a stimulating environment.
Knowledge Spillover Effects of Star Analysts
Dr. Po-Kai Huang, Shih Hsin University, Taiwan
This paper investigates the knowledge spillover effects of star analysts. Specifically, we investigate whether the analysts who belong to the investment banks existing star analysts can forecast earnings accurately through the knowledge spillover effects of star analysts. This issue is important for knowledge spillover effects, and yet we know so little about it. We find that earnings forecasts that are produced by star analysts are indeed more accurate than those by non-star analysts. Moreover, we indicate that if during the specified year a brokerage firm has star analysts, the average earnings forecasting errors of other analysts at the same brokerage firm will be lower. These results remain unchanged even control for the number of companies and industries analysts covered, the number of days from forecast earnings to published actual earnings, and company characteristics, etc. Each year the Institutional Investor asks analysts who work on the buy-side to vote for their “star analysts.” The candidates consist of analysts that work on the sell-side. Institutional Investor asks the buy-side to rate sell-side analysts in six of their primary activities: picking stocks, estimating corporate earnings, acquiring knowledge of their industry, writing reports, being responsive to clients’ requests and initiating timely calls to investors. Academic literature believes that these star analysts indeed excel in what they do. For example, star analysts supply more accurate earnings forecasts than other analysts (Stickel, 1992), and stocks recommended by the all-star analysts outperform benchmarks (Desai, Liang, and Singh, 2000). Sinha, Brown, and Das (1997) also indicate that star analysts remain superior in the next period. Star analysts have expertise that can be transferred to other analysts who work at the same brokerage firm. As Nelson (1986) and Jaffe (1989) considered, when the research and development conducted by universities or research institutes produces external benefits by facilitating the production and innovation of manufacturing firms in their neighboring industries, such a diffusion of knowledge is known as a knowledge spillover. This study believes that because knowledge possesses distribution and transmission characteristics, the expertise of star analysts has positive knowledge spillover effects. Moreover, as Jaffe (1989) considered, neighboring producers of knowledge are beneficial to the positive externalities of knowledge spillovers, even through informal talks. Star analysts can share their experience with their colleagues, and these colleagues can also ask the star analysts for guidance or advice. In this way, colleagues within the same industry can obtain advanced industrial information or create private information channels. Colleagues from different industries can also develop better analytical skills and abilities, thus allowing the analysts who are affiliated with the star analysts to calculate earnings forecasts more accurately. Many studies have already confirmed the positive spillover effects if there are “stars” in the financial services industry. Star funds have positive spillover effects in cash inflow. Nanda, Wang, and Zheng (2004) indicate that star fund results in greater cash inflow not only to the fund, but to other funds in its fund family. Zhao (2004) also find that star fund closing decisions brings investors’ attention and investments to other funds in the family. Khorana and Servaes (2012) argue that the presence of a star fund has a strong positive spillover effect on fund family market share. The spillover effect of star analyst is also positively related to IPO market share. Clarke, Dunbar, and Kahle (2003) indicate that the investment bank gaining the star analyst experience a significant change in market share of 1.25%. However, relevant literature on the knowledge spillover effects of star analysts is still lacking. The purpose of this study is to investigate whether the earnings forecasts of other analysts are more accurate if the brokerage firm contains star analysts. The important nature of this study is that through exploring the knowledge spillover effects of star analysts, it can identify whether the earnings forecast experience has distribution and transmission characteristics. This is a subject that has not yet been discussed within the field of knowledge spillover effects. This study can make up for the empirical inadequacy in the field, as well as strengthen the theoretical foundation regarding knowledge spillover effects. The other sections of this study are as follows: the second section is a literature review to investigate why knowledge spillover effects of star analysts exist, the third section introduces the samples used in this study, and it defines star analysts in addition to the important variables, the fourth section describes the research methods used, the fifth section contains the empirical results, and the final section contains the conclusions. This study defines star analysts as the top three analysts in Taiwan, as selected by the Institutional Investor investment magazine. The Institutional Investor and Asiamoney both produce ratings for analysts in Taiwan; however the rating