Alignment Strategy Research Articles

Strategi Bisnis dan Efektivitas Sistem Informasi pada Operator Transportasi Barang Laut di Indonesia

Many studies have examined the value of strategic alignment on company’s performance in general, though the study of the information system value on other factors such as efficiency and productivity are still limited. The purpose of the study is to examine freight transportation related to its business strategy, information system strategy, Website information system effectiveness, organizational culture, and organizational effectiveness. The sample of the study was taken from 30 goods transportation companies. The data collection used 150 respondents consist of 50 academicians and 100 practitioners. The data analysis used hierarchical regression. The results show that the level of alignment of business strategy and information system affects the level of users’ satisfaction of the company’s website information system. The results also show that the level of users’ satisfaction of Web information system mediates the relationship between business strategy and information system of the company’s efficiency and productivity. The research findings do not support the role of organizational culture as a moderator of the relationship between Website information system and organizational effectiveness, although it shows the positive relationship between these variables.  

Using Soft System Methodology to Align Digitalization Strategies with Processes/Business Strategies: Towards A Qualitative Approach

This qualitative study investigated the alignment between digital strategy and business and process strategies, focusing on its impact on competitive advantage and operational effectiveness. Through interviews, case studies, focus group, and document analysis, the research highlighted the significance of strategic alignment in the digital era, identifying key themes such as leadership commitment, cross-functional collaboration, innovation culture, continuous learning, and data-driven decision-making. Key Findings: - Leadership Commitment: Effective digital strategy alignment is strongly influenced by the active support and vision of senior leadership; - Cross-Functional Collaboration. This study contributes valuable insights into the practices and challenges of strategic alignment, offering guidance for organizations navigating the complexities of digital transformation.

The effect of different alignment strategies on trochlear orientation after total knee arthroplasty.

When planning and delivering total knee arthroplasty (TKA), there are multiple coronal alignment strategies such as functional alignment (FA), kinematic alignment (KA), mechanical and adjusted mechanical alignment (MA, aMA). Recent three-dimensional and robotic-assisted surgery (RAS) studies have demonstrated that KA potentially better restores the trochlear anatomy than MA. The purpose of this study was to compare the restoration of the native trochlear orientation in patients undergoing RAS TKA using fourdifferent alignment strategies. It was hypothesised that FA would result in the lowest number of outliers. This is a prospective study of 200 patients undergoing RAS-TKA with a single implant. All patients were analysed for MA and KA prebalancing, and 157 patients received aMA and 43 patients FA with intraoperative balancing. Preoperative transverse computed tomography scans were used to determine the posterior condylar axis (PCA), lateral trochlear inclination (LTI) angle, sulcus angle (SA) and anterior trochlear line (ATL) angle. Implant measurements were obtained using a photographic analysis. Intraoperative software data combined with implant data and preoperative measurements were used to calculate the differences. Outliers were defined as ≥3° of alteration. Trochlea dysplasia was defined as LTI < 12°. Native transepicondylar PCA had a median of 2°, LTI 18°, SA 137°, ATL 4°. LTI outliers were observed in 47%-60% of cases, with KA < FA < aMA < MA. For ATL, the range of outliers was 40.5%-85%, KA < FA < aMA < MA. SA produced 81% of outliers. Of all median angle values, only LTI when using KA was not significantly altered compared to the native knee. There is a significant alteration of trochlear orientation after TKA, regardless of the alignment strategy used. KA produced the lowest, but a substantial, number of outliers. The uniform design of implants causes the surgeon to compromise on balance in flexion versus trochlear position. The clinical relevance of this compromise requires further clinical investigations. Level II, prospective cohort study.

