Incorporating polarity of relationships in ISM and TISM for theory building in information and organization management

International Journal of Information Management 43 (2018) 38–51

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International Journal of Information Management journal homepage: www.elsevier.com/locate/ijinfomgt

Incorporating polarity of relationships in ISM and TISM for theory building in information and organization management

T

Sushil Department of Management Studies, Indian Institute of Technology Delhi, Vishwakarma Bhawan, Shaheed Jeet Singh Marg, New Delhi 110016, India

A R T I C LE I N FO

A B S T R A C T

Keywords: Information and organization management ISM Polarity Problem structuring TISM TISM-P Theory building

Some mapping methods are used for the conceptualization of theories in information and organization management which may help in modeling both feedback and hierarchical structures. Total Interpretive Structural Modelling (TISM) is an enhancement of interpretive structural modeling (ISM) to explore hierarchical models that aid in theory building in information and organization management. It is based on pair comparison method to generate a hierarchical digraph, which is then translated into the interpretive model by interpreting both nodes and links in the digraph. The relationships in both ISM and TISM could be binary or fuzzy. However, a missing dimension of these structural models is the polarity or direction of relationships. A relationship between a pair of elements could be having positive or negative polarity. This aspect has been widely used in the feedback structures as causal influence diagrams (CIDs) in system dynamics methodology. This paper is using the learning from CIDs to introduce polarity of relationships in ISM and TISM. The polarity of relationships will further refine ISM/TISM as a more explanatory model for theory building. This enhanced model not only provides hypothesis formulation simply as a driver-dependence relationship but also addresses whether the driving variable(s) will influence the dependent variable positively or negatively. The paper illustrates modified TISM process incorporating polarity with an example in the context of information and organizational change management and discusses the nuances of such an enhanced model.

1. Introduction Theory building in information and organization management requires mapping of mental models and hypothesized relationships. This mapping has been aided by a range of methods such as cognitive mapping (Eden, 1988), system dynamics (Forrester, 1968), and ISM/ TISM. Both Interpretive Structural modeling (ISM) (Warfield, 1974) and Total Interpretive Structural Modelling (TISM) (Sushil, 2012, 2016) are interpretive methods to crystallize ill-structured mental models into well-articulated hierarchically structured conceptual models. Both researchers and practitioners have used these as enabling aids in conceptualization and theory building. The fundamental questions in theory building such as ‘what’, ‘how’ and ‘why’, as given by Whetten (1989), are addressed by these interpretive methods. ISM effectively addresses the first two questions, i.e. ‘what’ and ‘how’ regarding elements and their hierarchical (driver-dependence) relationships, whereas TISM further addresses the question ‘why’ and brings more explanatory power in the context of theory building. There is yet another dimension in theory building and formulation of a hypothesis, i.e., the polarity of relationships. This dimension explains the direction of the relationship between independent and dependent variables, i.e.,

E-mail address: [email protected]. https://doi.org/10.1016/j.ijinfomgt.2018.06.003 Received 14 May 2018; Received in revised form 22 June 2018; Accepted 22 June 2018 0268-4012/ © 2018 Elsevier Ltd. All rights reserved.

whether the independent variable(s) is/are influencing the dependent variable positively or negatively (Eisenhardt, 1989). Such positive and negative relationships are extensively used in theory building in the context of information and organization management. Even though the application of both ISM and TISM is in vogue, the literature on these modeling methods is almost silent on the concept of polarity in relationships, which is integral to the process of conceptualization and theory building. This concept of polarity is extensively used in the context of System Dynamics (SD) methodology that portrays feedback structures in the form of causal influence diagrams (Forrester, 1975; Sterman, 2000). The learning and significance of polarity in system dynamics are reflected to examine and define the polarity of relationships in ISM and TISM to make them far more powerful by portraying the direction of relationships in a more explanatory manner. Hypotheses conceptualizing positive and negative relationships have been developed by some researchers in information and organizational management. For example, Lane, Salk, and Lyles (2001) hypothesized that absorptive capacity and learning relate positively to joint venture performance. Whereas Morris and Cadogan (2001) related partners conflict with quality of joint venture marketing strategy both positively (functional) and negatively (dysfunctional).