standards used by both magazines are different. Sharon Su, who became the Principal Analyst at the Institutional Investor in 2003, pointed out in Business Weekly, “Unlike the analysis carried out by Asiamoney that tends to portray analysts’ personal styles, the Institutional Investor emphasizes team integration and receives more attention from foreign investment circles.” As the purpose of this study is to explore the knowledge spillover effects of star analysts, it emphasizes the contributions made by a star analyst’s research team, rather than their individual performance. Hence, this study defines star analysts as the top three analysts in Taiwan, as selected by the Institutional Investor. Table 1 identifies the top three analysts in Taiwan, as selected by the Institutional Investor magazine, encompassing the selections made by Asiamoney magazine for comparison. The sample period used is from 1996 to 2001. According to the results of voting by the Institutional Investor, although there was not a single analyst that was listed during each of the consecutive years, SBC Warburg remained on the list each year. From 1999 to 2001, there were three foreign brokerage firms that took turns being replaced on the list. They were SBC Warburg, Merrill Lynch, and ABN AMRO. Since 1998, ING Barings fell off the list after Peter Kurz left Barings Group in July 1997 and became the General Manager of Merrill Lynch Securities. This outcome was also represented identically on Asiamoney magazine’s results. Surprisingly, some analysts from internationally renowned investment banks—such as Goldman Sachs, Morgan Stanley, and Donaldson Lufkin & Jenrette—were not selected as the top three analysts in Taiwan.
A Study of Emotional Hyperbolic Discount Utility for Intertemporal Decision Making
Dr. Yeu-Shiang Huang, National Cheng Kung University, Taiwan
Yu-Sheng Chen, National Cheng Kung University, Taiwan
The hyperbolic discount utility model, which is commonly used to help explain intertemporal decision-making behaviors, is well-recognized for resolving the paradox of preference reversal and being less steeply discounting than the exponential discount utility model. However, while much attention has been paid to hyperbolic discount utility, it still cannot explain some anomalies. This study considers decision makers’ internal feelings toward the difference between the actual and anticipated results of a future event to explain intertemporal decision-making behaviors, and proposes an anticipative hyperbolic discount utility model in which the degree of the subjective feeling of inner happiness, which is an emotional function that may affect the decision-making process, is taken into account. This proposed emotional hyperbolic discount utility model investigates the effects of the decision makers’ emotional fluctuations on intertemporal decisions with conditions of different amounts of payoffs, probabilities of obtaining payoffs, and timing of obtaining payoffs. The proposed model can also be used to infer the relationship between the emotional factor and discount utility, and thus can explain some paradoxes that cannot be explained by the original intertemporal discount models. Therefore, the proposed model is more realistic in interpreting intertemporal decision behaviors. People would generally prefer to obtain $1,000 immediately rather than a week later, since they often calculate the discounted present value of the future amount of money based on the market interest rate. However, in an intertemporal decision-making context, the discount rate does not have a basis for the discounted utility of the anticipated results of a future event. Discount utility models have thus been developed to evaluate intertemporal decision-making behaviors (Samuelson, 1937; Lancaster, 1963; Stevenson, 1986). However, discount utility, like the expected utility theory, has some unexplainable anomalies (Thaler, 1981; Roelofsma, 1996; Wathieu, 1997; Lazaro et al., 2002). This study investigates the effects of the anticipated results of future events on current feelings based on the hyperbolic discount utility model with consideration of both the time factor and the likelihood the anticipated results would occur or not. Since the conventional discount utility models are incapable of sufficiently explaining intertemporal decision behaviors, many revised models have been proposed. The exponential discount utility model with a constant discount rate is the base model, with later models being developed by considering anticipated psychological factors (Prelec, 1989; Green et al., 1994). However, since exponential discount utility models are incapable of explaining preferential reversals, the hyperbolic discount utility model was then proposed, and has since become the main research stream in this area (Mazur, 1987). A number of anticipated psychological factors have also been considered to revise the original hyperbolic discount utility models. However, since such hyperbolic models have been criticized as being rather inflexible, a set of power law discount models have been proposed, although these have not yet been extensively studied (Huang & Wu, 2007). Loewenstein (1987) considered that discount utility models can be extended by incorporating anticipated psychological factors, and thus integrated consideration of