Applied Innovation Alliance has worked with teams in many industries and touched product and process challenges of all types. Projects have included tactical innovation problem solving, failure analysis, failure prediction, et al and strategic innovation strategically evolving the future of business models, processes and technologies, et al. At the root of all innovations lies problem-solving and adaptive reasoning; no innovation occurs without these skills appearing. These two talents are a key part of how human intelligence is measured.
Today, these methods have the potential to increase human intelligence of those who chose to master them. In such instances it is possible to use another strategy, directly comparing the assessments of expert judges. This is what analysts do in an informal way when they interview the important practitioners involved on all sides of a peace process in order to.
An important complication in evaluating outcomes, especially when relying on expert judgment, is that all observers do not have access to the same information. Often, important parts of a peace process are private— for instance, deliberations among negotiators on one side of a negotiation are seen only by that side, and the progress of a problem-solving workshop is rarely recorded electronically, so only those present know what happened. Those present at important private moments have relevant information others lack, but they also often have motivations that can distort their memories and their reports, so that others may suspect the accuracy of their accounts.
There is no standard procedure for addressing this problem, but it helps to be aware of it, to search for corroboration from participants whose incentives to distort may be different, and to temper conclusions when it appears that much of the available information is biased in one way or another. There are well-known tradeoffs between reliability and validity. Attempts to maximize agreement between coders in order to enhance reliability accuracy , as in mechanical coding, may result in a distortion of the concept being assessed, thereby detracting from validity meaning. For example, it is possible to obtain highly reliable measures of concessions in a bargaining experiment, where concessions can be defined as the numerical difference between an offer made at one time and the offer the next time.
It is much more difficult to reliably quantify a concession made in an international negotiation, as it must be inferred from suggestions, exploratory proposals, and packages that combine several offers. Coding is typically an interpretive exercise and tends to result in a loss of intercoder agreement, although possibly enhancing validity. The extent to which it enhances validity depends, however, on the adequacy of the coding categories and on the sampling of appropriate materials. Analysts often invent coding systems to capture the essence of a phenomenon and thus enhance validity.
To enhance reliability they tend to adopt standard categories that can be used repeatedly. Standard categories may be applicable across intervention types e. Analysts often observe that the coding categories that seem most useful are not the same from one type of data to another.
We have noted that evaluation is complicated by the fact that short-and long-term definitions of success may be quite different. As noted, it is important to have short-term indicators of progress even for interventions intended mainly to have long-term effects.
This is so partly to provide interim indications of progress and also to allow for meaningful evaluation even in cases in which intervening events not brought about by the intervention throw the process of conflict resolution off its intended course. One way to develop interim indicators of progress for interventions that have a long time horizon is to postulate mechanisms believed to lead from the intervention to the desired long-term results and identify indicators that can be used to tell whether events are moving along the desired track.
For instance, practitioners of interactive problem-solving workshops as a way to improve intergroup communication over the long term might postulate that one mechanism leading to long-term effects such as formal agreements between the parties is through improved communication and trust between workshop participants from opposing sides and their advancement over time to more influential positions in their groups.
One could assess whether this mechanism is operating by examining various indicators, such as improved communication between the participants immediately after the workshop, continued communication between them a year or two later, the rise of workshop participants to positions of increased influence, and evidence of sensitivity to and accommodation of the opposite group in the policies they influence.
Using such indicators of intermediate progress improves on an outcomes-only approach to evaluation by adding a process element to the evaluation and by strengthening the case for a causal link between the intervention and long-term outcomes. Another important measurement issue is that of setting realistic expectations for interventions.
This issue is especially important for evaluating interventions that are intended to contribute to a peace process but are not expected to produce peace by themselves; however, the issue has not received much attention to date. A first step is to raise the issue—to ask practitioners in interviews and to discern from their writings what their expectations have been for the short- and long-term effects of particular interventions.
Their own expectations constitute one reasonable test of success. Of course, different practitioners may have different expectations, even for the same intervention. Conducting a dialogue among.
One reason is that practitioners may state unrealistically high expectations to get political support for intervening or unrealistically low ones to increase the chances that the intervention will be judged successful. Also, they may have expectations unrelated to the peace process that influence their statements.
In the course of the project leading to this book, we have encouraged scholars and practitioners to interact around the questions of interim indicators and reasonable expectations and have asked authors to address the issues explicitly. The chapters that follow help clarify these issues for several conflict resolution techniques.