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digital government trustworthiness (Janssen, Rana, Slade, & Dwivedi, 2018), among other applications. It has also been applied to supplier development enablers (Dalvi & Kant, 2017), lean, green and resilient supply chain management (Cherrafi, Elfezazi, Garza-Reyes, Benhida, & Mokhlis, 2017; Ruiz-Benitez, López, & Real, 2017), and so on. However, it was observed that despite extensive application of ISM in a variety of areas these models (developed by ISM) are partly interpreted. ISM is a graph-based method (a graph consists of nodes as well as links), but it has been interpreting only nodes and partly interpreting links by defining the contextual relationship. The total interpretation of both nodes and links has been attempted in the form of Total Interpretive Structural Modelling (TISM) by Sushil (2012). After the advent of this enhancement of ISM, it has been applied in some areas such as drug selling (Wasuja, Sagar, & Sushil, 2012), sustainable manufacturing (Dubey, Gunasekaran, Sushil, & Singh, 2015), organizational excellence (Agarwal & Vrat, 2015), and strategic performance management (Yadav, Sushil, & Sagar, 2015). Some other areas of application of TISM are green supply chain management (Shibin et al., 2016), workplace flexibility (Yadav, Rangnekar, & Bamel, 2016), inland waterborne transport (Kumar, Haleem, Qamar, & Khan, 2017), agile performance in healthcare (Patri & Suresh, 2017), sustainable supply chain performance (Sandeepa & Chand, 2018; Shibin, Gunasekaran, & Dubey, 2017), and so on. Further, it is noted that a good number of applications of both ISM and TISM that have been published had technical errors. The correctness of these models can be checked by following the guidelines provided by Sushil (2016). It has also been realized that the key challenges in the application of both ISM and TISM are a large number of paircomparisons to be made by experts and cumbersome multi-order transitivity checks are to be carried out. These challenges can be obviated by a modified ISM/TISM method that has been developed to carry out transitivity checks along with the direct pair comparisons (Sushil, 2017a). This modified method provides a substantial reduction in direct pair-comparisons by eliminating redundant comparisons that can be easily derived by the transitivity logic. Apart from the development of TISM and other recent improvements in the ISM/TISM method, attempts had been made to obtain the strength of each pair relationship using fuzzy set theory (Zadeh, 1965). An early initiative in this direction was taken by Saxena et al. (1992) to provide a framework of fuzzy ISM. Recently, Khatwani, Singh, Trivedi, and Chauhan (2015) provided the methodology of fuzzy TISM, which has been applied in some cases (Mohanty & Shankar, 2017). In addition to methodological advancements, innovations in implementation of ISM and TISM have been made in terms of empirical validation (Anbarasan & Sushil, 2018; Bishwas & Sushil, 2016; Srivastava & Sushil, 2015), factor analysis to derive the elements for ISM/TISM (Chatterjee, Kar, & Gupta, 2017), anecdotal evidence in verifying the model in real life (Singh & Sushil, 2017), and so on. One recent advancement has been made to use TISM as a basis for deriving weights of criteria in MCDM methods by taking their driving power as the basis and used it in conjunction with Interpretive Ranking Process (Sushil, 2009) in the form of the TISM-IRP process by Sushil (2017b, 2018). Despite these developments, it is noticed that almost none of the past works on ISM/ TISM has taken into consideration the polarity of relationships, which has been used in developing causal influence diagrams (CIDs) as a conceptualization tool in system dynamics methodology. A brief review of the same is given in the next section to provide a basis and inspiration for considering the issue of the polarity of relationships in the ISM/TISM process.

Dhir and Sushil (2017) related constructs from transaction cost and social exchange paradigms with modification and termination flexibilities of cross-border joint ventures both positively and negatively. The main objectives of this paper are: (i) To propose an enhanced methodology of ISM/TISM incorporating polarity of relationships to aid theory building in information and organization management. (ii) To refine the classification of variables in the conceptual framework as driver, linkage, autonomous and dependent variables with a positive or negative orientation. (iii) To identify the positive and negative paths from driver variables to dependent variables, via linkage or intermediate variables in the management of information and organizational change. The paper first gives a selective review of evolution and application of ISM/TISM methodology. It also briefly reviews system dynamics methodology to bring out the significance of polarity of relationships. The methodology of incorporating polarity of relationships in modified ISM/TISM process with simultaneous transitivity checks (Sushil, 2017a) is elaborated. It then gives an illustrative example in the context of information and organizational change management to develop a TISM model of criteria for evaluating change propositions with the polarity of their relationships. Finally, it provides a discussion of its implications and concludes with limitations and directions for further research. 2. Literature review A selective review of evolution and application of ISM/TISM is first provided, which is supported by an overview of SD methodology to highlight the significance of polarity of relationships. This review has led to the identification of a gap area of the polarity of relationships which has not yet been effectively addressed in the ISM/TISM process. 2.1. Evolution and application of ISM/TISM The paired comparison method for unearthing the hierarchical structure among a set of elements/variables was introduced by Warfield (1974). He effectively utilized the paired relationships in a directional frame of reference using a contextual relationship to portray the hierarchical structure among them in the form of a digraph. This abstract digraph is interpreted regarding elements and their directional relationships as Interpretive Structural Model. The paired comparisons are used to minimize cognitive overload but require a system engineering approach to synthesize them in the form of a reachability matrix with transitivity checks. The reachability matrix indicates the reachability of one element to the other elements in the set. The transitivity implies that if element i reaches element j and element j reaches element k, then element i transitively reaches element k. The algorithms for transitivity checks and hierarchical partitioning have been presented in Warfield (1990). Some early applications of ISM are reported by Jedlicka and Meyer (1980), Malone (1975), Mandal and Deshmukh (1994), Saxena, Sushil, and Vrat (1990, 1992), and Sharma, Gupta, and Sushil (1995). The indirect relationships have been analyzed by using MICMAC method by Saxena et al. (1990). A large number of applications of ISM and MICMAC have been made in the extant literature. The applications of ISM have largely been made in the context of operations and information management. Some recent applications of ISM are related to IT enablers (Thakkar, Kanda, & Deshmukh, 2008), worldclass manufacturing (Haleem, Sushil, Qadri, & Kumar, 2012), supply chain risk (Venkatesh, Rathi, & Patwa, 2015), and supply chain resilience (Jain, Kumar, Soni, & Chandra, 2017). In the context of information management, it has been applied to study factors of information systems project failure (Hughes, Dwivedi, Rana, & Simintiras, 2016; Hughes, Dwivedi, & NP, 2017); hierarchy of factors related to innovation using big open linked data (Dwivedi et al., 2017), and