individuals’ future preferences and anticipation of specific results into the exponential discount utility model. Caplin and Leahy (2001) extended the expected utility theory to situations in which individuals can use past feelings of anticipation, such as expectation, anxiety, and worry, along with considerations of time and anticipated factors, and showed that these anticipatory feelings may result in time inconsistencies. Huang and Hsu (2008) considered anticipative psychological effects and incorporated two factors: the difficulty of decision-making for decision makers and the imagination of future specific events, into the hyperbolic discount utility model, thus proposing an anticipative hyperbolic discounting utility model. Kahn and Sarin (1988) proposed a model for predicting consumer choices under uncertainty, in which the measure of ambiguity is defined to distinguish various decisions made under conditions of risk, and thus this model can predict different decisions for individuals who are ambiguity averse, ambiguity seeking, or ambiguity indifferent. Lowenstein et al. (2001) summarized the influential factors which may affect intertemporal decision-making as anticipated outcomes (including anticipated emotions), subjective probability, immediacy, and imagination. Loewenstein and Prelec (1992) proposed a generalized hyperbolic discount utility model in which the exponential and hyperbolic discount utility models have a conversion relationship. Myerson and Green (1995) compared the exponential and hyperbolic discount utility models, and stated that the latter has a slower decrease in the present utility than the former. Kirby and Marakovic (1996) stated that the discounting rate is a function which decreases with the size of the delayed payoffs, no matter whether exponential or hyperbolic discount utility models are assumed. In addition, on average males have a higher discounting rate than females. Bleichrodt and Gafni (1996) investigated the discount utility in the context of individuals’ health status, and found that the exponential discount utility model cannot reveal individual preferences well and the hyperbolic one seem to be more flexible. Azfar (1999) stated that when individuals are uncertain about their discount behaviors, it is reasonable that the discount utility for the distant future is less than that for the near future. Previous studies on discount utility suggested that individuals’ discount behaviors for uncertain future outcomes often have hyperbolic instead of exponential rates. Rachlin et al. (2000) stated that the hyperbolic discount model performs better than the exponential one with considerations of the delay and probability. Cairns and van der Pol (2000) used a non-linear regression approach to compare the exponential discount utility model with three hyperbolic ones, and concluded that the hyperbolic discount utility model is more capable of interpreting human intertemporal preferences. van der Pol and Cairns (2002) stated that there is substantial evidence that hyperbolic discount utility models are better at describing intertemporal preferences for monetary outcomes than conventional exponential discount utility models, but relatively limited evidence that they can be used to make useful suggestion with regard to health problems. Similarly, Lazaro et al. (2002) stated that hyperbolic discount utility models can better describe intertemporal preferences than conventional exponential and quasi-hyperbolic discount utility models. In practice, human emotions, such as joy and disappointment, are often involved in evaluating the risk of the anticipated results of a future event which may occur or not (Bell, 1982; Bell, 1985; Loomes and Sugden, 1986; Gul,1991). Inman et al. (1997) stated that information about forgone alternatives, which are considered but not chosen, can have significant impacts on choice valuation, and thus proposed a decision model with consideration of expected performance, disappointment, and regret. Jia et al. (2001) extended the disappointment models originally proposed by Bell (1985) and Loomes and Sugden (1986), and proposed an explicit function which models the effects of disappointment on risky choice behavior to explain some intertemporal decision anomalies. Regret and disappointment are two emotions that are closely related to decision-making. Van Dijk and Zeelenberg (2002) compared assessments of the two emotions of anger and sadness, and found significant differences with regard to regret and disappointment.Agarwal and Malhotra (2005) incorporated both affect (e.g. feelings and emotions) and attitude (e.g. judgments due to brand equity) to propose an integrated model of attitude and choice, and investigated the interactions between affect and cognition. Delquie and Cillo (2006) released the assumption that disappointment may stem from comparing an unobtainable outcome with any outcome obtained from previous gambles, and thus proposed a new and general disappointment model which incorporated the rank dependent utility and risk evaluation. Delquie and Cillo (2006) proposed a disappointment model in which disappoint and joy arise from comparing the received outcomes, instead of the expected value of the uncertainty outcomes, and their approach can better reflect the way that individuals experience disappoint.