Social scientists have developed many analytical techniques for analyzing claims about cause-and-effect relationships, and numerous textbooks have been written that classify the techniques and assess the strengths and weaknesses of each. The texts and typologies they present usually emphasize applications in a particular discipline.
For discussing the challenges of inference about the effects of international conflict resolution interventions, it is useful to group the methods into three broad categories: experiments and simulations, multivariate analyses, and case study methods. This section discusses the possibilities and limitations of each. The distinguishing feature of experimental methods is that a researcher deliberately manipulates an independent variable—a variable that is hypothesized to have an effect—and, controlling for other variables that might affect the outcome, observes the consequences.
All conflict resolution efforts are experiments in the sense that they are deliberate and intended to have an effect. Experimental methodology is devoted to. There is little experimental research on international conflict resolution because actual conflict situations do not permit experimental controls and because, for most types of intervention, conceptual models are not yet sufficiently well developed even to conduct laboratory simulations negotiation processes are an exception to this generality. The conditions for drawing strong conclusions from experiments are rarely, if ever, met with international conflict resolution interventions.
The main condition is that all variables that might affect the outcome are either explicitly manipulated or adequately controlled. Adequate control may be achieved either by holding a variable constant—a condition researchers may be able to approximate only in the laboratory—or by randomly assigning each situation in which an intervention might be tried to receive either the intervention or some control or comparison condition. Because these conditions are rarely met for studies conducted outside the laboratory, an alternative approach referred to as quasi-experimentation has been developed Cook and Campbell, Quasi-experimental research involves using surrogates for experimental manipulation.
For example, a quasi-experimental study might compare the consequences of an intervention in one situation to the consequences in another situation that is comparable in important ways. A researcher might compare the consequences of different efforts to mediate the same conflict at different times, thus achieving control over some of the important variables extraneous to the negotiation strategy Stedman, , used this strategy as part of his study of mediation in Zimbabwe in the s.
Quasi-experimental research methodology makes explicit the limitations of each research design and the most important threats to valid inference about causation that arise with each type of research design. It thus helps researchers evaluate the extent to which these threats can be ignored in a particular study, taking into account its features and the pattern of results obtained see Robson, Classical experiments, in which cases are randomly assigned to intervention conditions, generally do a better job than quasi-experiments in ruling out alternative hypotheses; for quasi-experiments and other research methods, it is important to specify each rival hypothesis and seek evidence to rule it out.
Experimental evidence is sometimes used to draw conclusions about international conflict resolution. Researchers subject individuals or small groups to controlled manipulation of variables believed to affect behavior in actual international conflict resolution situations, and use the results to test hypotheses about the effects of those variables. This strategy obtains the chief advantage of experimental method—the ability to rule out rival. A laboratory experiment, such as a study of the effects of stress on the accuracy of perception, may have very strong internal validity and potential relevance to real-world conflicts but be highly questionable in terms of its external validity.
One approach used to increase external validity is the simulation experiment. An attempt is made to preserve the rigorous features of experiments random assignment, controls while representing key aspects of the conflict setting of interest. By building in aspects of the actual setting, it is believed that the results will be relevant to that setting. Of course, this is an empirical issue that is best evaluated by comparing findings obtained in simulations with those obtained in the field for more on these issues see Guetzkow and Valadez, Experimental research on conflict resolution has been most useful for identifying particular aspects of complex interventions that are critical sources of variation in outcomes.
This progress has come primarily in research on small-group interventions such as negotiation, mediation, and interactive problem-solving workshops. Simulations typically include considerable detail to enhance external validity and a careful experimental design to allow statistical separation of the key variables posited to affect processes and outcomes and to enhance internal validity.
For example, studies simulating the conflict between the Greek and Turkish Cypriots have explored hypotheses about the impact of focusing on values in negotiation. Facilitation was found to produce more cooperative negotiations than fractionation Druckman et al. Some studies show that when a mediator is seen as having no stake in the outcome and when hostility between parties is high, pressure tactics leverage are more effective than rewards in producing concessions Harris and Carnevale, ; Carnevale and Henry, Other experiments show that mediators are more likely.