2.2. Polarity of relationships in system dynamics System Dynamics (SD) methodology is a dynamic simulation method to test the dynamic hypothesis in any system. The dynamic hypothesis is portrayed in the form of causal influence diagrams (CIDs) to present the feedback structure logically. The SD methodology was 39

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first introduced by Forrester (1958) in studying the dynamic behavior of industrial systems in the form of Industrial Dynamics. Later this was applied to study urban dynamics and world dynamics (Forrester, 1969, 1971). The comprehensive details of SD can be seen in some past works (Coyle, 1983; Gottschalk, 1983; Morecroft, 1988; Richardson & Pugh, 1981; Sushil, 1993; Sterman, 2000; Wolstenholme & Coyle, 1983; Wolstenholme, 1988). The aim here is not to review SD and its widespread applications, but to highlight the issue of the polarity of relationships in CIDs. The start point of any CID is the basic variables (as taken in ISM/TISM) which may be qualitative, but need to be quantified later as stock and flow variables in the simulation model. The next step is to establish a causal relationship between a pair of two variables defining its polarity, which could be either +ve or −ve in nature. By linking together many such causal relationships a feedback loop can be derived, which could again be +ve or −ve in nature. Senge (1990) has effectively used the +ve or reinforcing causal loops and −ve or balancing loops to portray different generic structures to study the dynamic behavior of systems such as limits to growth, shifting the burden, fixes that backfire, and so on. The +ve feedback loop exhibits exponential behavior, whereas the −ve feedback loop generates goal seeking asymptotic behavior. A fuzzy simulation of causal loop diagrams is exemplified by Pankaj, Seth, and Sushil (1992). The system dynamics methodology, incorporating +ve and –ve feedbacks, has been effectively used in information and organization management. Georgantzas and Katsamakas (2008) sketched the use of system dynamics in information system (IS) research linking the effects of IS on people, organization, and markets. They have also given directions for IS research with SD regarding the criteria and themes to be considered for this kind of research. Another critical review by Tako and Robinson (2012) captures the use of both discrete event simulation and SD as decision support systems for logistics and supply chain management. An SD model is developed by Nazareth and Choi (2015) to examine information security management strategies and concludes that higher payoff can be attributed to security detection tools in comparison to deterrence investment. There are many more areas in information and organization management that can be conceptualized regarding dynamic hypotheses and its validation using SD methodology. The fundamental basis of the feedback structure is a causal relationship with +ve or −ve polarity. This insight about the polarity of the relationship between a pair of variables has acted as inspiration for examining its use in ISM/TISM process. A brief review of the evolution of ISM/TISM and polarity of relationships in SD modeling indicates a gap in ISM/TISM methodology. Both ISM and TISM are applied as conceptualization and theory building methods. In theory building and hypothesis formulation in information and organization management, the polarity of driver-dependence relationships is invariably utilized and empirically tested, which is the main justification for the enhancement of methodology presented in this paper. This justification is supported by select studies in information and organization management reported in the extant literature. For example, Thomas and McDaniel (1990) while interpreting the strategic issues using CEOs’ responses tested the polarity of relationships between strategy and information processing structure related with top-level teams. The impact of IT acceptance on individual performance has been analyzed in the context of Singapore by Igbaria and Tan (1997) using the hypotheses that indicate the polarity of relationships. Lee, Park, and Han (2008) investigated the effects of online customer reviews on product attitude using polarity of relationships from an information processing perspective. The positive and negative relationships among factors influencing information sharing and inter-organizational relationships in the context of the supply chain have been empirically tested among the top Taiwanese firms by Cheng (2011). Many such studies indicate the polarity of relationships among factors in theory building, whereas ISM and TISM as theory building methods have not captured it in the past. This gap area of the polarity of relationships is addressed in this paper, and an enhanced methodology of ISM/TISM is provided and illustrated