Some Thoughts on Business Rules
Dr. Mirko Cubrilo, Zagreb University, Croatia
Dr. Mirko Malekovic, Zagreb University, Croatia
Dr. Kornelije Rabuzin, Zagreb University, Croatia
In this paper we discuss some aspects of the current state in the field of business information systems domain with an emphasis on the domain (context) of business rules, ranging from theoretical assumptions through modelling (in appropriate languages and tools) to use in business, with a focus on so called business rule paradox. It is well known that in many areas of human activity there is a gap between theory and practice and that this gap is mostly expressed either through inadequate theory or inadequate practice, where it is usually more often the case that the theory is more developed then the practice, or (in its own way, even though that may seem to be paradoxical) practice is more developed then theory. In the context of business processes and rules, their modelling and effective application to business practices, there is a paradox of the existence of highly developed theories which would enable quality practices, and a whole array of individual, very proficient practical solutions (implementations of business rules) on the one side, but no universal and generally accepted methodology for modelling bussines rules as a service for business processes and information systems to support them on the other side. The part of the problem is to be found in the inherent complexity of corresponding theories and tools which are necessary for modelling business rules and processes, but a part is to be found in inadequate (and due to systemic reasons incomplete) education of the main participants in that process, as well as a lack of communication between them. Business rules are undoubtedly one of the most important resources of any business subject. They are inherent to every business process, independent of its being conducted manually or with the support of information technology. In the first case they are dispersed in the multitude of laws, statutes, standards, business regulations, business and technical documentation and so on. In the second case, in the context of modern business practice, which is increasingly dependent on the support of information technology and is conducted in the virtual world of computers and computer nets ranging from local to global, like the World Wide Web, they are partially implemented in the processes through which business itself is pursued. Large companies and software manufacturers like Microsoft or IBM, conscious of a potentially vast market in the business rules segment, have developed or are currently developing large-scale development environments for business support, such as BizTalk Server (Microsoft) or WebSphere ILOG JRules (IBM), which, among other features, contain “modules” or even “languages” for modelling business rules. The importance of business rule modelling is additionally emphasized by the fact that a whole host of professional association (such as Business Rule Community (1), Object Management Group (2) - OMG, W3C (3), Eclipse (4), Protégé (5), IIBA,(6) …)is developing standards, specific programming languages, modelling languages (especially markup languages grouped around XML) and development environments for the requirements of the business rule and business process modelling as well as for the so-called accompanying technologies (semantic web, ontologies, Web services). This is equally true of the academic community involved in these research areas. Scientific research in the area of modelling business rules belongs within a wider context of the so-called semantic technologies. Next to modelling business rules, they encompass categories such as ontologies and semantic Web. These encompass a few narrower areas, such as developing logic formalisms (Descriptive logic, F-Logic,…) for the business rules modelling and the development of corresponding programming languages and environments (Protégé, Ergo Suite, Flora-2, DATALOG,…). A part of the research and development is done through the development of (the) corresponding standards under the auspices of the already mentioned associations such as W3C, OMG and Business Rules Group. The other part is done through the research of numerous research teams and individuals within the academic community and the research departments of large software companies, profit-oriented (IBM, Microsoft, Google (to a certain extent), Vulcan, Coherent Knowledge Systems,…) and non-profit (Eclipse Foundation,…). Nevertheless, despite all the things described above, concerning the development of theory, programming languages, tools and development environments, very few (the authors aren't familiar with one single case) cases of implementation of business rule systems (by the (Business Rule) Book) exist which fulfil the criteria established in a well-reasoned manner as irrefutably valid by Ronald Ross and his collaborators. We will proceed to elaborate why this is the case and to suggest a solution for the above mentioned paradox. In order to understand that paradox we will shortly describe the existing practice of business rule and business process modelling, some (not all) theoretical pillars necessary for their successful modelling and also present the main “players” in that “game”. Finally, we will “distribute” that set of theoretical knowledge and practical skills to these