Aligning cooperative supply chain relationships with firm strategy: a cross-disciplinary analysis

Purpose This study aims to investigate the conflicting views in supply chain and strategic management literature regarding cooperative supply chain relationships (CSCR) and firm performance. Supply chain literature suggests a universally positive impact of CSCR on performance, irrespective of a firm’s strategy. In contrast, strategic management literature contends that the effectiveness of CSCR depends on their alignment with the firm’s competitive strategy. The research aims to clarify this disparity, offering insights into the strategic use of CSCR for enhancing firm performance. Design/methodology/approach This paper theorizes the integration of perspectives for the impact of CSCR on firm performance by examining the relationships considering the alignment of cost leadership and product differentiation strategies with supplier and customer relationships. Plant-level survey data is analyzed using regression techniques to test four hypotheses. Findings All four main relationships (cost leadership, product differentiation, supplier relationship and customer relationship) on firm performance are statistically significant. However, cost leadership firms are better aligned to their chosen strategy when they have strong relationships with suppliers, whereas similar relationships with customers create misalignment, negatively influencing firm performance. In contrast, product differentiators benefit by investing in relationships with customers rather than with suppliers. Practical implications A firm’s performance does not solely depend on its CSCR efforts but on aligning them with the firm’s overall strategy. Therefore, managers need to be cognizant of the firm’s competitive strategy when investing in CSCR. Failing to do so could negatively impact firm performance and, eventually, its ability to compete in the marketplace. Originality/value Scholars have advocated for the importance of examining competing perspectives of phenomena, both within and across various bodies of literature, as cross-disciplinary analysis often brings enhanced focus and depth, leading to improved understanding. This research is one of the initial efforts to empirically analyze the varying perspectives on CSCR in supply chain and strategic management literature. This cross-disciplinary approach can yield a more integrated perspective.

Assessing Strategic Business and IT Alignment: Validation of a Novel Model across Moroccan Enterprises

Strategic alignment between Business and IT remains a critical factor for organizational effectiveness, but measuring it often presents a complex challenge. This paper evaluates the model we proposed in previous work to measure the degree of alignment between Business strategy and IT initiatives. We applied the model to a selection of Moroccan companies. We compared the alignment scores obtained with those from the established Strategic Alignment Maturity Model (SAMM) using Cohen’s Kappa and simple linear regression for comparative analysis. This study demonstrates our model’s effectiveness in evaluating continuous scores and provides substantial concordance in categorical maturity assessments. Our validation confirms that our model can apply to various business contexts, paving the way for further refinement to evaluate business-IT alignment strategies.

True Confessions of Neutral Mechanical Disciple-How I Learned to Love a Patient-Specific Target.

Classical neutral mechanical alignment in total knee arthroplasty (TKA) has been a standard paradigm, while more recently, other alignment schemas, such as kinematic, individualized, and functional, have been explored. This study aimed to investigate the effect of three-dimensional (3D) computed tomography (CT)-based surgical robotics inputs on a classically trained surgeon's TKA component positions and alignment targets over time. Data from 1,394 consecutive robotically-assisted TKAs by a single surgeon from 2016 to 2020 were analyzed. Metrics collected included pre-balance planned implant component positions, final planned implant component positions after soft tissue balancing, and constitutional alignment from CT scans. Joint line obliquity was plotted against the arithmetic hip-knee angle (aHKA) using coronal plane alignment of the knee (CPAK). Three categories of alignment strategy were defined: true mechanical alignment (tMA), adjusted mechanical alignment (aMA), and no mechanical alignment (noMA). A shift to overall varus component positioning was observed over the years. Joint line obliquity according to CPAK showed a wider spread in later years, and the distribution of tibial and femoral coronal alignment angles expanded over time. The study revealed a change in alignment targets and final positioning of components away from neutral biomechanical axes in a large volume of TKAs by a single, classically trained surgeon over five years of using a robotic arm-assisted TKA system with CT-based planning. The most dominant factor for this change was the use of 3D CT planning, allowing the surgeon to assess patient-specific anatomy and plan accordingly. Outcome data is needed to determine if this change in behavior and surgical technique was beneficial. In summary, using a CT scan-based robotically assisted technique led to a gradual and complete shift from tMA to predominantly a non-mechanically aligned philosophy in TKA.