in the context of information and organizational change. 3. Enhanced TISM methodology with polarity of relationships As TISM is an extension of ISM the enhanced methodology is provided for TISM as a more comprehensive method; the same at a reduced level can also be applied for ISM applications as ISM-P (ISM with the polarity of relationships). The modified ISM/TISM method with simultaneous transitivity checks (Sushil, 2017a) is taken as a basis to describe the steps of this enhanced methodology with the polarity of relationships. The major steps of this enhanced TISM methodology with the polarity of relationships are summarized diagrammatically in Fig. 1. It may be noted that steps II, III, IV, VI, VII, and VIII are different than the existing process due to the introduction of polarity and steps IX–XI are additional in the enhanced process. Only steps I and V are similar to the existing process. All the steps of the enhanced TISM process are elaborated as follows: Step I: Define Elements, Contextual Relationship, and Interpretation As per the existing TISM process, first, the elements (to be hierarchically arranged) are identified with the contextual relationship used to pair-compare the elements, which is also to be qualified with the associated interpretation. Step II: Develop Successive Comparison Questionnaire with Transitivity Checks and Polarity of Relationships The elements need to be pair-compared in a successive manner as described in Sushil (2017a). Minimum first two comparisons (1, 2 and 2, 3) are to be carried out by experts to check transitivity of the relationship (1, 3) further. With each pair comparison, the direction of the relationship is to be established as forward (i–j), backward (j–i), both ways (i = j), and no relationship (0). If there is a relationship between a pair of elements i and j, then its polarity also need to be specified, i.e., +ve or −ve. While making the transitivity check, the polarity of the transitive link(s) is to be derived. For example, if i → j is +ve and j → k is −ve, then i → k is −ve. Step III: Obtain Pair-Comparisons with Simultaneous Transitivity Checks The direct pair-comparisons in the questionnaire are to be made by experts and transitivity checks on specified questions to be done by the researcher/facilitator or the computerized system. If the transitivity of a relationship is ‘No’, then expert comparison for that pair is to be made. In both the direct and transitive comparisons, the polarity of relationship (+ve or −ve) is to be specified. Step IV: Development of Successive Comparison Digraph and Transitive Reachability Matrix with Polarity and Convert it into Reachability Matrix (without polarity) The direct pair-comparisons and transitivity checks with polarity can be visualized by successive comparison digraph and transitive reachability matrix with polarity. All comparisons marked in the questionnaire are marked on the digraph (in a step-by-step manner) and entered as +1, −1 or 0 in the transitive reachability matrix with polarity as given below:

• i-j with +ve polarity : +1 in ij cell and 0 in ji cell • i-j with -ve polarity : - with −ve polarity : −1 in ij cell and 0 in ji cell • j-i with +ve polarity : +1 in ji cell and 0 in ij cell • j-i with -ve polarity : - with −ve polarity : −1 in ji cell and 0 in ij cell • i = j with +ve polarity : +1 in both ij and ji cells • i = j with −ve polarity : −1 in both ij and ji cells • 0 (no relationship) : 0 in both ij and ji cells The reachability matrix with +1, −1 and 0 entries is to be converted into binary reachability matrix with 1 and 0 entries (+1 and −1 are entered as 1) with transitive relationships marked. Step V: Carry Out Hierarchical Partitioning of the Reachability Matrix The original algorithm using reachability set, antecedent set and 40

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Fig. 1. Broad Steps of Total Interpretive Structural Modelling with Polarity of Relationships (TISM-P).

The experts may be contacted back with the direct interaction matrix to interpret the links mentioned in it. Wherever there is +1 or −1 entry in the matrix, interpretation is to be provided to get the interpretive matrix (Sushil, 2005) with polarity. Step VIII: Obtain TISM with Polarity (TISM-P) Both the nodes and links in the digraph need to be interpreted. The nodes are interpreted by the elements identified in Step I and interpretation of respective links (from Step VII) is superimposed from the interpretive matrix. The interpretation of both nodes and links gives TISM with the polarity of relationships. Step IX: Classification of Elements The sum of rows(s) in the reachability matrix (in Step IV) gives the

intersection set for all the elements is used for hierarchical partitioning. The elements having same reachability and intersection sets are taken at the highest level, and the process is repeated after iteratively removing these elements till all the elements are classified into different levels of hierarchy. Step VI: Prepare Hierarchical Digraph and Direct Interaction Matrix with Polarity of Relationships Arrange the elements in hierarchical levels and link them with direct as well as select transitive links as per reachability matrix with polarity. Specify polarity of all links in the digraph as well as in the interaction matrix. Step VII: Prepare Interpretive Matrix with Polarity 41