players according to their respective “jurisdictions” over business processes and business rules. The organisational structure of business subjects is constantly changing and adapting to the changes in its social and business environment, on the local as well as on the global level. The introduction of computer technology in the market competition has forced the enterprises to implement a good part (as large as possible) of their business through information systems, automatizing all business functions where automatization was possible. This pertains first and foremost to business processes and business rules. According to the Web source (7) of the Appian company: „Business process is a collection of linked tasks which find their end in the delivery of a service or product to a client. A business process has also been defined as a set of activities and tasks that, once completed, will accomplish an organizational goal. The process must involve clearly defined inputs and a single output. These inputs are made up of all of the factors which contribute (either directly or indirectly) to the added value of a service or product. These factors can be categorized into management processes, operational processes and supporting processes“. Until recently business processes were modelled in a "primordial" fashion, led and managed by business practice (a sort of an evolutionary development). Following the need for their automatization within the framework of information systems, theoretically founded methods for their modelling have been developed (and are still being developed today). Among the older methodologis the most popular is the one implemented within the ARIS (9) system, while the foundation of modern methodologies is the BPMN (Business Processes Modeling Notation), a standard developed by the OMG asociation. The following image shows a business process (ordering and delivering pizza) in BPMN notation. ) According to Business Rule Community: “Business rules are lists of statements that tell you whether you may or may not do something, or give you the criteria and conditions for making a decision.” (10). By their nature business rules represent a service for business processes. Until now, and mostly today as well, they were directly embedded in business processes. The fundamental proposition made by Ross (with which we are in full agreement) is that such implementation is highly dysfunctional and counterproductive due to business processes changing and being updated continuously, while business rules remain unchanged in a much broader conceptual and temporal context. Continuous interventions in business processes (their implementations within the frame of business information systems) simply increase the “entropy” of the whole system. This “entropy” will be discussed later on. But for us to be able to do that, we first have to roughly describe the theoretical and practical assumptions on which business rule modelling is founded.
The Financial Characteristics of Firms that Have Changed Their Pension Plans
Dr. Tae Ryu, Metropolitan State University of Denver, CO
Dr. Gregory Clifton, Metropolitan State University of Denver, CO
Pension plans are designed to provide income to individuals during their retirement years. This is accomplished by setting aside funds during an employee’s working years so that at retirement the accumulated funds plus earnings from investing those funds are available to replace wages. In general, pension plans are divided into two basic types: defined benefit (DB) and defined contribution (DC). DB plans promise a specified benefit at retirement, whereas DC plans are based on contributions into individual accounts, with benefits being determined by the value of the fund at retirement. Thus, employers are at risk with DB plans because they must contribute enough to meet the cost of benefits that the plan defines. According to a recent survey conducted by the U.S. Government Accountability Office, the number of private DB pension plans has declined substantially over the past two decades. About 92,000 DB plans existed in 1990 compared to just under 29,000 plans in 2009. At the same time, the number of DC pension plans, such as 401(k)-type plans, has grown dramatically and resulted in a huge shift from DB plans to DC plans. The primary reason for the shift is that government regulations make DB plans cumbersome and costly to administer. DC plans often cost no more than 3 percent of payroll, but DB plans can cost 5 to 6 percent of payroll. Also, employers bear the burden of any investment losses, in the form of increased funding requirements, because DB plans guarantee a certain benefit. In this study, we investigate the financial characteristics of firms that switch to DC plans. Using the dichotomous regression model, we examine the effects of firms’ financial characteristics – profitability, liquidity, activity, solvency, size, cash flows, pension funding status, pension expense ratio, bankruptcy risk - on their decisions to either stay with DB plans or switch to DC plans. The logistic regression results show that earnings per share, debt/equity ratio, pension funding status, pension expense ratio, cash flow ratio, bankruptcy risk are significant determinants of firms’ decisions on whether to switch to DC plans or not. Pension plans are an important component of compensation for many employees. The plans provide monetary