Also, agreements are likely to be more effective if the mediators encourage the parties to generate and test hypotheses about the sources of the conflict and to take ownership of any agreements that result Kressel et al. An illustrative finding on interactive problem solving comes from a simulation by Cross and Rosenthal , who recruited Palestinians and Israelis to participate in a discussion of several issues that divided these groups. Forty dyads were randomly assigned to one of four approaches to organizing the discussion: distributive bargaining, in which participants emphasize group positions and bargain about them; integrative bargaining, in which they identify interests and then seek to expand the alternatives; interactive problem solving, in which they identify needs and engage in joint problem solving; and a control condition in which participants receive no instructions on how to discuss the issues.
The study examined only attitudes, not negotiating behavior or outcomes. These examples illustrate how experimentation can be used to investigate the effects of well-defined aspects of conflict resolution interventions on attitudes and behaviors. Experimentation is well suited for clarifying the mechanisms responsible for effects and thus contributing to an explanation of why an intervention works the way it does. The usefulness of the approach is limited because the criteria for conducting strong experiments are too stringent for collecting and evaluating data on some types of conflict resolution interventions e.
Further, it is difficult to simulate the many aspects of international interventions. Nevertheless, experiments can make useful contributions to knowledge as part of a multimethod research strategy e. Insights from other research approaches can be evaluated in a more precise way with experiments, experimental hypotheses can be studied in field contexts, and the results from experiments can be compared to those obtained from other methods in a search for conclusions that do not depend on the research method.
The classical experiment is also a benchmark or a point of comparison in evaluating the results of nonexperimental methods of analysis. These methods measure aspects of the historical record of past international conflicts and conflict resolution efforts and search for regularities in that record that qualify as generic knowledge.
Researchers who use these methods typically examine a number of aspects of each of several interventions of a particular type. Their measurements may be qualitative, such as simple tabulations of whether particular conditions were present or absent, or they may involve numerical measurements e.
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Techniques of multivariate data analysis are designed to investigate the strengths of associations and sometimes the temporal ordering of events or indicators in order to support some hypotheses and rule out others concerning the causes of these associations and temporal orderings. Although most often used on datasets involving large numbers of separate events, the approach can also be applied to small numbers of events if many observations have been made of each. Thus, even case materials, if properly prepared, can be subjected to multivariate analysis.
A common use of multivariate analysis for research on international conflict resolution begins with the compilation of so-called events data on conflicts and on the efforts that have been made to resolve them. For example, Bercovitch and colleagues e. Each case is coded in terms of such indicators as type of mediator e.
These researchers used quantitative techniques to analyze the coded data and test hypotheses about relationships between the outcome of the intervention and conditions that may affect the outcome, such as the use of directive or passive mediation strategies, the nature of the issues as ideological or interest based, power imbalances between the parties, and early versus late intervention.
They also developed and tested various causal models of the connections among features of the disputes and the mediation outcome Bercovitch and Langley, This analytical approach makes it possible to examine the outcomes of mediation as a function not only of the mediation itself but also prior contextual conditions features of the dispute —something that is difficult to examine by simulation. The multivariate approach has also been used to study negotiation.
Using primary sources interviews with delegates and secondary sources. Researchers have used such data to define distinct types of negotiations Chasek, and to organize them along such dimensions as the size of the negotiation bilateral, trilateral, multilateral and the complexity of the issues Druckman, Such analyses provide an empirical basis for developing typologies based on profiles of negotiation characteristics. They may also enable practitioners to consult the historical record for past cases that may be instructive for present purposes. Multivariate datasets like these can be compiled for a wide variety of interventions.
For instance, they have been used to illuminate the effectiveness of economic sanctions Hufbauer et al. In all cases their usefulness depends on the relevance of the variables chosen, the validity of the coding, and the level of detail. The chief strength of the approach is that statistical analysis allows researchers to consider numerous possible causal mechanisms using more cases than they can evaluate with the unaided mind.
The chief weakness is that much of the richness of each case is lost when the case is reduced to a list of indicators.
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There is obviously a tradeoff between breadth and depth in case analysis, and multivariate data analysis normally exhibits both the strengths and the weaknesses of breadth. The above examples use a cross-sectional approach—that is, they analyze multivariate data in which each case is assigned a single value for each characteristic being coded, without regard to time. While the cross-sectional approach can, with sufficient data, probe complex contingent relationships among variables, it is limited for investigating causal mechanisms because the operation of these can only be assessed across time.