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driving power, and the sum of column(s) gives the dependence of respective elements. These can be mapped to a driving power v/s dependence graph (as per MICMAC analysis) and classified as driver, linkage, autonomous and dependent elements. These can be further qualified as elements with +ve or −ve orientation based on +ve/−ve driving power and +ve/−ve dependence as obtained through Reachability Matrix with polarity. Step X: Grouping of Elements on TISM Model The driver, linkage, autonomous and dependent elements with +ve or −ve orientation can be placed in groups on the TISM model for better comprehension. Step XI: Identify Paths with Polarity The paths from driver elements to dependent elements via linkage/ intermediate elements can be identified and their polarity to be examined. All the above steps have been illustrated with the help of an example in the context of information and organizational change management in the next section.

Table 1 Elements, Contextual Relationship and Interpretation. Element No.

Elements (Change Criteria)

Contextual Relationship

Interpretation

1 2 3 4

Impact Proliferation Cost to be incurred Difficulty and complexity level Acceptability Awareness Preparedness Timeframe for implementation

Change criterion A will influence change criterion B (positively or negatively)

How or in what way change criterion A will influence change criterion B?

5 6 7 8

by the researcher/facilitator, and in case of transitivity, polarity has also been derived logically using the polarity of previous relationships. If any transitivity check depicts ‘No’, then the concerned pair is subject to expert opinion. Step III: The pair comparisons are obtained from a select group of experts from the organizations undergoing information and organizational change and transitivity checks are done as depicted in Appendix A. For transitivity checks; the successive comparison digraph is prepared as given in the next step. Step IV: The successive comparison digraph is prepared along with the answering of the questionnaire to have a visualization of the relationships and facilitating transitivity checks with polarity as portrayed in Fig. 2. Side-by-side the relationships are entered as ‘+1’, ‘−1’ or 0 in the Reachability Matrix with polarity as shown in Fig. 3. A color coding is done both in the digraph and reachability matrix to visualize direct and transitive relationships with +ve or −ve polarity. The reachability matrix with polarity is converted into reachability matrix (without polarity) by replacing entries with ‘+1’ and ‘−1’ by ‘1’ entry as exhibited in Fig. 4. Step V: The reachability matrix shown in Fig. 4 is the fully transitive reachability matrix. This matrix is used as a base for hierarchical partitioning; different iterations of the same are illustrated in Appendix B (Table B1), and the level-wise placement of all the eight information change criteria is summarized in Table 2. It can be observed from Table 2 that the change criteria ‘Impact’ and ‘Proliferation’ are at level I (highest level) and ‘Difficulty and complexity level’ and ‘Awareness’ are at level V (lowest level). Step VI: The elements (information change criteria) ‘1’ to ‘8’ are arranged level-wise as per the hierarchical partitioning and connected to each other with links depicting the polarity (+ve/−ve) as portrayed in Fig. 5. Select transitive links (having distinct influence) are retained and rest other transitive links are dropped while preparing the hierarchical digraph. The final digraph is translated first into binary interaction matrix with polarity (Fig. 6(a)). It is interesting to note that the direct links in the successive comparison digraph may appear as transitive links in the hierarchical digraph or vice versa, as the hierarchy was not known while making the successive comparisons. For example 6 → 5 is appearing as a direct link in successive comparison digraph (Fig. 2), whereas the same is appearing as a transitive link in the hierarchical digraph (Fig. 5). Similar is the case with link 4 → 5. Step VII: The experts’ knowledge is used to convert all the +1/−1 entries in the binary interaction matrix into the interpretive matrix (Fig. 6(b)) giving an interpretation of each link. For example, element 1 (Impact) creates ‘motivation in other areas’ to influence element 2 (Proliferation). Similarly, interpretation of all the links in interaction matrix is provided. Step VIII: The nodes in the hierarchical digraph (Fig. 5) are interpreted as elements (as given in Table 2) and links are interpreted from the interpretive matrix (Fig. 6(b)). The interpretation of the nodes and links gives the final total interpretive structural model of information change criteria with the polarity of relationships (Fig. 7). In this case,