benefits to employees after their retirement in return for their employment services. A survey found that, in 2006, nearly half of all American households whose head was age 60 or older had income from their own or spouse’s pension, and the average pension benefit was $15,784 per year (National Institute on Retirement Security). Pension plans are established for a variety of reasons. One of the earliest benefits was that a pension plan solved the problem that arose from the rising wages for long-term employees. As employees’ wages continued to rise, old workers were sometimes encouraged to stay with their employers for too long. But, pension plans can encourage the employees to retire at the “right” age. Also, pension plans can provide employees with a degree of retirement security and this security in turn attract and help retain good employees. Meanwhile, from the employees’ perspective, pensions are valued as a means of long-term savings, especially in cases where employees perceive that they lack the self-control to save independently (Friedberg 2002). Germany became the first county in the world to introduce a public pension in 1889. It was not until 1935 that pension plans started in the United States. In the 1930’s very large employers such as railroad and utility companies became prevalent, and people started to work for their employees. These employers made a credible promise to provide their employees with old-age pensions, and this promise must have been a valuable asset for the employees because they knew they would be taken care of when they became old (Clark et al. 2006). In general, pension plans are divided into two basic types: defined contribution (DC) and defined benefit (DB) plans. DC plans define or specify only the employer’s contribution. The plans make no promise regarding the ultimate benefits paid out to employees. Once contributions are made, the employer has no additional liability. On the other hand, DB plans define the benefits that employees will receive when they retire, mostly based on employees’ years of service and their compensation level. The employer is responsible for ensuring that sufficient funds are available to provide promised benefits. DB plans, however, may be more advantageous to short-service older employees while short-service younger workers may benefit more from DC plans. DB plans can be a vehicle to lure older, experienced and talented employees while DC plans are often regarded as a vehicle to attract young employees (Findlay 1977). Also, DB plans are more advantageous to employers with stable or growing earnings and shareholder-employees age 50 and older with long service records. The employers should have the financial resources to meet the DB plan’s funding requirements for at least five (preferably, ten) years to avoid IRS arguments that the plan was not intended to be permanent when adopted (Ellentuck 2005). DC plans have supplanted DB plans as the typical pension for many workers in recent years. According to a survey conducted by the U.S. Government Accountability Office, the number of private DB pension plans has declined substantially over the past two decades (US Government Accountability Office 2009). About 92,000 DB plans existed in 1990 compared to just under 29,000 plans in 2009. At the same time, the number of DC pension plans, such as 401(k)-type plans, has grown dramatically and resulted in a shift from DB plans to DC plans. There are a couple of reasons for this shift. First, employers bear the burden of any investment losses, in the form of increased funding requirements. So, employers are at risk with DB plans because they must contribute enough to meet the cost of benefits that their plans guarantee. But, in DC plans, the employer’s obligation ends when contributions are made. Secondly, government regulations (for example, The Employee Retirement Income Security Act, ERISA, 1974) make DB plans cumbersome and costly to administer. Due to the added burden of administration (documentation, government filings, nondiscrimination requirements, etc.), many employers started to bemoan these new requirements of the DB plans by the ERISA. Also, according to a survey, DC plans cost no more than 3 percent of payroll, whereas DB plans can cost 5 to 6 percent of payroll (Kieso et. al., 2008, pp 1023). As an example, Teleflex, Inc cited the following reasons in their filing with Securities and Exchange Commission (SEC):
Integrative Forces in Dynamic Ecosystems of Entrepreneurship
Dr. Maryluz Ordonez Santos, University of Pamplona, Colombia
Luz Stella Arenas Perez, Francisco de Paula Santander University, Colombia