It is possible to apply statistical techniques of causal modeling to test the consistency of the data with a hypothesis about causal mechanisms e. Multivariate analysis can also consider change over time—and examine causal mechanisms more directly—by analyzing data arranged in a time series, in which the same variable is coded at many points in the history of a case to allow the study of temporal processes.
The goal is to develop a conceptually coherent account of a. For example, in a study of base rights negotiation between Spain and the United States in —, Druckman showed that the agreement resulted from a sequence of identifiable crises and turning points. The approach can also be used to identify a mechanism for the operation of an intervention that is consistent with the observed chain of events.
Hopmann and Smith showed that the outcomes of the — partial nuclear test ban talks resulted from certain actions taken by nations outside the negotiations. This multivariate time series research design is quite useful for identifying how the temporal pattern of interactions between the conflicting parties and interventions by external actors led to the ultimate outcome.
Historical data that are coded by time of occurrence can be used to evaluate the impacts of planned interventions and to trace the processes by which these impacts occur. The research approach is referred to in the technical literature as interrupted time series analysis. An example is a study of five mediation efforts in the conflict between Armenia and Azerbaijan between and Mooradian and Druckman demonstrated that each mediation effort had limited effects on the time series of events.
The pattern of conflictual events before the offensive October to March changed to a cooperative trend following the fighting May to September , only to turn again to conflict by October A long time series of events enables the analyst to ascertain the extent to which each intervention alters the trend and to consider whether the effects are immediate or delayed. Inference is complicated by the fact that the multiple interventions are not independent of each other—for example, the effect of a successful intervention may be due in part to the fact that past efforts failed.
In addition, the validity of the time series is sometimes hard to establish. Many of the problems of inference involved in time series analyses have been addressed by quantitative methodologists. For example, they have developed techniques for differential weighting of distant and recent events referred to as exponential smoothing , compensating for the dependence of events on similar events in the recent past statistical controls for autocorrelation , accounting for possible explanatory factors that are associated with each other stepwise regression for unique variance explained , and taking account of changes in the estimated subjective.
These techniques have been used in sophisticated forecasting methodologies e. They have not, however, solved some of the fundamental conceptual and measurement problems of multivariate time series analysis, such as the need to compare events with counterfactuals, the lack of valid measures for some important variables, and the lack of sufficient size or variation in the sample of historical events. Multivariate analysis techniques, like other methods, have their characteristic strengths and weaknesses. As already noted, their chief strength is that they can simultaneously consider more cases, and more aspects of a single case, than a human analyst can comprehend.
This capability is particularly useful for analyzing the effects of contextual factors on conflict resolution efforts because the potential effects are numerous and because useful indicators are available for many contextual variables. In such situations, multivariate analysis can reveal patterns that might otherwise go unnoticed. The chief limitations of the approach are those of the available data and concepts. The methods can only be applied when a sufficient number of historical cases or a sufficient richness of data on a single case exist for quantitative comparison—normally, dozens of data points are necessary to test a single bivariate hypothesis, and more are needed to test hypotheses involving a conjunction of several variables.
Also, the available data must include reasonably valid measures of the variables that are central to the desired analysis. Sometimes the variables for which valid measures are available for numerous cases are not the ones of the greatest theoretical or practical interest, and sometimes the variables of greatest interest are not well enough conceptualized to allow for valid measurement.
In such instances, multivariate analysis has obvious limitations. Multivariate analysis is particularly useful at the current state of theory development for uncovering patterns that deserve further analysis by other analytical methods. When patterns evident in cross-sectional studies suggest causal hypotheses, it may be useful to explore those hypotheses further by using simulation experiments or detailed analyses of individual cases through time.
Similarly, patterns that emerge from time series data are also worth further examination by other methods, particularly intensive examination of case material. A major value of quantitative research approaches is through the discipline they impose on thinking. The measurement efforts that these approaches demand e. The case study is one of the classical methods of political science, and its uses and limitations for making inferences are well known e. Our interest here is in refinements of the traditional case study approach that have been developed over the past two or three decades to increase the rigor of the approach and overcome some of its limitations, particularly the problem of noncomparability across cases and the difficulty of using case material to test hypotheses.