4. Illustration: information and organizational change The proposed enhanced methodology for TISM with the polarity of relationships (as given in the previous section) is illustrated with an example of criteria to be used for evaluation of informational change initiatives in organizations. For example, an organizational change may be in the form of mergers and acquisitions (M&A), which may require integration of information systems (IS) during the post-M&A integration process. Such business and information technology (IT) alignment has been analyzed by Baker and Niederman (2014) using multiple case design. The post-merger challenges in the case of IS implementation project has been examined concerning organizational identity by Vieru and Rivard (2014) using an interpretive approach. Another aspect of the change management of IT implementation projects is related to team learning (Akgün, Lynn, Keskin, & Dogan, 2014). The selection of an information delivery model to find the best user for the information from business analytics is taken up by Martin, Lakshmi, and Venkatesan (2014) using fuzzy MCDM technique. The business-IT alignment requires different types of models for successful change; a framework considering different criteria has been tested on such models by ElMekawy, Rusu, and Perjons (2015). There are many such information linked organizational change considerations that have been studied in the past using empirical analysis, case research, and interpretive methods. Usually, management first identifies multiple information integrated change initiatives which can be subsequently ranked by using relevant change criteria. The step-by-step development of TISM with polarity (TISM-P) for these criteria is described in the following steps. Step I: By going through the literature on information and organizational change management and discussion with experts, eight change criteria have been identified. These criteria include impact created by the information change initiative, its proliferation in other areas, the cost to be incurred for implementing change, and level of difficulty and complexity of the information and organizational change programme. Further, it is important to consider the acceptability of information change to various stakeholders (employees, partners, etc.), awareness of change among stakeholders, level of preparedness of information change regarding required training, etc., and required time frame for the information change implementation project. These criteria are summarized in Table 1. The contextual relationship is regarding “influence” (+ve or −ve), and the interpretation is about how or in what way one criterion influences the other as given in Table 1. Step II: A questionnaire for successive pair-comparisons with the polarity of relationships is shown in Appendix A. In this questionnaire, the direct successive comparisons 1,2; 2,3; 3,4; ….; 7,8 are to be obtained from experts dealing with information linked organizational change. The transitive comparisons in between have been carried out 42

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Fig. 2. Successive Comparison Digraph with Direct and Transitive Links and Polarity of Links.

Fig. 3. Reachability Matrix with Polarity of Relationships.

Fig. 4. Transitive Reachability Matrix. 43

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variables in the fourth quadrant (low driving power - low dependence) in this case. Further, the orientation of these criteria (+ve/−ve) is obtained by +ve/−ve driving power and +ve/−ve dependence from reachability matrix with polarity (Fig. 3). The criterion 6 (Awareness) has higher +ve driving power (4) in comparison to −ve driving power (2) and thus treated as a +ve driver. Whereas, criteria 3, 4 and 8 having higher −ve driving power (4 each) in comparison to the +ve driving power (1, 2 and 1 respectively) considered as −ve drivers. The linkage criteria also have higher +ve driving power and considered as +ve linkage criteria. The outcome criteria 1 and 2 have higher +ve dependence (4 each) in comparison to −ve dependence (3 each) and are treated as +ve outcomes. Further, it is observed that −ve dependence is coming through −ve drivers, thereby indicating the +ve orientation of the outcome variables. It may be noted from Fig. 8 that the criterion 6 (Awareness) is the only +ve driver and the other three driver criteria 3, 4 and 8 (i.e., cost, difficulty/complexity, and time frame, respectively) are of −ve orientation. All the linkage criteria and dependent criteria or outcomes are of +ve orientation in this example. Step X: This classification, as obtained in Fig. 8, is superimposed on TISM as shown in Fig. 9. Fig. 9 gives the classification of variables with their +ve/−ve orientation and interrelationships with polarity in one diagram. This kind of representation gives insight into theory building. For example, −ve drivers influence the linkage criteria negatively, which in turn influence the outcomes positively. Similarly, +ve driver influences the linkage criteria positively, which further influence the outcome positively. The TISM with polarity will thus help in the formulation of both the positive and negative hypotheses, with mediation effect through linkages. Step XI: The four driver criteria (three −ve and one +ve) are taken as the start point to trace the paths of their influence on outcomes through intermediate variables directly influenced by them as shown in

Table 2 List of Elements (Information Change Criteria) and their Levels in TISM. Element No.

Elements (Information Change Criteria)

Level in TISM

1 2 3 4 5 6 7 8

Impact Proliferation Cost to be incurred Difficulty and complexity level Acceptability Awareness Preparedness Timeframe for implementation

I I IV V III V II IV

the ‘Awareness’ criterion negatively influences the ‘cost' and 'time frame' of the change initiative, and it affects ‘Acceptability’ in a positive manner. With higher ‘Acceptability’ people accept the training on new information systems (IS), thereby increasing the ‘Preparedness’ is a positive direction. Thus, the change initiative will have higher ‘Impact’ with better training and information infrastructure. Higher ‘Impact’ gives more motivation, and better ‘Preparedness’ gives more acceptability in other areas so that the 'Proliferation' of change would take place. This TISM model with polarity (TISM-P) is more explanatory both regarding the interpretation of relationships and their polarity than ISM. Step IX: The information change criteria are classified by placing them on driving power-dependence graph. The driving power and dependence are obtained from the reachability matrix (Fig. 4). In this manner, the criteria are classified as driver criteria (high driving power - low dependence), linkage criteria (medium driving power - medium dependence), and dependent criteria or outcomes (low driving powerhigh dependence) as depicted in Fig. 8. There are no autonomous

Fig. 5. Digraph after Hierarchical Partitioning with Polarity of Links.