The AACN essentials: An intentional framework for successful implementation

Nursing education is shifting toward competency-based education (CBE) in line with the American Association of Colleges of Nursing's (AACN) 2021 Essentials. This pedagogical shift from knowledge-based leaner outcomes to competency-based learner and program outcomes affects how faculty teach, how students learn, and how programs allocate resources to support this change. The initial move toward CBE necessitates scrutiny of current curricula and alignment of curriculum, teaching strategies, and assessment tactics framed within the ten domains of the Essentials.Drawing on the Donabedian quality improvement framework, one school of nursing's curricular revisions project team discusses their strategies and challenges in implementing the AACN Essentials, illustrating the structural, procedural, and initial outcomes of adopting the Essentials across programs and specialties.Key to this approach is engaging all relevant stakeholders and mapping current curricula to the Essentials' many competencies and subcompetencies. This work informs curricular revisions and fosters faculty engagement and creativity. Lessons learned highlight a critical need for ongoing faculty development and use of learner-centric pedagogies to achieve students' competency development and practice readiness.This article offers insights and guidance for nursing programs embracing CBE and aligning with AACN Essentials.

How children generalize novel nouns: An eye-tracking analysis of their generalization strategies.

Recent research has shown that comparisons of multiple learning stimuli which are associated with the same novel noun favor taxonomic generalization of this noun. These findings contrast with single-stimulus learning in which children follow so-called lexical biases. However, little is known about the underlying search strategies. The present experiment provides an eye-tracking analysis of search strategies during novel word learning in a comparison design. We manipulated both the conceptual distance between the two learning items, i.e., children saw examples which were associated with a noun (e.g., the two learning items were either two bracelets in a "close" comparison condition or a bracelet and a watch in a "far" comparison condition), and the conceptual distance between the learning items and the taxonomically related items in the generalization options (e.g., the taxonomic generalization answer; a pendant, a near generalization item; versus a bow tie, a distant generalization item). We tested 5-, 6- and 8-year-old children's taxonomic (versus perceptual and thematic) generalization of novel names for objects. The search patterns showed that participants first focused on the learning items and then compared them with each of the possible choices. They also spent less time comparing the various options with one another; this search profile remained stable across age groups. Data also revealed that early comparisons, (i.e., reflecting alignment strategies) predicted generalization performance. We discuss four search strategies as well as the effect of age and conceptual distance on these strategies.

DDAGCN: an unsupervised cross-domain identification method for tie rod bolt loosening in a rod-fastening rotor system under different working conditions

The cross-domain diagnosis of tie rod bolt loosening is essential for guaranteeing the healthy operation of rod-fastening rotor (RFR) systems. The unsupervised domain adaptation (UDA) method effectively alleviates the impact of domain discrepancy and has been applied for cross-domain diagnosis. Traditional UDA methods mainly focus on the marginal and conditional distributions with fixed weights to adapt the domain distribution discrepancy. However, the fixed distribution combination cannot satisfy the requirement of feature domain alignment under different working conditions, and the relative importance of the two distributions cannot be evaluated quantitatively. This paper proposes an improved dynamic distribution adaptive graph convolutional network (DDAGCN) for the cross-domain diagnosis of tie rod bolt loosening under different working conditions. This method can quantitatively evaluate the relative significance of each distribution in representing the distribution discrepancy. First, it combines the convolutional neural network and the graph convolutional network to extract the features in the graph structure by using the connection relationship between nodes, and realizes the full extraction of neighbourhood information of nodes. Then, the dynamic distribution adaptive alignment strategy is introduced to construct the dynamic linear combination of marginal and conditional distributions, so as to measure the distribution discrepancy between domains. Meanwhile, the domain adversarial module is combined to further reduce the domain gap and finally realize feature alignment. The extracted domain invariant features can effectively enhance the generalization ability and fault identification ability of the model. The case of the public bearing dataset verifies that the effectiveness and generalization ability of the proposed method for cross-domain fault diagnosis under different working conditions is superior to other compared methods. In addition, the identification ability of the proposed method for the degree of tie rod bolt loosening is verified by the self-made bolt loosening dataset of the RFR system.