Traditional case studies can be useful by demonstrating that a particular case is inconsistent with an existing theory and thus stimulating scholarly research and rethinking by practitioners. However, the contribution of traditional case studies to cumulative knowledge has been limited by noncomparability across cases: there is typically no way to test the conclusions from one case study against evidence from other case studies because they fail to include information needed for the comparison.
The focus here is on two particular refinements to the case study approach: the method of structured, focused case comparisons, and process tracing. Both methods improve on the traditional case study by being more theoretically explicit. By stating in advance which variables are worth examining and which processes are worth tracing and by implication which are not , these approaches make it possible to focus case-based research and thus to build knowledge cumulatively.
Structured, focused case comparisons differ from the traditional case study approach in that cases are selected and case descriptions developed with particular theory-guided questions or conceptual issues in mind Lijphart, ; George and Smoke, ; George, ; Collier, ; Putnam, ; Faure, This allows the researcher to compare the cases on the central issues of interest. The structured, focused case comparison method cannot, as a rule, be applied to previously completed case studies because they usually lack information demanded by the protocol.
A well-known application of the structured, focused case comparison approach has been to test deterrence theory. Researchers select a set of cases they judge to represent successful and failed deterrence and then examine the historical evidence on each case to answer theoretically rel-. This research has focused especially on deterrence failures because a failure that occurred when all of the theoretical conditions for success were in place would call into question the principles of deterrence theory and because of the difficulties of establishing deterrence successes when a deterrent is successful, the result is often that nothing observable happens.
The structured, focused case comparison method requires a theory or conceptual framework that is sufficiently well specified to generate the list of factors or variables that must be considered in each case. The method is particularly attractive when a testable hypothesis exists along with unambiguous indicators of the relevant variables that are obtainable from available historical information. It also requires that several relevant cases are available. Sometimes, useful results can be obtained with fewer than a dozen cases—a contrast to the requirements of the multivariate quantitative approach.
Compared to traditional case study research, structured and focused case comparisons have the advantage of comparability: the same information is collected about each case using the same methods. Because only selected information is needed about each case, it may be possible to do more structured comparisons with a given set of resources than unstructured comparisons, but the method can miss information on aspects of the cases that are not presumed in advance to be important.
This is both the advantage and the disadvantage of research methods informed by explicit conceptualization. Structured and focused case comparisons can to some extent overcome this problem by being flexible about the way information is extracted from cases. Flexibility allows insights to be discovered in individual cases that may have been missed in the answers to structured questions. These insights can then be examined by collecting the necessary information on the other cases under study. Compared to the multivariate analysis approach, structured and focused case comparisons examine fewer variables and fewer cases the selection being made on theoretical grounds but provide much more detailed information on the variables they do examine.
Because of these differences, the multivariate approach has a comparative advantage for exploratory analysis, like traditional case studies; in contrast, structured and focused case comparison is comparatively well suited to testing hypotheses from theory and refining the theories it tests. It is sometimes possible to treat a set of case comparisons as a source of events data and to. For example, Table 3. Such a table of categorical data can be analyzed statistically in the manner of cross-sectional events-data analysis, although this particular data table may be small enough for adequate analysis by inspection.
Just as structured case comparison is a sort of qualitative analog to cross-sectional events-data research, the process-tracing approach is a sort of qualitative analog to time series data analysis. The process-tracing approach can allow for multiple tests of the same hypothesis in a single case, thus dampening the criticism that a single case study cannot test a hypothesis because a single test is never statistically convincing. However, repetitions of similar conditions in the course of the same conflict are not independent in a statistical sense.
This situation presents a threat to the validity of cause-and-effect generalizations drawn from process-tracing studies, analogous to threats to validity in quantitative time series research. Process tracing could, in principle, be used in a way that allows statistical tests of the relative explanatory power of different theories; however, we are not aware of any such applications. It is important to note that, for drawing inferences about learning and other experience-dependent processes, statistical nonindependence between events is not a problem and in fact is necessary for a case to be informative Bennett and George, forthcoming.
An important difference between enhanced case study approaches and multivariate data analysis is that the former requires an explicit theory or conceptual framework while the latter does not. The enhanced case study approaches are an improvement over traditional case studies precisely because of their greater conceptual explicitness. It is useful to make a similar distinction regarding how multivariate quantitative research is conducted. This approach can be employed as a form of nearly pure empiricism by simply analyzing whatever indicators are available across a set of cases to see what regularities emerge.