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Fig. 6. Interaction Matrix (a) Binary Matrix with Polarity (b) Interpretive Matrix.

controlled to improve the success of the information linked change programme. This kind of analysis needs to be reflected in the evaluation of any information and organizational change programme and development of the theory of its evaluation.

Table 3. It is interesting to observe from Table 3 that there are seven −ve paths and three +ve paths in this case example. The sole +ve driver criterion is ‘Awareness’ which gives a positive influence on the outcome criteria via cost, time frame, and acceptability variables. The +ve driver needs to be strengthened and −ve drivers need to be

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Fig. 7. Total Interpretive Structural Model with Polarity (TISM-P) for Information Change Criteria Relationships.

Fig. 8. Classification of Information Change Criteria.

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Fig. 9. Total Interpretive Structural Model with Polarity for Information Change Criteria Relationships and Classification of Variables.

incorporate polarity of relationships from questionnaire design, data collection, transitivity checks, reachability matrix with polarity, digraph, interaction matrix and TISM with polarity. The second enhancement is to integrate additional steps on the classification of variables (based on MICMAC) with +ve/−ve orientation, superimposing the grouping of variables on TISM, and tracing paths from driver variables to dependent variables with their polarity. The enhanced and additional steps with the polarity of relationships provide more explanatory power to TISM-P in contrast to ISM and TISM. The additional insights by incorporating polarity of relationships (similar to SD methodology - Forrester, 1975; Sterman, 2000) would be helpful for enriched theory building as well as more effective practical applications of these methods to information and organization management. In the example of information and organizational change, there are more −ve drivers, i.e., difficulty and complexity of change, and associated cost and time. The sole +ve driver is awareness created about change. All the paths to outcomes from –ve drivers are –ve paths and the paths emanating from the +ve driver are +ve ones. The acceptability of change and preparedness for change serve as mediating variables between the drivers and outcomes. These relationships conceptualizing a theory of information and organizational change as per the TISM-P model can be validated in empirical settings.

Table 3 Nature of Paths. Driver Variables

Path through the Variables

Polarity of Path

Difficulty & Complexity

Cost Time Frame Acceptability Time Frame Acceptability Cost Acceptability Cost Time Frame Acceptability

−ve −ve −ve −ve −ve −ve −ve +ve +ve +ve

Cost Time Frame Awareness

5. Discussion The paper gives an enhanced version of TISM as TISM-P (applicable to ISM also as ISM-P) that incorporates polarity of relationships along with their explanatory interpretation. It may be noted from the proposed methodology (as exhibited in Fig. 1) that the identification of elements, contextual relationship and hierarchical partitioning of the reachability matrix are similar to the original ISM process as given by Warfield (1974, 1990). The interpretation of elements and retaining select transitive links (with interpretation) in digraph and TISM are similar to the TISM methodology as given by Sushil (2012). The simultaneous transitivity checks along with the direct pair-comparisons are similar to the modified ISM/TISM process as per Sushil (2017a). The difference is created in this enhanced version on two fronts. First, to

5.1. Implications for research The researchers will find this enhanced version (TISM-P) useful on multiple fronts such as:

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• Developing driver-dependence relationships in hypothesis formula-

• •

oriented variables and relationships which can be unearthed by this enhanced version (TISM-P) and will be valuable in practice in identifying positive and negative nerve centers for enhancing organizational performance. The proposed method needs to be examined in multiple case situations to be refined further for its effective use in practice.

tion with positive or negative influence as used in information and organization management research design. The TISM-P model will facilitate in hypothesis formulation such as variables of information technology acceptance related positively or negatively to individual performance (Igbaria & Tan, 1997). Classification of variables with a +ve/−ve orientation that further facilitate hypothesis building with mediation effects and its positive or negative influence on the outcome variable(s). For example, conceptualizing effect of +ve/−ve online customer reviews. Tracing +ve/−ve paths from the driver variables to the dependent variables to find out in how many ways driver variables may affect the dependent variable(s) positively or negatively to focus on crucial paths for effective information and organization research. The impact of paths can be traced by fuzzy simulation based on fuzzy rules for all the relationships (Pankaj et al., 1992).