The Determinants of Success in the Implementation of Information Technology in Moroccan Companies: Exploratory Case Study

The study, which is being conducted in the context of the widespread use of information technology in Moroccan companies, focuses on examining the success factors of IT implementation. This is a crucial step in maximizing the value of information technology investments. The goal is to address concerns regarding the delay in digital transformation and the role of information technology in establishing a sustainable and irreplaceable competitive advantage. The research methodology utilizes a qualitative approach, specifically a single case study with semi-structured interviews for data collection. The theoretical framework incorporates the resource-based view (RBV) and contingency theories. The key findings of the study emphasize the challenges encountered during information technology implementation, especially concerning the complementarity of organizational and human resources with information technology resources. Based on these challenges, the factors contributing to successful information technology implementation are categorized into three groups: pre-implementation phase, alignment of information technology strategy, and collaboration and coordination among all stakeholders involved in the implementation process.

Green marketing strategy, technology implementation and corporate performance: the role of green creative behaviour and institutional isomorphism

PurposeThis paper aimed to test a proposed framework highlighting strategic green marketing initiatives and how they drive new technology implementation towards green corporate performance, underpinned by institutional isomorphism.Design/methodology/approachThe study used a quantitative method and convenience sampling approach in gathering data using adapted questionnaires to solicit first-hand information from 225 employees of small and medium-sized enterprises (SMEs) in the tourism and hospitality sector underpinned by the theory of institutional isomorphism.FindingsThe study shows that green communication and green strategy alignment have significant predictive effects on new technology implementation. Cultural isomorphism significantly moderated the effects of implementing new technology (i.e. green communication and strategy alignment). In addition, “new technology implementation had a significant predictive effect on green corporate performance”. Meanwhile, the moderation effect of “green creative behaviour on the new technology-green corporate performance dyad was positive but insignificant.”Originality/valueThe study’s novel framework confirms how green communication strategy and green strategy alignment complement cultural isomorphism to explain the impact of new technology implementation on green corporate performance, underpinned by institutional isomorphism.

The Influence of Entrepreneurial Orientation, Market Orientation, and Product Innovation on the Marketing Performance of the Batik Industry

This research is motivated by the need to understand the factors that influence the marketing performance of the batik industry in the city of Surakarta, which is one of the leading batik production centers in Indonesia. Batik not only has high cultural value but is also an important industry for the local economy, especially through Micro, Small and Medium Enterprises (MSMEs). This research aims to analyze the influence of entrepreneurial orientation, market orientation, and product innovation on the marketing performance of the batik industry in the city of Surakarta. Quantitative methods were used with the population of batik MSME actors in the city, and a sample of 100 actors was selected through purposive sampling, with the criteria of active operation for a minimum of 3 years according to Sugiyono (2015). Data was collected through a questionnaire whose validity and reliability have been tested. The results of the analysis show that entrepreneurial orientation, market orientation and product innovation play an important role in improving the marketing performance of batik MSMEs in Surakarta City. Increasing and implementing entrepreneurial and market orientation contributes significantly to marketing growth and performance. Intensive product innovation also has a positive impact on marketing performance. Therefore, a strategy that focuses on developing these three aspects is considered key for batik MSME entrepreneurs to achieve sustainable growth and improve their marketing performance. In addition, companies need to strengthen internal factors such as product innovation, service quality and operational excellence to increase efficiency, effectiveness and customer satisfaction. Alignment of marketing strategies with in-depth market analysis and superior customer service is also necessary to strengthen the position in the market.

Open-Set Graph Domain Adaptation via Separate Domain Alignment

Domain adaptation has become an attractive learning paradigm, as it can leverage source domains with rich labels to deal with classification tasks in an unlabeled target domain. A few recent studies develop domain adaptation approaches for graph-structured data. In the case of node classification task, current domain adaptation methods only focus on the closed-set setting, where source and target domains share the same label space. A more practical assumption is that the target domain may contain new classes that are not included in the source domain. Therefore, in this paper, we introduce a novel and challenging problem for graphs, i.e., open-set domain adaptive node classification, and propose a new approach to solve it. Specifically, we develop an algorithm for efficient knowledge transfer from a labeled source graph to an unlabeled target graph under a separate domain alignment (SDA) strategy, in order to learn discriminative feature representations for the target graph. Our goal is to not only correctly classify target nodes into the known classes, but also classify unseen types of nodes into an unknown class. Experimental results on real-world datasets show that our method outperforms existing methods on graph domain adaptation.