However, the results will be unsatisfying if the available indicators do not include measures of the important variables affecting outcomes. Thus, multivariate analysis is likely to yield more useful results if concepts are made explicit—if an effort is made at the start to specify the key variables and to develop. This form of multivariate analysis is enhanced in much the same way as enhanced case study methods are.
We are suggesting here that there can be some convergence between case-based and multivariate quantitative approaches. We further believe that progress in understanding depends on such convergence. Both case study and multivariate research approaches to international conflict resolution were initially used in an exploratory mode to examine the available evidence either case material or quantitative indicators and to search for empirical regularities. This empiricist strategy has not led to strongly supported generic knowledge, but it has generated hypotheses that can be tested with more carefully focused research, using either case-based or quantitative research modes.
It has also led to refinements in understanding, in which bivariate hypotheses about relationships between interventions and outcomes give way to conditional generalizations. These are statements or propositions that specify the conditions sometimes called moderating variables under which such relationships are likely to occur. Propositions about these conditions contribute to more nuanced knowledge, as exemplified by contingent theories of conflict resolution Fisher, Because of the limitations of each approach, progress seems most likely if both methods are used. One promising way to do this is to apply quantitative methods to data gathered by case study methods.
This has occasionally been done with traditional case study data e. The approach can be applied to several of the topics in this book, including the use of threats of force Chapter 3 , electoral system design Chapter 11 , and language policy Chapter Quantifying case study data may make for more precise comparisons between theories in terms of how well they explain available data and may also show more clearly where the data are inconclusive.
Following a similar logic, statistical techniques of time series analysis can be applied to data from process-tracing case studies. It is also possible for quantitative researchers to build on the results of case comparisons by designing large-N studies that focus on the key variables identified in case-based research. Another promising strategy for combining research methods is to use the results of multivariate studies to guide the development of protocols for structured case comparisons and process-tracing studies.
Whenever multivariate research identifies a statistical regularity, it generates a hypothesis that could be tested in case-based research. Case study researchers may find the indicators from multivariate research too restrictive for their way of thinking, but they have the option to add depth to these variables in their.
We believe that this sort of interplay between methods is much more likely to be productive than a continuation of arguments about which method is superior, such as have frequently appeared in the literature on research methodology in international relations. The challenges of evaluating efforts at international conflict resolution and our suggestions for how to meet those challenges are summarized in Table 2.
This work leads to three major conclusions.
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First, theory development is key to addressing many of the most serious challenges of building knowledge about what works in international conflict resolution. This point has also been emphasized by others e. Second, understanding is most likely to progress through a dialogue between theory and. Frame the topic conceptually and historically; look for hidden and incomplete cases.
Use multiple analytical methods experimentation, multivariate analysis, case study. The practical concern with how best to develop generic knowledge about what works in international conflict resolution leads to a perhaps surprising conclusion: there is a critical need to develop theory.
This conclusion follows from the recognition that improvements in the quality of theory would help meet each of the major challenges of evaluation. The needed theories would combine three elements. First, taxonomies, which can focus on types within a kind of intervention e. Stages of Conflict Management Stage 3: Intention 1. Competition 2. Collaboration 3. Compromise 4. Accommodate 5. Avoiding 13 Stages of Conflict Management Stage 3:Intention 1.
Stages of Conflict Management Stage 3:Intention 2. Stages of Conflict Management Stage 3:Intention 3. Stages of Conflict Management Stage 3: Intention 4. Stages of Conflict Management Stage 3: Intention 5. The behavior stage incorporates the announcements, activities, and responses made by the conflict parties. Stages of Conflict Management Stage 5: Outcomes Decreased group performance Increased group performance performance 21 Case Study on 22 Objective To give business, social and historical perspective of the reasons behind the series of incidents that took place due to the conflict at the Manesar plant of Maruti Udyog Limited in which lead to the murder of a senior HR executive and extensive damage to property.
Wage disparities between the regular and contracted workers. Lack of trust between the HR staff and the workers Lack of connectivity and active communication between the management and workers Lack of intelligence and information Possible collusion of local police, retrenched workers and politicians Both permanent and contracted workers were unsatisfied 25 You just clipped your first slide! Clipping is a handy way to collect important slides you want to go back to later.
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