6. Conclusion Both ISM and TISM are mixed-methods with a healthy mix of both qualitative and quantitative components. The comparison of variables in pairs is qualitative, whereas the transitivity check and hierarchical partitioning are quantitative. TISM takes the qualitative component to the next higher level by interpretations of links. The proposed enhanced method is this paper (TISM-P) is adding another dimension of the qualitative-quantitative mix by way of the polarity of relationships. The expert opinion on polarity is qualitative by examining the intrinsic nature of the relationship, whereas the polarity of transitive relationship is established by quantitative logic. Further, the +ve/−ve driving power or dependence and thereby classification of variables with +ve /−ve orientation is again quantitative. The proposed enhanced method would reflect the relationships and their theoretical and practical implications. However, the limitation of the method lies at two levels: one in data collection that requires greater involvement of experts to define the polarity between any pair and the other in the enhancement of computational complexity in deriving transitive relations with polarity. Another limitation is regarding the polarity assumption in case of ‘both ways’ relationship, according to which both way linkages are either +ve or −ve. This assumption needs to be examined in some case illustrations in information and organization management to assess the validity of this assumption. Though the paper provides a sound base for incorporating polarity in the ISM/TISM models, it needs to be further examined on multiple fronts. Some prominent future research areas to make the proposed method more effective are as follows:

However, for realizing the real benefits of TISM-P, a sizeable number of problems in information and organization management need to be solved and compared with the original models based on ISM/ TISM. 5.2. Implications for practice In practice, this enhanced version of interpretive modeling may be useful in many group-based strategic level problems such as clarifying strategic intent, policy structuring, strategic information system architecture, designing performance systems, etc. In many of these applications, the polarity of relationships plays an important role. For example, in clarifying strategic intent, it would be meaningful to not only know the driving objectives and the dependent ones but also their +ve/−ve orientation. Similarly, in policy structuring and strategic IS architecture +ve/−ve linkages will be of great value to assess the positive or negative impacts of the policy under consideration. In case of performance management, the leading and lagging variables are to be identified in the form of driver and dependent variables. But it gives more clarity about the +ve/−ve orientations of these variables as well. For example, cost and risk are variables with a negative orientation that need to be contained. The +ve and –ve paths from the driver variables to outcomes will give the insight to manage them effectively in practice. In the case example taken in the context of information and organizational change management, it is noticed that there are more −ve drivers and more −ve paths then the +ve driver and +ve paths. The practitioners have to enhance the positive drivers and contain the negative ones for the higher success of the information change programmes. Similarly, a large number of problem areas can be identified at the operational as well as strategic levels that may be better managed with a clear understanding of +ve/−ve polarity of relationships. For example critical success factors of information systems (some +ve, some −ve), information flexibility with +ve/−ve impacts, and so on. Almost in all problem areas, there are both positive and negative

i To validate the assumption of the same polarity on both links of a two-way relationship and the developing a process to incorporate different polarity (+ve in one direction and −ve on the other) in strongly connected elements. ii To develop algorithms to derive polarity of multi-order transitive relationships. iii To Interpret multiple paths with +ve/−ve polarity. iv To transform TISM-P into SD models as both consider polarity of relationships. v To make use of big-data in establishing polarity of relationships. vi To convert TISM-P model into hypotheses indicating positive and negative relationships for theory building in information and organization management. vii To empirically validate the hypotheses based on TISM-P model using structural equation modeling (SEM). viii To cross-validate the empirically tested theory in case situations in information and organization management.

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Appendix A Exhibit I.1: Filled-in Questionnaire with Transitivity Checks for the Illustrative Example

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Appendix B Table B1 Hierarchical Partitioning of Reachability Matrix for the Illustrative Example. Elements (Change Criteria)

Reachability set

Antecedent set

Intersection set

Level

Iteration-1 1

1, 2

I

1, 2

1, 2

I

3 4 5 6 7 8

1, 1, 1, 1, 1, 1,

2, 2, 2, 2, 2, 2,

2, 3, 4, 5, 6, 8 2, 3, 4, 5, 6, 8 4, 6, 8

1, 2

2

1, 7, 1, 7, 3, 4 3, 6 3, 3,

Iteration-2 3 4 5 6 7 8

3, 3, 5, 5, 7 3,

5, 7, 8 4, 5, 7, 8 7 6, 7, 8

3, 4 3, 6 3, 3,

4, 6, 8

Iteration-3 3 4 5 6 8

3, 3, 5 5, 3,

5, 8 4, 5, 8

Iteration-4 3 4 6 8

3, 3, 6, 3,

Iteration-5 4 6

4 6

3, 3, 5, 3, 7 3,

5, 7, 8 4, 5, 7, 8 7 5, 6, 7, 8 5, 7, 8

5, 7, 8

4, 5, 6, 8 4, 5, 6, 7, 8 4, 6, 8

4, 5, 6, 8 4, 5, 6, 7, 8 4, 6, 8

3, 8 4 5 6 7 3, 8 3, 8 4 5 6 7 3, 8

II

6, 8 5, 8

3, 4, 6, 8 4 3, 4, 5, 6, 8 6 3, 4, 6, 8

3, 8 4 5 6 3, 8

8 4, 8 8 8

3, 4, 6, 8 4 6 3, 4, 6, 8

3, 8 4 6 3, 8

IV

4 6

4 6

V V

III

IV

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