QAGait: Revisit Gait Recognition from a Quality Perspective

Gait recognition is a promising biometric method that aims to identify pedestrians from their unique walking patterns. Silhouette modality, renowned for its easy acquisition, simple structure, sparse representation, and convenient modeling, has been widely employed in controlled in-the-lab research. However, as gait recognition rapidly advances from in-the-lab to in-the-wild scenarios, various conditions raise significant challenges for silhouette modality, including 1) unidentifiable low-quality silhouettes (abnormal segmentation, severe occlusion, or even non-human shape), and 2) identifiable but challenging silhouettes (background noise, non-standard posture, slight occlusion). To address these challenges, we revisit gait recognition pipeline and approach gait recognition from a quality perspective, namely QAGait. Specifically, we propose a series of cost-effective quality assessment strategies, including Maxmial Connect Area and Template Match to eliminate background noises and unidentifiable silhouettes, Alignment strategy to handle non-standard postures. We also propose two quality-aware loss functions to integrate silhouette quality into optimization within the embedding space. Extensive experiments demonstrate our QAGait can guarantee both gait reliability and performance enhancement. Furthermore, our quality assessment strategies can seamlessly integrate with existing gait datasets, showcasing our superiority. Code is available at https://github.com/wzb-bupt/QAGait.

Inverse Weight-Balancing for Deep Long-Tailed Learning

The performance of deep learning models often degrades rapidly when faced with imbalanced data characterized by a long-tailed distribution. Researchers have found that the fully connected layer trained by cross-entropy loss has large weight-norms for classes with many samples, but not for classes with few samples. How to address the data imbalance problem with both the encoder and the classifier seems an under-researched problem. In this paper, we propose an inverse weight-balancing (IWB) approach to guide model training and alleviate the data imbalance problem in two stages. In the first stage, an encoder and classifier (the fully connected layer) are trained using conventional cross-entropy loss. In the second stage, with a fixed encoder, the classifier is finetuned through an adaptive distribution for IWB in the decision space. Unlike existing inverse image frequency that implements a multiplicative margin adjustment transformation in the classification layer, our approach can be interpreted as an adaptive distribution alignment strategy using not only the class-wise number distribution but also the sample-wise difficulty distribution in both encoder and classifier. Experiments show that our method can greatly improve performance on imbalanced datasets such as CIFAR100-LT with different imbalance factors, ImageNet-LT, and iNaturelists2018.

Dialogue for Prompting: A Policy-Gradient-Based Discrete Prompt Generation for Few-Shot Learning

Prompt-based pre-trained language models (PLMs) paradigm has succeeded substantially in few-shot natural language processing (NLP) tasks. However, prior discrete prompt optimization methods require expert knowledge to design the base prompt set and identify high-quality prompts, which is costly, inefficient, and subjective. Meanwhile, existing continuous prompt optimization methods improve the performance by learning the ideal prompts through the gradient information of PLMs, whose high computational cost, and low readability and generalizability are often concerning. To address the research gap, we propose a Dialogue-comprised Policy-gradient-based Discrete Prompt Optimization (DP_2O) method. We first design a multi-round dialogue alignment strategy for readability prompt set generation based on GPT-4. Furthermore, we propose an efficient prompt screening metric to identify high-quality prompts with linear complexity. Finally, we construct a reinforcement learning (RL) framework based on policy gradients to match the prompts to inputs optimally. By training a policy network with only 0.62M parameters on the tasks in the few-shot setting, DP_2O outperforms the state-of-the-art (SOTA) method by 1.52% in accuracy on average on four open-source datasets. Moreover, subsequent experiments also demonstrate that DP_2O has good universality, robustness and generalization ability.