Analyzing the vision of entrepreneurship, beyond a traditional position, implies an ecosystem look, which assumes the convergence of various actors towards a common goal. Faced with this challenge, this article seeks to interpret the structure underlying the dynamic ecosystems of entrepreneurship, and presents an abstract model to define the study of its nature and address the complexity of the phenomenon that the complex relationships present between the actors and their environment. From a graphical view, the results are shown as a driving force with integrative view, which is obtained through a qualitative study, and supported methodologically in hermeneutics and ethnomethodology. The article aims to contribute both from a theoretical perspective, and from a practical point of view, by linking stakeholders in the ecosystem and contribute to the discussion an image that will allow a systematic review. One of the basic pillars of Entrepreneurship is developing the “Ecosystem” term, which refers to the study, analysis and explanation of the various complex relationships between institutions and entrepreneurs, with academic, social, political and economic environments. In the Ecosystem, strategies are developed to create successful relationships as a source capable of generating a competitive advantage. It is argued that universities contribute to the formation of entrepreneurs with the teaching of the knowledge that the environment calls for it; however, training programs, the definition and identification of the idea, methodologies and tools to build business models and business plans do not always give a seal of approval for business initiation. Given this inconsistency, it is interesting to remember the entrepreneurial action from two key concepts: the ecosystem and sustainability, for all systems of innovation and entrepreneurship work the way they should be. In the literature focused on issues of entrepreneurship, it is common that the terms ecosystem and sustainability have taken off in recent years. When referring about the culture of entrepreneurship, the local development and the competitiveness, in any social study is sought to understand the place that each player occupies as an individual, as a member of an organization, and as an association. It is from Kantis’ proposal (2008) that shows positive influence on the success by promoting entrepreneurial development, which adds behavioral determinants to broaden the base of competitive SMEs committed to regional profile enrichment with activities that build the productive structure of socioeconomic development. She analyzes the relationship of a series of variables that result in the accumulation of a knowledge platform, from the results of the research process, the consulting work in the field by promoting entrepreneurship, the pioneering studies with the Inter-American Development Bank, the research on experiences of international policy, the pilot projects of the Multilateral Investment Fund and the various programs and policy initiatives undertaken in different countries. In this line, Kantis (2014), facing the challenge for the emergence of innovative proposals, suggests the need to implement policies and regulations that can contribute decisively to the creation of more favorable conditions for entrepreneurship. A distinctive aspect of entrepreneurship policies is that various actors involved in formulating and implementing are needed; not only governments, but also universities, incubators and accelerators, investors and financial institutions, business entities and young entrepreneurs (p.19). Currently, for this author, the systemic weaknesses are still visible in the formation of human resources with abilities to undertake the problems of access to information on the business option as a career and life plan for the bulk of young people. The tacit knowledge barriers and obstacles to access support networks are flaws in the functioning of markets for technical assistance and, basically, flaws in the financing market of business ideas. To achieve the goal of research, this paper is divided into four parts: first, through a review of the contributions of literature, defining the theoretical framework in which are approached the components of the business ecosystem posed by Isenberg and the proposal addressed by Kantis, who have been identified with a history of the subject; second, the methodology used in research based on qualitative study is presented; then, the results of the investigation leading to an integrative view of the dynamic entrepreneurial ecosystem is presented. Finally, the most important conclusions are exposed. This study aims to contribute to this research, both from a theoretical perspective and from a practical point of view. This is how through a turbine figure, the Entrepreneurship Ecosystem is presented as a propelling force with integrated vision; where the main proposal of this figure, is the consideration of the constructs included therein, on the basis of the insights gained. The fundamental originality of the work lies in linking the actors involved in the ecosystem, and contributing to the discussion a proposal for systematic review. According to Daniel Isenberg, director of Babson Entrepreneurship Ecosystem Project (BEEP), there are twelve business ecosystem components: leadership, governance, culture, human capital, financial capital, entrepreneurial organizations, education, infrastructure, clusters, networks, support services and clients (Figure 1). Isenberg preliminary approach differs from the first reference to the term ecosystem (Moore 1993) with a predators and prey view, in which the ecosystem of business was defined as an area of interconnection and interdependence between economic agents, whose collective health was essential for the success and survival of organizations. From a more concise view, about the factors that make up an ecosystem of entrepreneurship, Isenberg poses the reviewing of the state of an ecosystem through general assessment of six domains: policy, culture, finance, human capital, supports and markets (figure 1).
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