Preference Aware Dual Contrastive Learning for Item Cold-Start Recommendation

Existing cold-start recommendation methods often adopt item-level alignment strategies to align the content feature and the collaborative feature of warm items for model training, however, cold items in the test stage have no historical interactions with users to obtain the collaborative feature. These existing models ignore the aforementioned condition of cold items in the training stage, resulting in the performance limitation. In this paper, we propose a preference aware dual contrastive learning based recommendation model (PAD-CLRec), where the user preference is explored to take into account the condition of cold items for feature alignment. Here, the user preference is obtained by aggregating a group of collaborative feature of the warm items in the user's purchase records. Then, a group-level alignment between the user preference and the item's content feature can be realized via a proposed preference aware contrastive function for enhancing cold-item recommendation. In addition, a joint objective function is introduced to achieve a better trade-off between the recommendation performance of warm items and cold items from both item-level and group-level perspectives, yielding better overall recommendation performance. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method, and the results show the superiority of our method, as compared with the state-of-the-arts.

XKD: Cross-Modal Knowledge Distillation with Domain Alignment for Video Representation Learning

We present XKD, a novel self-supervised framework to learn meaningful representations from unlabelled videos. XKD is trained with two pseudo objectives. First, masked data reconstruction is performed to learn modality-specific representations from audio and visual streams. Next, self-supervised cross-modal knowledge distillation is performed between the two modalities through a teacher-student setup to learn complementary information. We introduce a novel domain alignment strategy to tackle domain discrepancy between audio and visual modalities enabling effective cross-modal knowledge distillation. Additionally, to develop a general-purpose network capable of handling both audio and visual streams, modality-agnostic variants of XKD are introduced, which use the same pretrained backbone for different audio and visual tasks. Our proposed cross-modal knowledge distillation improves video action classification by 8% to 14% on UCF101, HMDB51, and Kinetics400. Additionally, XKD improves multimodal action classification by 5.5% on Kinetics-Sound. XKD shows state-of-the-art performance in sound classification on ESC50, achieving top-1 accuracy of 96.5%.

Multimodal Event Causality Reasoning with Scene Graph Enhanced Interaction Network

Multimodal event causality reasoning aims to recognize the causal relations based on the given events and accompanying image pairs, requiring the model to have a comprehensive grasp of visual and textual information. However, existing studies fail to effectively model the relations of the objects within the image and capture the object interactions across the image pair, resulting in an insufficient understanding of visual information by the model. To address these issues, we propose a Scene Graph Enhanced Interaction Network (SEIN) in this paper, which can leverage the interactions of the generated scene graph for multimodal event causality reasoning. Specifically, the proposed method adopts a graph convolutional network to model the objects and their relations derived from the scene graph structure, empowering the model to exploit the rich structural and semantic information in the image adequately. To capture the object interactions between the two images, we design an optimal transport-based alignment strategy to match the objects across the images, which could help the model recognize changes in visual information and facilitate causality reasoning. In addition, we introduce a cross-modal fusion module to combine textual and visual features for causality prediction. Experimental results indicate that the proposed SEIN outperforms state-of-the-art methods on the Vis-Causal dataset.

SDGAN: Disentangling Semantic Manipulation for Facial Attribute Editing

Facial attribute editing has garnered significant attention, yet prevailing methods struggle with achieving precise attribute manipulation while preserving irrelevant details and controlling attribute styles. This challenge primarily arises from the strong correlations between different attributes and the interplay between attributes and identity. In this paper, we propose Semantic Disentangled GAN (SDGAN), a novel method addressing this challenge. SDGAN introduces two key concepts: a semantic disentanglement generator that assigns facial representations to distinct attribute-specific editing modules, enabling the decoupling of the facial attribute editing process, and a semantic mask alignment strategy that confines attribute editing to appropriate regions, thereby avoiding undesired modifications. Leveraging these concepts, SDGAN demonstrates accurate attribute editing and achieves high-quality attribute style manipulation through both latent-guided and reference-guided manners. We extensively evaluate our method on the CelebA-HQ database, providing both qualitative and quantitative analyses. Our results establish that SDGAN significantly outperforms state-of-the-art techniques, showcasing the effectiveness of our approach. To foster reproducibility and further research, we will provide the code for our method.